Reflections: Hit Refresh (Part 1)

Introduction

I had been meaning to read Satya Nadella's Hit Refresh for some time before joining Microsoft myself as an FTE in April 2018. One of the welcoming gifts to new hires was this book, and I decided to read it promptly. I remember being impressed by the thoughtfulness of the author and the attitude that the book advocated, and it made me all the more eager to dive into my new role.

While I had intended to write a reflection on the book closer to the time of actually reading it, life happens, and here I am over a year later. However, I have recently looked back over the book, scanning the underlined portions and notes taken in the margin and reflecting on the ideas, thoughts, and vision expressed in its pages.

The book can be divided into two parts. The first recounts and reflects on Satya's professional journey and how Microsoft has and is changing under his leadership. The second focuses on Satya's thoughts on the future of Microsoft and technology. I will take up the first part in this post, and consider the second part in a follow up post.

Note: all personal opinions expressed below are my own and do not represent any official opinions or positions taken by my employer.

Part 1: Chapters 1 - 5

Mission

Since becoming CEO, Satya has made it his job to make the company culture about people. One of the first things he did with the senior leadership team was to have them share with each other about themselves personally. For many, it was the first time they had talked about themselves, "not exclusively about business matters" (6). Work needed to become a personal and meaningful place to be, for "we spend far too much time at work for it not to have deep meaning" (6).

How could this meaning be given to work? How could this personal touch be brought to the workplace? Satya believed it could happen by connecting what people were passionate about with what Microsoft was working on, and by making people the focus of what Microsoft was doing. In short, to "employ Microsoft in pursuit of [employee's] personal passions to empower others" (11). Said in a different way, "[o]ur culture needed to be about realizing our personal passions and using Microsoft as a platform to pursue that passion" (94).

I believe Satya would say that this was what Microsoft's mission had been all along, but that mission had been obscured and perhaps forgotten at times. Since Microsoft's "soul is about empowering people" (68) it is important to make that explicit, hence, Microsoft's mission statement was more explicitly stated and promoted: to "empower every person and every organization on the planet to do more and achieve more" (79). Such a clear mission statement unambiguously gave purpose and focus to what the company was doing, and with such a noble goal as empowering others, this mission statement also gave meaning to the work being done.

Leadership: Clarity and Energy

By unambiguously stating Microsoft's mission, Satya demonstrated a key leadership principle: to
"bring clarity to those you work with" (119). Without a clear direction or goal, the company could not leverage its resources in a coherent or efficient manner. It would be reactionary, flailing about in response to markets and other companies instead of steadily and diligently driving towards fulfilling its purpose through its products and solutions.

Satya also demonstrated another leadership principle, that of generating energy (119). By defining a noble purpose for the company, Satya gave employees energy, or to use his words, passion, again. If Microsoft's mission was about empowering others, and pursuing one's passions at work was to be the means to fulfilling this mission, how could one not be excited and energized about coming to work?

Culture

While empowering others (i.e., customers) was the mission, Satya needed to create a culture at Microsoft that would support and sustain that vision and actually carry it out. A mission statement that is not lived is just mere verbiage. Any energy generated by that mission statement would fizzle out if not actually carried out. The culture needed to change in order to implement the mission.

Satya defines culture as "the values, customs, beliefs, and symbolic practices that men and women live and breathe each day. Culture is made up of acts that become habitual and accrue to something coherent and meaningful" (91). Satya made "renewing our company's culture" his "highest priority" when he became CEO (2). Why? In short, because the company culture is the foundation for everything that the company does, everything that the people in the company do, everything that the people in the company experience. It supports the vision and purpose for the company. It sets the tone and feel of the workplace and the attitudes of those working there.

In a very real sense, culture is the company.

My impression is that Microsoft had been more of a top-down decision, fear-based, and internally-competitive culture prior to Satya's new role as CEO. The company was fragmented along organizational lines, moving in different and often conflicting directions without a clear sense of coherent purpose or mission. It was all about business and performance. It was reactionary.

In the early 2000s, Microsoft was largely on the defensive against the likes of Google, Amazon, and Apple, who were disrupting the technology world and innovating with speed and passion. Microsoft had appeared to stall out, living off past successes and choosing to mimic the products and services of these other companies. There was no sense of vigorous competition. Instead, a spirit of defeat and settling had prevailed. Passions were squashed or left unconnected to one's day to day work experience. Jill Tracie Nichols, Satya's chief of staff, told him days before he became CEO that "[employees] are actually hungry to do more, but things keep getting in their way" (67). Satya needed to allow employees and Microsoft to once again become a place of passion and innovation, a place where Microsoft would not settle for business as usual and would go on the offensive against competing companies. A place with a "sense of purpose and pride in what we do" (71).   A place where Microsoft would once again "compete vigorously and with passion in the face of uncertainty and intimidation" (38).

How could this change take place? Here are some key principles that Satya focused on for bringing about the culture change.


Individual and Internal Empowerment

Although Microsoft as a company now had a clear vision for empowering others outside the company, it needed to adopt a culture of empowering employees internally. "The key to the culture change was individual empowerment" (109).

For Satya, leadership is about "bringing out the best in everyone" (40). A leader needs to "bolster the confidence of the people [he or she is] leading" (40). A leader's job is to empower those who follow the leader, but not so much with skills or learning. Instead, the leader provides the inspiring vision and encourages the formation of proper attitudes. But the vision and attitudes must be made one's own if they are to be effective. Quoting Ray Ozzie, Satya agrees that "[t]he one irrefutable truth is that in any large organization, any transformation that is to 'stick' must come from within" (53). Similarly, any transformation of the culture at Microsoft would have to "come from within, from the core. It's the only way to make change sustainable" (57).

We know that this is true for individuals too. External pressures to do things never really work until they are internalized. The motivation has to come from within. The ownership and responsibility has to be with the individual. Otherwise a fear based, micro-managerial, and bureaucratic culture will ultimately prevail. Microsoft's previous culture of top-down and fear based decision making had to be replaced with bottom-up empowerment and freedom.

Servant Leadership and Collaboration

Another major part of the culture change required the use of another leadership principle: "the importance of putting your team first, ahead of your personal statistics and recognition" (39). A company at odds internally with itself, where employees were rewarded based on outcompeting coworkers, could not effectively compete with outside companies. A house divided cannot stand. The success of the company depended on its many parts working together, which required changing the culture to one where employees, teams, and organizations were required to build on the successes of others and to help others succeed. Collaboration towards shared success would be rewarded over "winning" at the expense of others (and ultimately the company).

In fact, Microsoft would make collaboration an intentional business strategy. By "actively seek[ing] diversity and inclusion... our ideas will be better, our products will be better, and our customers will be better served" (101-102). The inclusion of diverse perspectives and the promotion of collaboration would drive better product development as well as better employee work experiences. In short, Satya believed that "we must learn to build on the ideas of others and collaborate across boundaries to bring the best of Microsoft to our customer as one - one Microsoft" (102).

Growth Mindset

A final major attitudinal shift was the promotion of the "growth mindset". Using the principles drawn from Carol Dweck's research in her book Mindset, Satya encouraged a "learn-it-all" culture over a "know-it-all" culture. The fixed mindset assumes that one cannot grow, learn, or change to any significant degree. Decisions coming from this mindset "are ones that reinforce the tendency to continue doing what we've always done" (105). This is often done out of a fear of failure for trying something new or different. Instead, one sticks with what is known to sort-of work.

On the contrary, a growth mindset assumes that one can always learn, grow, change. As Satya applied it to Microsoft, "[w]e need to be willing to lean into uncertainty, to take risks, and to move quickly when we make mistakes, recognizing failure happens along the way to mastery" (111). For the individual, "I must internalize for myself the belief that the sky is the limit. I must work hard - not to climb the ladder, but to do important work" (116).

This mindset can only be adopted in a supportive work environment that embraces failure as a necessary byproduct of experimentation in seeking new and better ways of doing work and in creating new products. With the explicit adoption and promotion of this attitude across the company, leaders and employees could "find a way to deliver success, to make things happen," by trying new things without fear of repercussions when failure would sometimes occur (120). Microsoft would experiment and learn from its mistakes instead of trying to be perfect from the outset (and potentially punishing those that initially failed).

Conclusion

This in summary is how Satya changed Microsoft from a languishing and reactive old-school tech company to a vibrant, innovative, leading, and now cutting edge tech company once again. In short:

• He made the company mission and purpose to be employee and customer centric.
• He stated and promoted this mission with clarity and gave employees a purpose that they could be excited about.
• He encouraged, promoted, and rewarded a company culture that would center around individual employee passion and empowerment, servant leadership and collaboration at all levels, and a growth mindset to encourage innovation and experimentation.

