Tackling Bias in Machine Learning, AI and Humanity


I nearly tweeted about a dozen responses back to Mr. Hamner, repeatedly however, I pulled up short. The nuance in this tweet is an enormous comment on where we are at in our discussion on AI and its interactions with humanity. Let me start with some thoughts and then we can dissect each one

  1. Direct reference to fixing ML bias being easier to fix than bias in humans
  2. Implication that overcoming bias in ML is more valuable than in humans
  3. Implication that some people are far more comfortable with machines than people
  4. A subtle undertone picked up by me, about corporate perspective, should bias be fixed (to be clear, I do not believe, this is not Mr. Hamner’s intent)?

Direct reference to fixing ML bias being easier to fix than bias in humans

I cannot imagine what the evidence for this argument looks like. I recognize that as a CTO, Mr. Hammer’s comfort level with machines is pretty high, but likewise an ethics professor is probably more comfortable working on bias at the human level. So are we talking about skill sets? Or is there a belief that rooting out Machine Learning (ML) bias is genuinely easy. If it is so easy, why isn’t it being done already and comprehensively. I reckon that it is not easy to identify and often hidden.

If it is easy to identify, then we should talk Mr. Hamner, because I would like to benefit from your expertise and partner with you in order to bring those benefits to the rest of the Machine Learning community on behalf of humanity. Especially to those groups who experience bias. It’s a worthy endeavor to be sure and I certainly hope that Mr. Hamner is right. If ML bias is easily identifiable then AI can go a long way to eliminating bias in our evaluation of data/markets and our decision making process. Humanity will have a lot to gain by eliminating bias.

Should you be wrong, and it is difficult to root out bias in our algorithms and in our data sets, then we are at the same place where humanity sits now, with institutionalized bias, but we are about to expand its reach. Not only would these biases be pervasive in our culture, but they would be codified in our artificial intelligence. I do hope that Mr. Hamner and others are well equipped to tackle this problem. ForHumanity stands ready to work with those who feel they have a good handle on this issue and to develop ways to make it a fundamental part of all AI and ML development.

Implication that overcoming bias in ML is more valuable than in humans

This implication made me uncomfortable. Not because I think Mr. Hamner is wrong, but rather I am concerned that he is right. One of the great things about ML and AI is that it often can be broken down into discrete building blocks. Fully observable data, transparent algorithms and dedicated processes may allow us to quantify the source of bias. If and when that is true, we may find it a fairly straightforward process to identify and readjust bias in our ML processes. However, today, we know that many deep learning processes are quite opaque to their designers. These technique have become so “deep” that their designers frequently are unsure why/how they work. This fact, for me, is worrisome, especially when considering bias. In these types of processes, if bias is introduced it may prove exceedingly difficult to remove. So, given the complexity of some of the ML going on today, we can be certain that perfect compliance is literally impossible. I remain optimistic, with Mr. Hamner, that in some ML we can identify and remove bias. Where we can, we should and it should be done post haste.

So then the question is, is it easier than overcoming bias in humans. Humans often obfuscate. Their data sets are not transparent, their algorithms completely opaque and their processes far from dedicated. But instead, humans have a will. They may even have a desire to change and seek out the elimination of their bias, especially when confronted with them. This is a societal decision, do we work with each other to face our bias, and then work to change them. This can only happen when we wake up and realize that all of humanity has EQUAL value. Minorities and Majorities, Each race, Each gender, Each sexual orientation, Each Faith or non-Faith, Each Political Party, Each Age and the list can go on and on. But we do not believe this today. Take our political discourse currently, each side thinks the other is either lunatics or ignorant. The answer is that neither is right.

The more value each member of our society places in each other member, the easier it is to eliminate bias. In fact, you would have changed the will of the people. Instead of hiding behind their bias, or worse yet, not even recognizing it, people will actively improve. Seeking out ways to eliminate their bias and increase the value that they can receive through equality. All very dreamy, I know, but it is a good dream and should be a goal.

This is all quite a long answer to the question of is ML bias easier to root out than human bias. But the answer is unsurprisingly, it depends. For certain situations and people, when confronted well by their peers and approached from an aspect of healing versus judgement, then I believe humans are easier to heal from bias, than any machine. Faced with the most difficult curmudgeon, who simply will not realize that all people have value, then the ML bias removal will be significantly easier and Mr. Hamner will be correct.

