Machine Learning and the New Racism

Scary stuff, but something I think we’re already deeply mired in: Physiognomy’s New Clothes (the new racism, courtesy of machine learning).

Reminds me of the book Weapons of Math Destruction, which also highlighted many important points about the problems with “runaway” algorithms, which not only face the danger of falling into a closed feedback loop (and thus feeding their native biases), but also where the builders of the algorithm are no longer around to ensure the algorithm’s still behaving according to theory and can no longer validate its results qualitatively.

What is more, having worked on many data science projects, I know how easy it is to build models that can be tweaked to say anything I want by just tweaking a couple of parameters.

And let’s just say that models that don’t quite agree with management’s decree don’t always see the light of day.

(Link to article above “Physiognomy’s New Clothes” via the wonderful Marginal Revolution blog, which also highlighted the fact that this was “neglected” — I personally am finding myself increasingly leaning toward the AI doomsayers. The more I know, the more I worry.)

Thinking About Life

Maybe it’s do with the weather of late – cool, dreary, wet; or maybe it’s to do  the long runs I’ve been doing – lonely, peaceful, contemplative.

Whatever it was, I’ve been thinking about life – about how it has been; about how it is now; and about how it is going to be.


I first came across this beautiful poem called Ithaca by C.P. Cavafy more than ten years ago. I was about 17 or 18 then, and I must admit that I didn’t fully appreciate it. I had, in fact, actually thought that it had to be mistaken: what is life but the destination?

Now I’m almost twice as old, and its reading has a profound new meaning to me, and reading it always calms my nerves when I start worrying about possible life-changing decisions (which, experience tells me, is truly life-changing in only 1% of the cases).

Ithaka gave you the marvelous journey.
Without her you would not have set out.
She has nothing left to give you now.

And if you find her poor, Ithaka won’t have fooled you.
Wise as you will have become, so full of experience,
you will have understood by then what these Ithakas mean.

(An aside: I’d come across the poem in Robert Fulghum‘s Words I Wish I Wrote, a book I first loaned from the library and which I later procured second-hand through a charity event. It was perhaps the most influential book in my life, introducing me to some of my favourite pieces of literature and authors, including the book Catch-22, which made me realise I could actually like fiction; and Albert Camus, who introduced me into the rather dark world of existential philosophy.)


An added bonus here. I was just re-reading Words I Wish I Wrote and came across this gem from Franz Kafka, which is another magnificent calm-your-nerves piece:

If we knew we were on the right road, having to leave it would mean endless despair. But we are on a road that only leads to a second one and then to a third one and so forth. And the real highway will not be sighted for a long, long time, perhaps never. So we drift in doubt. But also in an unbelievable beautiful diversity. Thus the accomplishment of hopes remains an always unexpected miracle. But in compensation, the miracle remains forever possible.

life: larger than our plans

The net is set for the fish,
    But catches the swan in its mesh.
The praying mantis covets its prey,
    While the sparrow approaches from the rear.
Within one contrivance hides another;
Beyond one change, another is born.
Are wisdom and skill enough to put your hopes on?

From the book Master of the Three Ways, by Hung Ying-ming (1:148).

Just a reminder to myself that sometimes life is larger than our plans.

Perspective: Million vs. Billion

"How long is a million seconds? How many days do you think that is?" I asked.

"I don't know," she said, then started counting, realised it was pretty hard to do in your head, then stopped.

I gave her the answer: "approximately 11 days".

"Now," I continued, "how about a billion? How many days is a billion seconds?"

This time she was ready. The answer was intuitive now. A million and a billion – they're not too different. We talk about millionaires and billionaires in pretty much the same breath. A little rough arithmetic and we're done.

"A hundred days?" she guessed. "Maybe a little more?"

31 years.

 


I was reminded of this fact recently in a book on financial planning by Tony Robbins, where he was trying to push the point on how we often think we know what we want, but because never thought about it in greater detail are probably really wrong about that. He used the example of how we think the road to becoming a millionaire and the road to becoming a billionaire are pretty much the same.

They're different. Very different.

Another recent story came to mind, this time in the world of fitness: that of how a pro gambler won a $1 million dollar bet to go below 10% body fat. Reading the story, I was reminded how different it was to go from 25% to 15%, as compared to going from 15% to sub-10%. Seemingly similar goals, but in reality very different.

I wonder how many other things we take for granted to be similar but in reality are anything but.

Getting Excited About Small Data

The next few quarters for analytics in my company are, from my perspective, going to be game-changing, and I’m excited to say my team’s taking the lead on it: from machine learning and advanced visualisations to new ways of thinking about data, we’re currently taking the steps to get to what I call “the next phase of analytics”. We are a small team with big dreams.

But what I often get from friends (and some colleagues) when I tell them about the things my team is doing, though, are questions on how “big data” is playing a part in it. Specifically, how it figures in our plans for the next few quarters.

When I tell them it doesn’t, they look at me as if I just said I loved eating broccoli ice-cream: perplexed; a little disgusted; and mixed with a bit of pity on the side. (If you clicked on that link or you know that song, you might have guessed I’m doing that parenting thing.)

“Big data” simply doesn’t factor in those plans (yet). We have enough small data to worry about to even think about big data. And yet, to them small data is yesterday’s news. It’s as if small data doesn’t count; as if it’s nothing to get excited about.