In all of this I believe Satya's approach has been correct. I have witnessed first hand the changes at Microsoft since beginning my career here as a contractor in 2012. Where this vision and culture refresh is embraced, employees seem to be happier and are thriving professionally. I have also experienced the culture at other companies, and even pockets of Microsoft where this vision has not fully permeated. In those places, cynicism, attitudes of futility and negativity, unhealthy competition, and political infighting often abound to the detriment of all involved. However, Microsoft as a whole is doing much better than it has in recent previous years in financial terms and market share. Thus, it seems that this approach has proven to be effective.

Consequently, Satya and his fellow leadership members have provided a blueprint for anyone who wants to change the culture of his or her organization or business, and it appears to be rather straightforward: focus on and serve people (employees and customers). How this is done specifically will differ from organization to organization, but if this is truly the goal that is sought after, I think any organization will make great strides in refreshing itself.

So concludes my thoughts on the first part of this book. Look for a follow up post covering the second half of this book as Satya focuses his attention on the future of technology and its impact on society.

Reference

NADELLA, SATYA. HIT REFRESH: the Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone. HARPERBUSINESS, 2017.

Hope When Death Surrounds

I didn't grow up with death.

My dad's father passed away when I was relatively young, but I did not know him very well. Nobody else I was remotely close to died or even suffered from a major illness.  However, in the past two years, death has been a regularly repeated refrain.  My grandmother passed away late in 2017 after a stroke.  In the summer of 2018, a beloved uncle passed away suddenly, likely from a heart attack caused by a biologically pre-determined bad heart.  Earlier this year, a best friend of my wife lost her battle with cancer after a life of fighting with cystic fibrosis.  A month later, my mom's dad, a man who hiked mountains and scoured beaches well into his old age, passed away; he could hardly move in his final days.  We recently learned that the mom of another best friend of my wife, a woman we both know, was diagnosed with stage four cancer and given 3 months to live.

I meet with a group of men regularly throughout the year.  These men are mostly older and retired.  The concerns and struggles they share increasingly involve illness and death of loved ones.  A few I have known have also passed away.  I have reached the age when many of the people I know and love are increasingly facing illness and death.  The adults I have looked up to and continue to seek guidance from are closer to the end than the beginning, and it is starting to show.

For all the wonders that humankind has created, for all the achievements and improvements in quality and length of life we have achieved, we still have not conquered death and ultimate bodily degradation.  It is the irresolute fact that threatens to destroy and erase any meaning we find or have created here.  And if nothing lasts, if everything we have done in our lives will someday be forgotten, if everything the human race has accomplished will ultimately be wiped away through environmental destruction, the expansion and death of the sun, or the heat death of the universe, what was the point?

Some Philosophy

It seems that for something to have ultimate meaning, it has to last, be permanent, and endure. Forever.

If that is true, then we are faced with two possibilities.

• The first possibility: nothing ultimately has meaning or matters, because there is no life after death.  Nothing endures or is permanent.  What we do in this life will be forgotten or destroyed. Life has no ultimate meaning.  We have no ultimate meaning.  
• The second possibility: something ultimately has meaning.  Something will survive.  If that something includes us, then we have ultimate meaning too.

I would bet that most people feel that there is meaning and purpose to life in general and to their life specifically.  We may not know how to express it or account for it, but we know it and feel it.  We do ultimately matter.  Our lives ultimately matter.  What we do matters and is meaningful in some ultimate and enduring way.  If so, by a simple modus ponens, then we have to last, endure, be permanent.

Some Theology

Such an argument can be used to argue for an afterlife of some sort.  But of what sort?  Most people, if they believe in an afterlife, might imagine being up in "heaven" in a sort of disembodied immaterial state.  But if this is what actually happens in a permanent way, then notice what this implies about our bodies: if our bodies have ultimate meaning, then they will last; they do not last; therefore, they do not have ultimate meaning.  A similar argument could be made for all of material existence.

This sort of soul escapism has similarities to Gnosticism or Manichaeism, in which the material world and our bodies are something to escape.  And wouldn't it follow that, since we are escaping our bodies and this world, which will not last, we can use our bodies or this world in any manner we choose?  There might be some restrictions insofar as this impacts what will last (our souls), but any respect for bodies or the material world would derive from this relationship and not in virtue of what our bodies or this world are for their own sake, that is, in virtue of their own intrinsic value.  And wouldn't it follow that there is a lack of ultimate meaning for our bodies except in a derivative sense?  What then should we make of our bodily sufferings or the environmental destruction and material chaos that surrounds us?  Doesn't that matter, have importance, for its own sake?

On the contrary, our bodies do matter.  What happens to our bodies, our world, matters.  We don't just need salvation for our souls.  Our bodies, indeed, the whole world, needs and requires redemption and renewal.

This is what the Christian tradition advocates.  Not an escape from our bodies or material reality, but a redemption of them.  Not that the material reality will ultimately be destroyed, but that it will be renewed and endure.  The Apostle's Creed proclaims a belief in "the resurrection of the body" and the "life everlasting".  The Nicene Creed states that "we look forward to the resurrection of the dead and the life of the world to come."  The Christian belief professes that after death, souls will be reunited with healed, restored, and redeemed bodies (see here),  and the material world itself will be freed from corruption and renewed (see here).   Our bodies, and all of created reality, will endure forever, having been healed and restored from all material degradation and decay.

Death will not have the final word.

A Hope

What if spring never came?  What if after the flowers bloomed in summer and the trees dazzled us with their many colors in the fall, they would slowly die or be reduced to skeletons of bark and wood alone, never to return to fullness of life?  We might remember what once was, but what did it matter if that was the end?  However, we know that spring will come, and new life will shoot up from the ground in a burst of sights and smells.  The earth will be fresh, healthy, and alive once more.

The resurrection of the body is the hope for spring.  It is the hope that a winter of death is not the end, but a never ending spring of new and restored life will come.  It is the hope that my wife's friend will breathe fully and freely for the first time. It is the hope that my mom's dad  will have the youthful and outdoor vigor I remember him for.  It is the hope that I may carve another bowl from wood by my uncle's side.  It is the hope that all will ultimately follow in the footsteps of He who did in fact conquer death.

“Where, O death, is your victory?
Where, O death, is your sting?”

(1 Corinthians 15:55)

Popular Ontology: Self Classification

Ontology is the study of being, and includes a study of what exists and the various categories or classifications that existing things fall into at the most fundamental level.  While philosophy has traditionally been focused on properties, substances, possible worlds, and other rather abstract ontological entities for categorization, I have noticed that there is a lot of popular ontology these days. These popular ontologies carve up the human race into various kinds (usually four types of some kind), and one can usually find out what one is by taking some sort of online test.

Most tests apply to one's personality in some way, while others relate to one's physicality. Combined, one can gain at least an initial understanding of what sort of body/soul composite one is, and how one is different from the other body/soul composites in one's work, social, and family life.

As my wife and I frequently discuss various schemes of these categorizations and what we each are, I figured a running list might be useful to keep track of them all.  At least so I remember who and what I am...

Here is my popular ontology so far:

Personality

1. INTJ
• The Architect/Scientist
• Introverted, intuitive, Thinking, Judging
• Myers-Briggs
2. Upholder
• Four Tendencies
3. 4 (deep thinking, reflective), 3 (active, dynamic)
• Energy Type
4. Melancholic, Choleric
• Four Temperaments
5. Reformer (1), Investigator (5)
• Enneagram
6. Observer (blue, red/green, yellow)
• Discovery Insights

Physicality

1. Yang natural
• Image Archetype
2. Dark Winter
• Personal Color Analysis

What is your popular ontology?

Reflections on a Life Well-Lived: My Uncle Lamar

It hadn't rained since the middle of June in the Seattle area, with heat above 90 degrees for days on end.  It was unrelenting.  Yet on Thursday August 2, 2018, it rained.  My Uncle Lamar passed away that day.

When I first started writing this, the day after, I did not yet know the full details of what had happened.  Despite this, I felt compelled to write about him.  Writing seemed to me to be the best way to process the loss, to remember him and to share him with others, and to make sure that I did not forget what he had taught me and what he might teach to others still.

I now know that he was biking as he often did.  He was found still alive from what was probably a heart attack, but passed away either on the way to the hospital or soon after.  This was not the first heart attack.  He had had one several years earlier.  Fortunately, that had taken place at a rock climbing gym with a heart defibrillator nearby.  My uncle, despite being in incredibly good shape and active, was dealt a bad biological hand, or more specifically, a bad biological heart, and there was little he could do about it.  Still in his early sixties, he should have had many more years to live.