Implication that some people are far more comfortable with machines than people

This undertone to Mr. Hamner’s tweet makes me sad. Number one, I know this is a very accurate implication and two, I think it is increasing. If people are increasingly more comfortable with their machines, I believe they will actually be damaging their humanity, especially when that machine becomes the center of their focus. Even if our machines and our technology make our lives easier, do they make them better? From a microeconomic level, the answer is almost always “yes”, they do make them better. Otherwise, how else did that technology come into being? If you look at a cybernetically linked prosthetic that returns the ability of a person to have a hand and use it with their mind, with the same dexterity and functionality as before they lost their limb, there is beauty in that. A deep beauty that stirs the soul and endears the development of technology to the masses.

But from another more macro-economic perspective, our technology may have consequences we don’t realize. More importantly, I am certain that people, broadly speaking do not realize that much of our lives today are based on a MASSIVE assumption. An assumption that society will continue in its present form. Take GPS… a marvelous convenience to be sure. All maps at the ready, voice activated directions to allow you to keep your eyes on the road and hands on the wheel. Understandably, everyone uses it. But do people even realize anymore that knowing where you are, how to get from here to there and maybe most importantly, being able to read a map were once life and death skills. Failure to have those skills, meant certain death for some. It is easy to assume that our systems, our technology and our society will continue on in one direction never having a hiccup, a breakdown or worse yet a reversal. There are certainly scenarios I can imagine where many of the primitive skills, which were once common place to all people/society, may become required again. Putting all of your faith in technology might be easy and commonplace, but it doesn’t guarantee that it will always be there for you.

A society that eschews its human relationships and breaks down its sense of community is a fragile one indeed. Our technology is creating an illusion of self-sufficiency that is wrong. Each of us is completely dependent on a myriad of links in the chain that allows our existence to thrive. But like a chain link, it could be rendered useless, if even a single link were to break. And when your chain link breaks, who will be there to help? Not your technology. Will it be your community? Will it be your friends and peers? Will it be your neighbors?

I won’t dwell on the value of human relationships, as other have spent great time and effort documenting this, including a recent article by Brad Stulberg in NY Magazine. I recommend you give it a read.


I simply suggest that cultivating a robust community of human relationships takes considerable effort and it is an investment that will pay dividends in your-well being. Furthermore, it is likely to create a robust cushion for you if we do see disruption in society in its current form.

A subtle undertone, from a corporate perspective, should bias be fixed?

To be clear and fair to Mr. Hamner, I do not believe he was commenting on this idea. But as I began to consider ML bias, I realized that I doubt it is always in a corporate entity’s best interest to remedy bias in their AI. Now, before the pitchforks come out, let me be clear from ForHumanity’s perspective, YES, all bias should be removed. However, corporations may not be fully aligned with the best interest of society on this one. Let me explain.

If bias is removed from a company’s algorithms, the resulting decisions may not provide a product or solution that your customers actually want. Ostensibly, the data that was used in a company’s algos was identified as the right set to solve a problem for your customers. If that data set is altered to remove bias, the result might not be palatable to the customer, especially if they hold that bias. If their bias prevents them from employing the solution or purchasing the product, then the removal of the bias has hurt business. So while it is in society’s best interest to remove bias and to value all members equally, that is not how corporations act. Corporations, at least in the United States, have a responsibility to shareholders, not society. There are many examples in history, where companies have put their bottom-line ahead of the best interest of their community. We’d be foolish to think that will magically change now. Society and Corporations may be misaligned on the value to eliminating bias. So we certainly cannot rely on companies alone, to lead the way on the removal of bias from their AIs. We will have to make them do it.

Bias is wrong. In all its forms, in all its manifestations. Wherever it is found, it should be rooted out and changed. But this is society’s challenge. This is ForHumanity’s challenge. And it applies to our technology as well as it applies to all people. I want to thank Mr. Hamner for an extremely thought provoking tweet, whether he meant it as such or not. I hope that my thoughts are useful to all and that where appropriate, you will join with me to combat bias and to tackle the changes that AI & Automation pose for our humanity.