But it does count. And to those who haven’t yet experienced the joys of wringing all the value out of small data, it is downright exciting.

Sure, big data has the potential to change the world, and in many cases it already has. But by and large most of the value of big data still lies in its potential.

Small data, on the other hand, has long shown its ability to change the world.

I love especially this little story from the book mind+machine by Marc Vollenwider:

Using just 800 bits of HR information, an investment bank saved USD 1 million every year, generating an ROI of several thousand percent. How? Banking analysts use a lot of expensive data from databases paid through individual seat licenses. After bonus time in January, the musical chairs game starts and many analyst teams join competitor institutions, at which point the seat license should be canceled. In this case, the process step simply did not happen, as nobody thought about sending the corresponding instructions to the database companies in time. Therefore, the bank kept unnecessarily paying about USD 1 million annually. Why 800 bits? Clearly, whether someone is employed (“1”) or not (“0”) is a binary piece of information called a “bit”. With 800 analysts, the bank had 800 bits of HR information. The anlaytics rule was almost embarrassingly simple: “If no longer employed, send email to terminate the seat license.” All that needed to happen was a simple search for changes in employment status int he employment information from HR.

The amazing thing about this use case is it just required some solid thinking, linking a bit of employment information with the database licenses.

Small data can have big impact.

So yes, I am excited about small data!

And no, big data won’t be part of our coming analytics revolution. (Yet.)

The problem with running a team at full capacity

I shared this earlier on LinkedIn, but thought that it was worth sharing it here too as a reminder to myself: Six Myths of Product Development

I came across the article above while researching why a team that traditionally does great work may sometimes stumble (yes, mine). The past few weeks had been a whirlwind of activity, with team output close to or at an all time high. We were publishing and developing things left and right, and everyone was running close to capacity. It was great.

Then came an e-mail that questioned the quality of the output. Then another. Much of the great work threatened to come undone, but thankfully most made it through unscathed. We were still, generally, in a good place. But this was a wake up call. Something needed to be done.

After I explained to her my conundrum, my knowledgeable friend, Google, suggested an article from the Harvard Business Review website called “Six Myths of Product Development.”

It was a most excellent suggestion.

The article highlighted six myths or fallacies:

  1. High utilization of resources will improve performance.
  2. Processing work in large batches improves the economics of the development process.
  3. Our development plan is great; we just need to stick to it.
  4. The sooner the project is started, the sooner it will be finished.
  5. The more features we put into a product, the more customers will like it.
  6. We will be more successful if we get it right the first time.

It didn’t take long for me to realise that our problem was very likely linked to #1: I’d neglected slack.

You see, I normally tend guard slack time jealously as I know time-pressures are often a big cause of low quality output. But given the myriad of “urgent” business needs had allowed myself and the team to run too close to full capacity.

We have seen that projects’ speed, efficiency, and output quality inevitably decrease when managers completely fill the plates of their product-development employees—no matter how skilled those managers may be. High utilization has serious negative side effects… Add 5% more work, and completing it may take 100% longer. But few people understand this effect.

It’s funny how bringing down the amount of expected output may actually increase it.

(As an aside, I love point #6 – I’m a big fan of “fail fast, fail often” as I believe strongly in “the wisdom of crowds”, where we can aggregate feedback and iterate quickly, especially for early development. But it’s not always easy to get business buy-in, especially when all they see in “fail fast, fail often” is “fail”!)

 

The need for theory in prediction models

I’d like to share this wonderful quip by philosopher Robert Long, that was quoted in the (also insightful and actually pretty good) book A Richer Life by Philip Roscoe:

Let’s say that in early 2001 I formulate a theory to the effect that there is a Constant Tolkienian Force in the Universe that produces a Tolkien film every year. When Austrians complain that my theory ignores the fact that films are products of human action and not of constant impersonal forces, I reply: ‘Oh, I know that. My theory isn’t supposed to be realistic. The question is whether it’s a good predictor.’ So I test it in 2001, 2002, and 2003. Lo and behold, my theory works each year! … But unless I pay some attention to the true explanation of this sequence of film releases, I’ll be caught by surprise when the regularity fails for 2004.

The above quote relates to the “blind” prediction models that we build (maybe statistical, maybe machine learning), where “accurate prediction” is so often the aim of the game.

The problem with just going for accurate predictions, without thinking about the underlying causes or theory, is that it’s difficult to tell whether or not the predictions are flukes, or if they contain true insight that may not be so obvious to anybody or anything else but the algorithm.

Though the quote above is rather tongue-in-cheek (the “Tolkienian Force” model is really built on just three data points, way too few for statistical significance) the moral of the need for a good theory wasn’t lost on me: theory is the human ying to the technological yang. They balance and support each other, creating something much more powerful and persuasive than either would alone. When one is amiss, the other somehow doesn’t feel right either.

Another (More Selfish) Benefit of Theory

They’re actually really useful when persuading the business on accepting the results of a prediction model that they otherwise know nothing about. Without a theory, on what basis should the business believe the model? On faith?

That’s a tough sell.

And if you were the one who built the model, and they “trusted” the model because they trust you, what happens if the model fails?

Well, you fail with the model.

With a convincing theory, the model stands on its own merits. They may “trust” the model a bit more because they trust you, but if the model fails, it’s the theory that fails with it, not you.