Biology aside, my Uncle Lamar had a wonderful heart. He was without a doubt one of the best men I have ever known.  I think anyone who knew him would agree.

My earliest memories of him are of our yearly Christmas trip to visit him, my aunt, and my cousins.  I looked forward to cheesy potatoes and French toast casserole made by my aunt, and I couldn't wait to get into my uncle's woodshop in the garage, where we would sometime during our visit turn a bowl on the lathe or work on some other wood project.  While I didn't really know him at the time, I always enjoyed his company.  He was always laughing and making jokes.  He didn't treat me like a child to be dismissed or tolerated.  Instead, he engaged with me and treated me as equally worthy of his time as any adult.  As I got older, I continued to make yearly trips to visit, sometimes with my parents and siblings, but most recently, with my wife and my children.  These trips as an adult were where I really got to know and to admire him, as family, as a man, and as a friend.

Who was my uncle?  Perhaps it is easiest to talk about him in terms of the roles he played and the virtues he displayed.

He was a husband.  One of the ways I admired him most was in how he shared life with my aunt.  I can't imagine them apart. He always looked out for her best interests.  I always noticed how he checked on her needs: did she need any help, did she need a drink, was there anything he could do for her?  He was always making sure she was taken care of.  And I knew that he loved her.  From the affectionate nicknames to the ways they touched and interacted, there was a warmness and "knowingness" between them.  They laughed and joked together.  They were best friends.

He was a father.  As a father myself, I now appreciate this aspect of my uncle even more.  He handled my cousins with love and support, but also firmness and discipline.  He encouraged them in their pursuits and challenged them to be excellent.  He expected their best but did his part to show them how to be their best.  He also took time with them.  In recent years he has gone on numerous adventures and trips with his kids, building treasured memories and experiences that will last a lifetime.  Later this month he was going to go rock climbing with his son and my brother at Devil's Tower.  Both of my cousins are successful, intelligent, ambitious, and mature adults, thanks largely to his firm but loving guidance.

He was a servant.  As he sought my aunt's best interest, he also sought mine and that of anyone who was around.  He noticed when we needed a drink refill.  He'd carry in the luggage.  He always cleaned up after meals.  He'd take a dirty diaper out of my hand and take it outside to the garbage.  No task was too small for him to not do.  He looked for ways to serve others, and upon seeing the opportunities, he did them.

He was generous, with his time and his resources.  He would give you the shirt off his back, which he actually did once.  He had a jacket that he was wearing that didn't quite fit right and thought would fit my younger brother better.  He took it off, had my brother try it on, and as it was a good fit, gave it to him on the spot.  Then considering that he hadn't given me anything, he grabbed a fleece jacket that either he didn't need (or that he knew he could replace) and gave it to me.  One of my favorite memories occurred at my cousin's high school graduation BBQ in their backyard.  My son, who adored Uncle Lamar and was always excited to see him, hadn't yet said hello.  My uncle, who was hosting, watching over a pig being roasted, and talking with the many adults surrounding him, squatted down to eye level to have a chat with my son when he came over, and proceeded to talk with him for several minutes.  This made my son's day.  I also recall all of the many free samples of Proctor and Gamble samples from his work that we would take home with us, as he had plenty to share.  Many of the tools I use in my own home projects were given to me by him.

He loved the outdoors and travel.  Upon "retiring", he got a part-time job at REI partly to get more outdoor equipment.  He volunteered on the Pacific Crest trail maintaining the trail by clearing brush and logs.  He was a regular biker and rock climber.  He went on canoeing trips with his son.  Whenever we would visit, he would always recommend a trail to take a walk or hike on.  His trips around the world always involved visits to mountains, scuba diving, or other outdoor exploration.  He thoroughly enjoyed his hobbies.

He was a worker.  As best as I can recall, he basically took care of himself from the age of 16 onwards.  He worked fulltime and put himself through college, taking classes at night.  Eventually he earned an MBA.  He worked a lot on the road when my cousins were little, but he eventually made his way to a top sales and marketing position at Proctor and Gamble that was more stable and allowed him to be home and to avoid travel.  I never heard him complain about working hard.  Life is hard and working is hard.  He accepted that and made the best of it.

But he also knew when to have fun too and the right priority of things.  He retired as soon as he could to spend more time with family and those he loved, more time traveling, and more time enjoying life.  Of course he could have continued on to greater things in a career, but this was of no importance to him.  He devoted the past several years to making memories, and in light of recent events, this was definitely the right choice.  His family will have more loving memories of him than most people will of parents and husbands that lived many more years than he did.

He was a man of laughter and humor.  No matter the conversation or topic, he would inject humor into it.  Life is full of ironies and comical situations, and my uncle also saw these and pointed them out.  One always enjoyed a conversation with him.  Laughter would follow every paragraph.  His odd phrases and words (e.g., "gigundi" = gigantic) always brought a smile.  His other quirks, like always wearing shorts (even in the middle of winter), make me laugh whenever I think about them.

I do not really know what he believed about life after death, God, and so forth.  I think some bad experiences with religion and "religious people"  may have turned him off to faith.  Regardless, he struck me as someone who "loved thy neighbor" in spite of this.  I am reminded of a story in Langdon Gilkey's Shantung Compound.  In this, Gilkey writes about his experiences in an American and British civilian internment camp during WWII located in China and controlled by the Japanese.  He recounts the story of a woman of loose moral character in the camp.  The good "religious" folk in the camp looked down on her for this moral failing.  However, she was the one who took food to the needy.  She was the one who sought out those who were lonely and in need of extra care and attention.  She was the one who worked hard in the kitchen to help prepare meals for the camp members.  Contrast this with the "religious" members who, like the Pharisees, were more interested in the show and pretenses of religion than in actually living out the gospel.  My uncle was like this woman.  He instinctively lived out the values taught and shared by most religions, despite the lack of formal religiosity, and perhaps better than many (or even most) people with formal religious affiliation.

He was a man of peace and joy.  My uncle was at peace with himself and the world.  Perhaps that is why he felt free to serve and love others and why he radiated joy to others.  He knew who he was and what he was about.  He knew what mattered and he focused his efforts on that: loving others and building memories with others.  We would often discuss politics or whatever else was "wrong with the world".  While we often disagreed, it was always a beneficial and mutually affirming discussion.  He sought common ground on every topic and looked to interpret what was said in the best possible light.  He had no need to prove me wrong or to win an argument, as that was not really what mattered.  Instead, what mattered was our relationship, and he used every opportunity to strengthen and deepen that.  This peace and joy made him attractive, and people couldn't help but be drawn to him.

So what can we learn from him and his life?  I suppose it is nothing new or that we have not heard before, but it certainly rings more true to me today than it has before.

• Love your family and your friends.  Today.  Concretely.  With words and actions.  Engage.
• Experience life.  Take trips, make memories.  Don't put it off.
• Work hard, but not too hard.
• Don't take yourself too seriously.  Laugh!
• Be generous.  Give.  You have enough to share.
• Be open to new and different ideas.  Be charitable in your conversations.
• Know yourself, know what matters, do it, and in that, be free.

I think my uncle would be ok with his passing.  Yes, of course he would have liked to have had 20 more years of life, as we would have liked to have had 20 more years with him.  But he was living every day to the fullest, and he had not put his life and his love on hold until a future unknown and unpromised time.  He was doing exactly what he should have been doing with his life, and consequently, could live and die without regret.

May that be true for all of us.

Rest in peace Uncle Lamar.

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“The longer I live, the more I realize the impact of attitude on life. Attitude, to me, is more important than facts. It is more important than the past, than education, than money, than circumstances, than failures, than successes, than what other people think or say or do. It is more important than appearance, giftedness or skill. It will make or break a company...a church....a home. The remarkable thing is we have a choice every day regarding the attitude we will embrace for that day. We cannot change our past...we cannot change the fact that people will act in a certain way. We cannot change the inevitable. The only thing we can do is play on the one string we have, and that is our attitude...I am convinced that life is 10% what happens to me and 90% how I react to it. And so it is with you...we are in charge of our attitudes.” ― Charles Swindoll (and Lamar's favorite quote)

Married to My Work

The phrase "married to my work" is supposed to carry a negative connotation.  It means something like: one's work comes first and foremost in priority, consumes most of one's time (even supposedly free time), that one can never get away from work, it distracts or takes away from family and friend time, and so forth.  The above would be more accurately described as being a "slave to my work," and is obviously not a healthy situation.  On the contrary, an appropriate level of being "married to my work" is a good thing.  But to understand this, we must first reflect on our understanding of marriage itself.

Dating vs. Marriage

What typically is the purpose of dating?  To paraphrase Tom Hanks in Sleepless in Seattle, it is to "try others on" to see how they fit with you.  Do you like the same things?  Do you enjoy talking and spending time together?  Do you have shared values and dreams?  Can you support each other?  It is very much self-focused: what can I or am I getting out of this?  This need not be selfish or self-centered, as it is important if one's ultimate aim is to find a partner for marriage that one finds a good match.  But there is a tendency to focus on having fun, and if things get rough, it might be easier to bail on the relationship instead using the opportunity to grow.

Marriage is different.  Read any marriage book or talk to any marriage counselor.  Once you say "I do", if your marriage is to be successful, you must now begin to focus on the other.  How can you support your spouse's dreams, goals, and pursuits, especially if they are different from your own? The idea behind marriage is that you achieve more and become more having committed to one person.  For better or worse, in sickness and in health, this person is supposed to help you fully realize your potential (and you are supposed to do the same).  You trade short-term, low-commitment advantages for a long-term investment that you can only get from sticking it out with one person for a lifetime.  You get to go deep with someone, knowing that they have committed to you and you have committed to them, and so you can be vulnerable and share yourself with them.

Traditionally, marriage has been about the mutual support of the spouses and the procreation of children.  It is best for you in the long term, and it is fruitful as children are brought into the world and reared.  It is supposed to provide a stable and loving environment for all members to live and grow in.  Yes, it is hard, yes, there are bad times, but in theory, when one looks back on one's life, one will be able to see that one has grown, matured, and gained far more than one has lost in being married as opposed to maintaining a single or frequent dating lifestyle.  The commitment is what allows one, indeed, forces one, to grow and be fruitful, as this is often born out of the challenges of marriage.

No, I am not saying that single people cannot grow and mature without being married or that they will have unfulfilling lives.  Any relationship that is a sustained commitment, an investment, through thick and thin, for the long haul, will encourage and require us to grow, but will also provide life-long enjoyment.  Think of your best friends, your siblings, your parents even.  These are our meaningful relationships.  These are the kinds of relationships that prove to be the most fulfilling.  These relationships are what we are made for.

Application to Work

With the above in mind, what is our approach to our work?  Now I am not advocating placing work above family or other legitimately higher priorities.  But what really is our attitude towards and commitment to work?  Are we dating our work, or married to it (in a healthy way)?  Work is important, and it can be very satisfying, dignifying, and developing in our personal growth and flourishing as a human being.

I have been consulting for the past six years.  The life of a consultant (in my experience, granted, your experience may be different), is that one goes from project to project, company to company, 3, 6, 12 months at a time.  You get in, get what you want out of it (or what your client wants out of it), and then you move on.  It is much like dating.  Either you break up with your client or your client breaks up with you after the work has been accomplished, the relationship has soured, or something else better comes along.  You always have one foot out the door.

Consequently, one's attitude towards work shifts to "what can I get out of this?" for every new project: will it advance my career, will I learn new skills, will I broaden my experience?  The client is asking the same thing: will this consultant advance my work, my project, and my career?  Now some of this is fine and good and healthy, as we do need to think about what is best for us (in a healthy way).  But it is interesting to notice how this can prioritize short term gain over the long term benefits of really committing to a role for the long haul, and encourages breadth of experience at the cost of depth of experience and knowledge.

I recently had the opportunity to go full time and leave consulting behind.  In thinking about whether to accept that opportunity, the analogy to dating and marriage came to mind.  Going full time is much more like marriage.  I would be trading a broad range of experiences for the depth of a single experience.  Instead of having relatively shallow knowledge of a wide variety subject areas, I would become an expert in a relatively narrow field.  Instead of hopping from project to project after my work has been completed, I would get to enjoy the "fruits of my labor" along with the challenges of building and maintaining a long term solution.  I would be committed, knowing that I cannot just walk away if the work gets challenging or coworkers get unpleasant.  I'd be in it, for better, and for worse.  And the role would definitely require more of me both in time and effort.  But hopefully, I'd get to reap the rewards of that long term commitment: greater satisfaction, deep knowledge, something I can point to as having accomplished, deeper friendships with coworkers, and personal and professional growth.

Conclusion

Yes, there is a time for dating your work.  And there are outside work commitments and other considerations that may make a lesser commitment to your work the right decision.  Yes, you should prioritize your family and perhaps other commitments over work. Yes, you shouldn't be overly committed to your work in an unhealthy environment, and you should maintain appropriate boundaries.  Yes, yes, yes, all of that needs to be considered. 

But after considering all of the caveats and addendums and factors in your life, honestly reflect: are you dating your work?  Why?  Is there a good reason?  Are you satisfied with your work?  If not, maybe its because you have never really committed and invested in your work in the ways that are necessary for satisfaction, fruitfulness, growth, and fulfillment in the long term.

If you want to be happy in your work, maybe it's time to get married.

Data Science and Philosophy of Science: What Makes a Model Good?

Introduction

In a previous article, I discussed philosophical views on the nature of scientific theories, and applied these discussions to data science models.  I concluded that data science models, the terms they invoke and the relationships they postulate, ought to be considered to correspond to reality in some way.  That is, a model's terms do in fact represent something real in the world (although this may be an abbreviation, summary, or approximation of potentially many real entities).  Similarly, a model's prescribed relationship does represent something real in the world (e.g., a causal relationship amongst the terms in the model, or amongst hidden terms that make up the terms in the model, or....).  While such correspondence may only be approximate and fall far short of 100% perfection and predictive accuracy, nevertheless, it is not merely useful.  It does approximate the truth, or attach to reality, in some albeit imperfect way.

Whether or not you agree, let's move on to another question in the philosophy of science that does not necessarily depend on how you answered the realist/anti-realist debate: what makes a good scientific model?  How does this apply to data science models?  Let's explore some ideas and then summarize at the end.

The Problem of Induction

Induction is the formation of generalizations or laws on the basis of past experience.  We believe that future occurrences will behave like past occurrences, and so on the basis of past occurrences, we can predict future occurrences.  For example, based on past experiences, we believe that we know (and have mathematically formulated a law) such that when billiard ball A hits billiard ball B in a certain way with a certain force in conditions X, Y, Z, etc., then ball A will go in this direction at this speed and ball B will go in that direction at that speed.

However, we have no guarantee that the past will be like the future in most cases, as there are not typically necessary relationships between the objects we are interested in.  It is conceivable, because it is not a matter of logical necessity, that ball A will spontaneously combust, or turn into a carrot, when it hits ball B.  Such a thing has never occurred before, but that doesn't mean it cannot happen.  Such thoughts have caused some people (most famously David Hume) to be skeptical about our ability to acquire knowledge through induction.

And yet, this is precisely what we do in the sciences.  Even in the absence of logical necessity, we believe that we know what will happen to ball A and ball B in these circumstances, and we can reliably predict what does in fact happen with a very small margin of error.  We even go so far as to form a law, a matter of physical necessity, to explain this relationship.

But what do we do in the face of competing "laws" that both explain the data we have?  Which theory do we go with and use for future research and development of theory?  This is the problem of induction.  How can we justify inductive inferences?  That is, how can we make universal or natural law claims based on experience, when so many alternative claims could be postulated?

Falsifiable

Enter Karl Popper.  His goal is to answer the problem of induction and to distinguish true scientific theories from pseudo-scientific theories.  He observes that it is really easy to formulate a theory that explains the known data, since it is done so using that data (hindsight is 20-20).  While this theory may be correct, one can think of many alternative theories that also explain the data.  How can one tell which theory to accept?

Popper answers that each theory must make so called risky predictions, that is predictions which one should expect to be false unless the theory is right.  A theory that is not refutable is merely pseudo-scientific.  Once we have excluded the pseudo-scientific theories and we have competing scientific theories, we can test them on the basis of what each predicts, focusing in particular on where they would disagree in a prediction.  That is, each theory must propose hypotheses that are then empirically tested after the theory has been formulated.

Conclusions are deduced from the theory, and these are then compared against each other to make sure that the theory is internally consistent, externally consistent with other unfalsified theories, and that when it makes a prediction, that prediction is correct.  When a theory fails to predict accurately or is discovered to be inconsistent, it is falsified.  If it is not inconsistent and does accurately predict, it is acceptable for use (although it may be falsified in the future).  In this, Popper proposes a deductive style method of testing.  We deduce in a manner similar to this: if theory A is true, then X must occur.  X did not occur.  Therefore, A is falsified.

In short, "Science in his view is a deductive process in which scientists formulate hypotheses and theories that they test by deriving particular observable consequences. Theories are not confirmed or verified. They may be falsified and rejected or tentatively accepted if corroborated in the absence of falsification by the proper kinds of tests" (Stanford Encyclopedia).   Theories are true so far if they are successful in making predictions and surviving falsification. Theories are judged by the deductive consequences of the hypotheses they make.

So a virtue of a theory is its ability to be falsified.  Theories that make stronger claims are more falsifiable because the predictions they make are bolder, and typically, more informative.

While this is all well and good, we still have a problem: we can have two theories that are both unfalsified and that make different predictions.  Which should we use until those predictions can be tested?  To answer, let's look at some other virtues that make a model good.

 

Elegance and Parsimony

A theory that is more simple is to be preferred over a more complex theory, all else being equal.  Simplicity can refer to both syntactic simplicity (the number of complexity of hypotheses in a theory; it is elegant) and to ontological simplicity (the number and kinds of entities postulated by the theory; it is parsimonious) (Stanford Encyclopedia).  Most well known, Occam's razor asserts that “entities must not be multiplied beyond necessity."

So why should we prefer more elegant and parsimonious theories?  That is, when faced with a choice between two theories that both explain the data equally well, why choose one over the other on the grounds that one is more simple?  To answer, let us consider the field of epistemology, that is, the study of knowledge.  Knowledge is said to consist in having a justified and true belief.  When faced with competing theories, we are asking ourselves which theory we ought to believe to be true, so our focus is on the justification for each theory.  Now we have already said that each theory is consistent with the data, so what other grounds do we have for believing one theory to more likely be true than another?  Which is more justified?

The simpler theory is more likely to be true because of probability.  Each entity in a theory has a probability of existing or having a certain relationship with the other entities.  So the more we multiply the entities and relationships, the more we multiply probabilities, which always being less than 1, lowers the overall probability.  For example, suppose you have a theory with 2 entities postulated versus 3 entities.  If each entity has a probability of existing/having a certain relationship of 0.75, then the former theory has a probability of  (0.75)^2 =  0.56 versus the latter theory of (0.75)^3 = 0.42 of being true.  Probabilistically speaking, you ought to prefer the former theory because it is more likely to be true, and since the theories are otherwise indistinguishable, you have no other reason to prefer the latter theory.

Or returning to epistemology and the notion of justification, you have no reason for choosing a more complex theory over a more simple theory when both are equally explanatory of the data.  Suppose for example that you return home and find that your house has been robbed.  What would you conclude?  You know that at least one person must have robbed your house.  But are you justified in believing that two people robbed your house?  What about an alien from outer space that came and robbed your house?  If you have no reason to believe that more than one person robbed your house (or that an alien robbed your house), then it seems you are not justified in believing so.  Instead, you must hold the theory that only a single robber broke into your house.  This in spite of the fact that two robbers did really break into your house (unknown to you).  That is, you must hold the most simple theory that explains the data to be true in order for that belief to be justified.

Granted, judgements about which theories are more simple, elegant, and parsimonious can be subjective to a degree.  We may have disagreements in certain cases.  However, we all intuitively have some understanding about what we are talking about and can agree on many cases that one theory is simpler than another.

Predictive and "Accurate"

These last three virtues are mentioned in the discussion on falsifiability, but deserve more attention in their own right.  The first is that a model must be predictive.  This is related to being falsifiable, in that a falsifiable theory makes predictions that can be proven to be false.   But we are interested in theories that not only make predictions, but that make accurate predictions.  In Popper's terms, we want theories that are strongly falsifiable and have failed to be falsified.  These are our best theories and we have made lots of relatively accurate predictions based on conclusions derived from their claims.   Consequently, they are extremely useful in advancing our understanding of the world and our interaction with it, according to our aims and purposes.

Coherence

A theory in order to not be falsified must be internally and externally consistent.  We can think about this in terms of coherence.  First, the theory must be internally coherent: any claim that the theory makes must not contradict any other claims by the theory.  Such contradictions can be logical, or less strongly, physical.  Even better is the case when the claims are supportive of each other (without being simply alternative ways of saying the same thing).  Second, the theory must be externally coherent: it must not contradict (unless it is challenging the existing paradigm) any of the best scientific theories.

Informative and Explanatory

While there are perhaps other virtues that could be considered, let us consider a final one here.  We do not want theories that are merely predictive and accurate.  We want to understand why.  Thus, we expect a good scientific theory to be informative, to explain why things are the way they are in the world.  It will postulate the causal mechanisms that explain why something happens the way the theory accurately predicts.  It will provide direction for new avenues of research in light of those causal explanations.  In short, we do NOT want a black box, no matter how accurate that black box may be.

Data Science and Model Virtues

So how can the above be applied to data science models?

Falsifiable

A data science model must be falsifiable.  It must make predictions (i.e., hypotheses) that are capable of being false, and are tested accordingly.  This is why separating one's data into a training set, test set, (and verification set) is so important: it keeps one's model falsifiable.  When one builds a model on all of the data, one can have an extremely accurate model when only looking at the data at hand.  However, one is in danger of overfitting the model.  One is modeling aberrations, errors, outliers, or biases in the sample data, and consequently, the model will not generalize to future data.  It has NOT captured the real relationships underlying the data.  Using a hold out test set can keep your model honest, and make sure that your model will generalize to data that it has not seen before.

Furthermore, doing so prevents you from refitting the model with each new addition of data.  If one were to receive data on a daily basis, and on that basis, retrained the model, and if that model significantly changed each day, how confident would you be that your model was going to predict well?  If it would predict something today and something different tomorrow, then your model is not stable and it is not going to make accurate predictions.  It is no longer useful.  It is as though your model is changing its mind every day, changing with the wind, and never subjected to critical scrutiny because it is always explaining the latest data without being held accountable for the inaccurate predictions it is making.  This would be a pseudo-scientific model.

 

Elegance and Parsimony

A data science model must be as simple as possible or as is necessary, according to one's purposes. Why?  Again, it is more likely to be "true" in the sense that one is more likely to have captured that actual relationships among the independent variables and their relationship to the dependent variable. But there is a challenge here, because more simple data science models tend to not be as accurate or predictive, and this can be due to excluding variables that are predictive, even to a small degree.  So we don't want a model to be too simple, and yet, we don't want it to be too complex either given overfitting.  We want to have a model that is as simple as possible without sacrificing accuracy and one that generalizes well when tested. 

 

Predictive and "Accurate"

A data science model must be predictive and accurate.  This is the whole point!  We want to accurately predict unknown values.  If a model doesn't do this, it doesn't matter if it is elegant or falsifiable.  It isn't true.  It does not accurately model reality.  Your model must generalize to new data.

Coherence

A data science model must be coherent.  I suppose one could have a model that contains a variable that is nearly the opposite of a different variable in the model, and the model could use them both.  While possible, I am not sure that both variables would survive even minimal feature selection.  Nevertheless, if your data science model is incoherent in some way, correct it, or look into why your model is paradoxical in this way.

 

Informative and Explanatory

Does you data science model explain, inform, or illuminate the relationships among the variables you are using to predict?  That is, when you look at the coefficients for your linear model, or the branches in your decision tree, do you understand or get an "aha"?  A good model will help us understand what is really going on.  This is especially important when one wants to know what action to take.  Is it better to add square footage or add a new roof if one is trying to improve the resale value of a home?  A good model should be able to tell us and quantify an answer.  This is where avoiding overfitting is so important, because an overfitted model will not have stable or reliable relationships among its variables, and so these cannot be relied on for informing decisions.

There is a downside though, in that some types of modeling are extremely accurate (i.e., neural networks) but are very difficult to interpret.  This is not always a problem if one does not need to understand why the model predicted in the way it did.  If the outcome is all that matters, then interpretability is not as important. 

 

Conclusion

Unfortunately, there are no hard and fast rules here for guidance on how to create good models, which is why model development in data science can be likened to an art or skill.  But with practice, one can develop this skill.  Consider these high level principles when creating your model.  Reference them and work to make sure that either your models have these virtues or that you have really good reasons for lacking them.  If you do so, you will have a good model.

 

Data Science and Philosophy of Science: Theory and Reality

Introduction

A major branch of philosophy called metaphysics may be characterized as the study of "being" or the nature of reality.  A sub-branch of metaphysics called ontology focuses on what exists and categories of existence.  The nature of causation is also considered within the realm of metaphysics.  Related to (and perhaps a further sub-branch of) these areas of philosophy is the philosophy of science, which studies the disciplines of science to understand their processes, assumptions, and commitments from a philosophical standpoint.

How might metaphysics and its related sub-branches/topics apply to the field of data science?  While there are several possibilities, I will focus on a debate in philosophy of science regarding the ontological status of scientific theories and the objects that are part of these theories, before applying that debate to data science.

Why is Science Successful? Realism vs. Instrumentalism vs. Constructive Empiricism

Why is science successful?  What needs explaining is the relationship between our scientific theories and objective reality.  For example, when we say that the Earth revolves around the sun, what does that mean?  Does that mean that we are claiming that the sun, the Earth, and the various gravitational forces actually exist in a mind independent way?  Or do we just mean that these concepts or ways of thinking are useful, practical, and help us achieve the ends we have, but that we shouldn't place any weight on the existence of such objects? 

This is the debate between scientific realism and instrumentalism (and constructive empiricism).  While the answer seems easy when discussing objects that we can verify with our senses, the answer is much more difficult when it comes to so called unobservable entities.  Does dark matter or anti-matter exist?  How do we know?  What about the theory of quantum mechanics?  Is a true depiction of physical reality, or is it just a useful way of thinking that makes the math come out properly?

Let's explore what each view is and what arguments for and against it there are.  For you hard-core philosophy of scientists, please forgive me if I am not exactly correct in my discussion below :)

Realism

Scientific realism is the view that "science aims to give us, in its theories, a literally true story of what the world is like; and acceptance of a scientific theory involves the belief that it is true" (Bas van Fraassen in The Scientific Image (1980)).  The intuition behind scientific realism is that we have made significant progress using scientific theories, and as science progresses, the theories we have are more reliable and lead to more reliable predictions.  The best way to account for this is that our scientific theories are true.  Hence, the objects of our theories we ought to consider to be real. 

So even if we have to postulate unobservable entities, we should believe these to be real and existing, even if we cannot see them, because what we can see and observe implies their existence.  Hence, unobservable entities are "discovered" in a sense, and not simply created.  The unobservable entities are considered to be mind independent (and theory independent).  Granted, science is fallible and theories are approximate, but we should think about scientific theories as being approximately true.

Instrumentalism

But what about the objects of scientific theories that are no longer accepted?  What about ether or ectoplasm?  These are things that were postulated to scientifically explain phenomena, and they may have worked well, but they have been discovered to not exist, and more reliable theories with different postulated objects have replaced them.  How can we be certain that unobservable entities in our current scientific theories will not suffer the same fate?  What about various geocentric models for the solar system that, although considered incorrect now, were still highly accurate in predicting phenomena?  How can we be certain that our current theories will survive the test of time?

This is the intuition behind instrumentalism.  Scientific theories "are just conceptual tools for classifying, systematizing, and predicting observational statements" (Duhem in The Aim and Structure of Physical Theory (1954)). This is pragmatism as applied to scientific theory.  Our scientific theories help us be more reliably successful in prediction and empirical observation, and they certainly can explain things, but we shouldn't take the things they talk about to be real or true, and we can't apply these theories to new issues.  In fact, the scientific statements we make cannot be considered to be true or false: they are just useful, and some are more useful than others.

Constructive Empiricism

Constructive empiricism may be thought of as a middle ground between realism and instrumentalism.  The constructive empiricist says that "science aims to give us theories which are empirically adequate; and acceptance of a theory involves as belief only that it is empirically adequate"  (Bas van Fraassen in The Scientific Image (1980)).  What does this mean?  In short, in contrast to instrumentalism, we can say that scientific statements can be true or false.  However, this does not commit us to believing in the unobservable entities.  In fact, one's acceptance of a theory is more of a commitment to continue to do research within that framework and to use the framework for new issues.

Arguments For and Against

All three views agree that a good theory must be predictive, informative, simple, and explanatory to be good (I'll discuss why in a follow up post).  However, they disagree on what this implies ontologically speaking.  So why believe one view over another?

In support of realism, we might regard the success of science as being miraculous if the objects in our theories are not real, that is, there is no relationship to or correspondence with reality.  It seems that the terms in our theories must refer to something real, although perhaps our characterization of those things is only approximately correct.  Furthermore, what is observable vs. unobservable is always changing, and what was once unobservable has become observable (e.g., bacteria, protons).  So even if something is unobservable now, it doesn't mean that is in principle unobservable or will never become observable.

But the anti-realist will respond that two theories can be equivalent empirically and yet postulate different unobservables, so they can't both be correct.  Both are empirically adequate.  Perhaps we should just say that neither is "true" though both are useful.

While I obviously won't resolve this debate, here are some quick thoughts.  It seems we should take some unobservables to be real, because past unobservables have become observable or have been disproven to exist.  Second, it seems scientific theories must be capable of being true or false to avoid pseudoscientific claims that cannot be disproven (I'll table this for a follow up post), and that when we speak about scientific theories, we are making claims about how the world really is.  Third, there may be unobservable entities that nevertheless exist.  Just because we cannot detect something does not mean it does not exist (this is to fall back into logical positivism).  This may make a theory that contains these difficult or perhaps physically impossible to falsify, but so long as it could in principle be falsified, perhaps this is ok.  So call me a cautious realist.

So what does this have to do with data science?

Why is Data Science Successful?

"All models are wrong, but some are useful."  So says George Box, a famous statistician, and this is something always quoted in any introduction to data science.  Which view does this match up with?  This seems to be an anti-realist position.  Is he correct?

Let's compare scientific models to the typical data science model from the anti-realist perspective.  First, the objects typically in scientific models are physical things (objects, forces), whereas the variables in a typical data science model are mostly conceptual.  We try to predict things like prices or movie ratings using variables like age or genre.  These are not objects in the world.  Instead, they are usually ways of expressing a value that people have. 

Second, scientific models always assume a causal relationship of some kind, whereas typical data science models don't postulate any sort of causal relationship, only correlation (and we all know, correlation does not equal causation).  How would the age of a person cause a certain movie rating, anyway?

Third, the relationship between the objects described in a scientific model is supposed to be unchanging, a law, whereas the relationships in typical data science models always seems to be changing and also depends on which variables are being considered.  For example, the coefficient for a certain variable may completely change depending on which other variables are being included in a model.

I could go on, but how might the realist respond to these objections?  First, one might say that our variables are short hand or abbreviations for physical things.  For example, a movie rating could be a proxy for something physical, like the amount of dopamine triggered in a person's brain when viewing that movie, and the genre composed of the various kinds of images and sounds depicted in the movie.  These are all physical things, but it is more useful and practical to speak about them in higher-level summary non-physical terms. 

Second, leaving aside debates about what causation actually is, using the prior response, we could tell a causal story between these variables.  When we say that the "increased square footage of the house caused its price to go up", we are actually saying something like "the amount of dopamine triggered in the brain by the recognition of increased square footage of the house triggered another neuron to fire that led to the individual valuing the house more, and being willing to pay more money for it."  This isn't exactly perfect, but you get the point: we can translate the non-physical entities and forces into physical entities and forces that underlie, compose, and give rise to them. 

Third, because these abbreviations and summaries are imprecise and don't account for every physical and causal entity, it shouldn't surprise us that our data science models are not perfect, or that the introduction of new variables (i.e., physical entities and forces) should change our understanding of the relationships.  However, that does not imply that the relationships are not legitimate or approximately true.  In fact, we have this intuition that if we could account for every physical entity and force in our data science model, then it would in fact be 100% accurate.

Conclusion

So what do I think?  I tend to have realist tendencies, so I am inclined to favor a realist interpretation of data science models.  I believe that our models must be attaching on to something real if they are in fact being useful.  And our concepts do attach to real entities and forces in the world, although they are summaries of very complex relationships among things and perhaps are not real in themselves (that is another debate).  So I am not surprised that our models are only approximately true (i.e., close in predicting numeric values, more often correct than not in categorizing). 

And while two models may both be equal in empirical accuracy, experience suggests that either only a few common variables are doing the main predictive work (and the additional variables are mostly noise) or the differing variables are both correlated with a third variable that really explains the correlation.  With additional work and time, the two supposedly differing models should converge and begin to look more and more similar as predictive accuracy increases.  Again, this suggests that there is something real that the models are latching on to, although it may be disguised and hard to uncover in a precise manner.

So I would respond that, strictly speaking, Box is correct: if a model is either all true or all false, then all models are in fact false.  But if models can be approximately true, then some models are approximately truer than others, and in this sense, are modeling reality and the relationships among entities in a truer and more real (and perhaps causal) way.

So how can we make sure that the data science model we have is approximating reality?  We'll explore this is a follow up post.

Data Science and Ethics: Deontology vs. Consequentialism

Introduction

Consider the following real scenario:

• President Donald Trump has implemented a travel and refugee ban on anyone from one of several predominantly Muslim countries.  Suppose that, as a result, there are 0 terrorist incidents in the United States in the next four years, and assume that, were it not for this ban, there would have been several terrorist incidents.  
• Is the travel and refugee ban moral?
• Furthermore, suppose that the government has gathered various kinds of information about you based on your web surfing history, Facebook profile, shopping history, mobile device locations, etc. in order to fight terrorism. 
• Is the collection of such data immoral? 
• Furthermore, suppose that a machine learning model used to fight terrorism uses the religion (or race, gender, marital status, sexual orientation, etc.) of an individual to highly predict if a person will commit an act of terrorism.  Without this field, the model cannot accurately identify terrorists as well as it previously could.
• Is the use of this highly sensitive, personal, and potentially discriminatory field permissible?

Underlying this real scenario is a sort of  dilemma, pitting two desirables against each other.  On the one hand, we want good results in the aims we pursue, so the more effective a means is to achieving those results, the better.  In this case, we want protection from terrorism and safety within the United States, and so we would embrace actions that further these aims.  However, we also believe there are limits that no means can cross, that the "end does not justify the means" in every circumstance.  If you are like me, then you are probably at least a little uncomfortable with using country of origin or religion in profiling immigrants, even in the service of fighting terrorism.

This is an age old debate between the opposing normative ethical theories of consequentialism and deontology, but it is now being played out in the realm of data science. Below, we will explore the relationship between ethics and data science and discuss how ethical theory can help provide guidance about the above scenario and similar situations.

Philosophical Terms/Distinctions

Here are some key terms and distinctions to understand:

• Deontology
• The normative ethical view that an action's moral rightness or wrongness is not determined solely by the outcome of the action.  Other factors are important as well (e.g., intention of the agent, the nature of the act).
• Consequentialism
• The normative ethical view that an action's moral rightness or wrongness is determined solely by the outcome or consequences of the action.  Typically, an action is morally right if and only if it maximizes happiness, utility, or some other desired output.
• Morality vs. Legality
• Actions may be legal but immoral.  Similarly, actions may be illegal but moral.  Thus, legality is not the same as morality.
• For example, one might argue that the use of pornography is immoral, even though it is legal.  Similarly, in many countries, certain kinds of religious or political expression are illegal, but most of us wouldn't say that these kinds of expression are immoral.
• User/Individual interests vs. Company/Government/Society/Group Interests
• An individual/user's interests are distinct from the interests of a company, government, group, or even society.  While these interests might be aligned, they do not need to be aligned and may come into conflict.
• Hierarchy of rights: life, liberty, property
• There is hierarchy to rights.  In particular, the right to life is more important than a right to liberty, which itself is more important than a right to property.  Life is necessary for liberty (without life, you cannot have liberty), and liberty is necessary for property (without liberty, you cannot exercise your right to have or make decisions with respect to your property). 
• In case of direct conflict, the higher right must take precedence over the lower right.

 

Data Science Core Concept: Machine Learning

At the core of data science is the use of algorithms to predict and classify entities (e.g., people) into different categories.  This is machine learning.  Most often, the algorithm uses various fields (e.g., age) to predict an unknown but useful value (e.g., spending behavior).  The more informative the fields fed into the algorithm, the more accurate and useful the predictions will be.  Thus, it is very important to figure out which fields are the most useful in predicting or classifying, and which are not helpful.

In some kinds of "deep learning" associated with AI, the algorithms do not require the use of fields.  The algorithm constructs these on its own before making a prediction or classification.  While such algorithms often work very well, understanding why the model produced the results it did can often be nearly impossible to interpret.  That is, we don't know why the "black box" works, but it does.

Ethical Data Science: An Exploration 

So how can we apply the above philosophical terms and distinctions to the realm of data science/machine learning, and in particular, thoughtfully address the above scenario? Let's explore.

A Consequentialist Start

The reasoning behind the travel ban has the form of something like:

• If one is Muslim (or from a predominately Muslim country), then one is (or very likely to be) a terrorist.  

In the context of data science, the input field would be "Country of Origin" and/or "Religion", and the target field is "Terrorist/Not Terrorist".  Now suppose you have a sample data set that is representative of the world population with these fields, what would you expect to find?

If this is all that you have, not much.  We know that most people aren't terrorists.  Most Muslims aren't terrorists.  Most Muslims from predominately Muslim countries are not terrorists.  A model just based on these fields would probably just label everyone from these predominately Muslim countries as not being a terrorist to get a high accuracy, even though it would miss identifying any actual terrorists.  In terms of a confusion matrix, our initial model would have 0 false positives, 0 true positives, several false negatives (labeled "not a terrorist" but actually a terrorist), and many true negatives (labeled "not a terrorist" and in fact not a terrorist). 

In contrast, to avoid missing any actual terrorists, the model behind President Trump's travel ban goes to the opposite extreme and labels everyone from these predominately Muslim countries as a terrorist.  That is, the model he proposes would have many false positives (labeled "a terrorist" but not actually a terrorist), several true positives  (labeled "a terrorist" and actually a terrorist), 0 false negatives , and 0 true negatives.
 

So how would one improve this model?  With more data!  Religion and country of origin do not carry enough information on their own to provide a predictive model that correctly identifies terrorists as terrorists and correctly identifies non-terrorists as non-terrorists.  While I am not an expert at what can correctly identify a terrorist as such, I would think that, in addition to Religion and Country of Origin, fields like Gender, Race, Ethnicity, Age, Education, Economic status, Internet Activity (websites visited), Purchase History, and Travel History may be useful in more accurately identifying terrorists and non-terrorists.  Suppose that we get this information and now we have a very accurate model that correctly identifies both terrorists and non-terrorists.  Great!

In summary, in the initial set up, President Trump's model supposedly prevented all terrorist attacks.  In this it was extremely effective in the desired outcome: no terrorist attacks.  But it came at the cost of preventing many non-terrorists from immigrating to the US.  That is, it prioritized the right to life of innocent civilians over the right to liberty of prospective immigrants, assuming a rights conflict.  But is there such a conflict?  Our updated model suggests that, in theory, there isn't.  If one has enough information, one can protect the right to life of civilians AND the right to liberty of immigrants by correctly identifying both terrorists and non-terrorists. 

A Philosophical Reflection

But notice what we had to do.  We had to make use of lots of personal information collected by the government on individuals.  Is there a limit to the amount and kinds of information that the government or anyone else can collect?  Are there restrictions on how the collected information can be used?
 

I would bet that most people intuitively want to place limits on both the amount and kinds of information being collected on them, and the reasons that they would give for that intuition are often moral in nature.  For example, most people are fine with a certain amount of information being collected on them.  But as that information increases in size, they would become less and less comfortable with this.  They would likely express fears that the information will be used in inappropriate ways to target them, manipulate them, or otherwise influence them in ways that they would not want.  That is, the information could be used to harm them.

Not only is the amount of information important, the kind of information matters greatly.  Some kinds of information we aren't concerned about being publicly known (e.g., gender).  But there are other kinds of information that we would be hesitant to share (e.g., medical history, financial history) not merely because of how it could be used, but because of its personal and private nature.  We can certainly think of kinds of information that we wouldn't want anyone to know, especially private companies or governmental organizations (e.g., sexual history and preferences).  In short, the collection of some kinds of information about us would be a violation of our privacy, personal integrity, or human dignity and nature in the extreme.  These are moral concerns.

Furthermore, even if the amount and kind of data were permissible, the use of such data may not be.  In the model above, we had to make use of extremely sensitive information like race and religion to come up with this model.  While I may not object to my race and religion being known and used in some matters (e.g., demographic statistics), I would certainly have concerns with it being used in such a way as to harm myself and others, or deal with myself or others in ways I consider to be unjust.  Thus, there are morally proper uses of data.

Let's step back for a moment.  Why use data in the first place?  In short, to better achieve our aims and goals through an increase in knowledge on the subject at hand and thereby improve our decision making in achieving our goals.  On the governmental side, the use of data is intended to help in governing, whether it is to improve services, increase safety, or support other legitimate government interests.  For companies, data helps a company succeed in meeting competition, increasing profits, and other business aims. 

In contrast, an individual typically does not use data in this way but is the source of the data that governments and companies use.  An individual consents to disclosing such information in exchange for goods and services.  That is, there is a sort of marketplace transaction that occurs in which an individual "pays" for goods and services with his or her information.  Thus, we can think about that information as being part of an individual's personal property.

We do this in numerous ways every day, usually without thinking about it.  We sign up for Facebook, giving our names and other demographic information.  Through the use of its services, we also provide information on who our friends are, what we are interested in, where we live, and what we believe, knowing that online companies make money off of such information through the use of targeted advertising.  We submit to background checks to receive employment, to travel in the faster TSA-Pre lines, or obtain passports to travel abroad.  We sign up for customer discount cards at grocery stores to save money, while these stores use our purchase information to improve pricing, marketing, and ultimately, sales.  In short, there are legitimate and mutually beneficial arrangements that exchange data for goods and services.  Typically, as long as the source of data has consented to the exchange, then there isn't a moral issue.

 However, this is not always true. Even supposing we have consented to the collection and use of certain kinds of data, there still could be a moral objection.  John Stuart Mill famously argued that we do not have the liberty to sell ourselves into slavery, for we would be using our freedom to make ourselves un-free (a sort of self-contradiction).  Similarly, I would argue that there are likely certain amounts, kinds, and uses of information that, even though we were to consent to the collection and use of them, would still be immoral as they would violate our moral worth and dignity, and hence, even our consent could not morally justify the action.  This is in spite of any other benefits, goods, or services that would result for us (consider similar arguments against prostitution).

Thus, it seems that in order to avoid these moral pitfalls in collecting and using various amounts and kinds of data, one must (at least):

• limit the amount of data gathered to morally acceptable limits
• limit the kind of data gathered to morally acceptable kinds
• use the data for morally acceptable uses
• obtain consent from individuals about whom the data is gathered for the collection and use of such data
• provide just compensation for the data in the form of goods and services to the source of the data

What these general limits are I cannot say, nor is it my point here to do so.  My point here is merely to argue that it is reasonable to believe that limits do exist.  As to what those general limits are, that would require much greater analysis than I have time for here.

A Deontological Critique

But perhaps we can say something useful about the specific scenario outlined above.  Does the above example satisfy the above criteria?  Let's explore.

The amount of data does not seem to be the issue here.  For the model above to work, no great amount of data regarding any single individual is required, although a large quantity of data is required (i.e., a single record for each person in the representative sample).

The kind of data being collected is a bit more concerning.  While country of origin isn't all that personal, religion is.  In some countries, that information being made public can mean the difference between life and death, freedom or persecution.  While this information may not be as sensitive as other kinds of information, it still is extremely personal.

The use of the data seems to be the primary concern.  Religion (and Country of Origin as a sort of proxy for religion) is being used to discriminate amongst immigrants, and this is cause for great concern.  Freedom of religion is protected by the First Amendment of the Constitution as a legal right.  Now such a right might not legally apply to immigrants (as they are not citizens of the United States), but it may morally apply.  If we believe that, morally speaking, all human beings have some right to freedom of religion, belief, or conscience, and this moral right is being restricted in some way without a direct conflict with a higher right, then that is cause for concern.

In this case, the government is treating certain country of origin/religion combinations as nearly guilty, even in the absence of any actual terrorist or criminal activity.  This reminds me of the movie Minority Report, in which individuals are arrested before they commit any crime, based on the prediction/foresight that they would commit the crime if not prevented.  Even though the program drives crime down to virtually zero, we are left with the impression that such a situation is unjust, for each supposed criminal has not yet committed any crime to be punished for.  A similar example comes from just war theory as opposed to pre-emptive strikes.  In just war theory, it is presumed that a wrong has been committed that needs correcting, whereas in a pre-emptive strike, no wrong has yet occurred even though it is perceived as imminent.  However, as many people note, while under the guise of a defensive measure against a likely aggression, a pre-emptive strike actually flips the roles of aggressor and defender. 

In our scenario, because a person may be a terrorist, he or she is treated as such, even though no actual terrorism has been committed by that person.  Joined with the facts that the reason for believing that he or she is a terrorist seems to be exclusively that he or she is Muslim and/or from a predominately Muslim country, and a person's religion is extremely personal and whose freedom of religion is a moral right, this use of data clearly seems to be unjustified and immoral, even if it would bring about great good (i.e., the prevention of terrorist activity).

The only escape from this argument I can see is that one must argue that there is a direct rights conflict, and that the right to life of US citizens takes precedence over the moral right to liberty of immigrants in their immigration and religion.  Is there a direct rights conflict?  I don't think so.  One has to say that there is no effective way to prevent or fight terrorism other than to ban all immigrants from certain countries on that basis alone, which does not seem reasonable. 

What is more reasonable is that there is no effective way to fight terrorism without taking into consideration religion and country of origin as part of the data used to identify terrorists.  That seems more reasonable, but it would hardly justify banning everyone from a certain country of origin or from a certain religion, as again, most Muslims from these countries are not terrorists.  Still, it may justify using these data fields as part of the overall data used to identify terrorists.

However, as mentioned previously, other kinds of information are likely much more informative in predicting whether or not someone is a terrorist, and if this sort of information is not sensitive or extremely personal in nature, then it can and should be used instead of the more sensitive information.  Thus, it is reasonable to believe that there are effective ways of fighting terrorism that do not rely exclusively or perhaps at all on this kind of sensitive data, and if so, there is no direct rights conflict between the right to life and the right to liberty.  In short, it seems extremely unlikely that we are forced to choose between upholding the right to freedom of religion (i.e., liberty) and right to life in this matter.  Or in other words, a claim that a failure to deny the freedom of religion and immigration to immigrants leads directly to the denial of the right to life of US citizens seems extremely implausible.

Has consent been obtained from the immigrants regarding the collection and use of such data?  It is hard to say.  Some information like country of origin is known publicly and explicitly through the immigration process.  Other information is perhaps voluntarily revealed as part of the application for immigration.  Would religion be part of this revealed information?  Perhaps.  Is it appropriate to ask about this as a condition for immigration?  Maybe, but it at least appears to be somewhat suspect given its sensitive and personal nature. 

Consent has been given as part of determining one's eligibility to immigrate to the United States.  But does this also mean a consent to using the data to fight against terrorism?  This is doubtful, unless eligibility means among other things that one is not a terrorist, which is reasonable: terrorists are not eligible to immigrate to the United States.  But one could argue that this is an instance in which even  the collection and use of such information, even though consented to, is still immoral given the kind of information being used and its intended use.

Has just compensation for the data in the form of goods and services been provided?  I believe so, if the exchange is the ability to immigrate to the United States.  It seems that this can be a just and reasonable compensation for the exchange of data.

Conclusion

So what can we conclude, if anything?  Much of what I have said above is surely debatable by reasonable and well informed people.  However, I believe the deontological concerns raised above suggest that even if preventing immigration from the several predominately Muslim countries stopped all terrorist activity that would have occurred otherwise, the practice itself would still be immoral given the highly sensitive nature of the data, how it is to be used, and the likelihood of using other kinds of data that are not sensitive but are still effective in fighting terrorism.  The consequentialist good of preventing terrorist activity cannot justify the, deontologically speaking, immoral means of collecting and using sensitive data like religion in this way.  In the absence of a direct rights conflict with a right to life, other effective and morally permissible ways of fighting and preventing terrorism must be found that do not infringe on the moral rights of immigrants in their liberty to immigrate and in their freedom of religion.

While I have focused on a specific example in the above discussion, a general point can be made that ties back to data science. The above shows that there may be cases, perhaps often, in which a predictive model may have to rely on data that from a collection, kind, or use perspective, is immoral, in order to achieve a desired level of effectiveness (e.g., accuracy, lift, precision, or other measure).  That is, data scientists will on occasion have to choose between building consequentially effective models and deontologically moral, though perhaps not as effective, models.  While such a choice can perhaps be avoided when informative but not morally suspect amounts, kinds, and uses of data exist, it seems that this won't always be the case.

Thus, it is important for data scientists to think ethically when doing their work. A model's predictive capabilities cannot be the sole criteria for judging whether it can be used or implemented.  We must also reflect on the moral implications.  Is the data ethical in amount and kind? Is it being used ethically?  Have the sources of data consented to its collection and use in this way?  Have they been justly compensated?  If the answer is no to any of these, are there ways to correct these issues or to use other kinds of data to achieve the same end?  If not, we may be forced to choose between being ethical and being effective.