A new course to teach people about fairness in machine learning

For instance, we would be appalled if a court found that the conviction rate for people like the defendant was extremely low, so the trial was going to start with the presumption of 80% guilt.

What's the problem? It's not one fixed by better modelling or a better bias percentage. The problem is that fairness in many contexts is about processes and institutions. We expect courts to follow principles like due process and equal protection under the law.

Companies like Google can't jump right to social science and social engineering without thinking about fairness and justice in these terms.

Can a user confirm that the data about them is accurate? If not, what can they do about it?

If a business has its account algorithmically cancelled, what is the appeals process? How is it fair?

When companies like Google start getting into social science, the problem is extremely complicated fast. For example, it starts looking like a judicial system in parts, but one that isn't established and maintained by a legitimate government.

The problem with them using words like 'fairness' is anyone who disagrees with what they're doing is immediately put on the defensive and labelled as 'unfair'. This is far from the truth though, as what society today is actually doing, is trying to figure out exactly what 'fair' should mean. Google et al just took it upon themselves to start pushing their particular brand of fairness onto society and acting as if it's the most natural widely-accepted common sense.

Google's idea of fairness is to build ML solutions that don't accurately predict the world, but rather inject Google biases into the predictions in order to bias some real-world process, and ultimately change the distribution of some factors in the real world according Google's image of how things should be. Not enough <demographic> represented in the workforce? Bias the candidate selection model to equalize it. Some <demographic> is over-represented in crime statistics? Bias jail sentencing model to be more lenient on them and hope it equalizes their representation in stats over time.

This is not inherently a bad mechanism, but it's an extremely powerful mechanism with far-reaching consequences, and it is currently controlled by a small number of people at the top of the US tech industry, with no checks or oversight. I worry that there's a strong possibility that the long term effects of messing around with these levers have not been considered. We have done serious unintentional damage with far smaller levers like the Australian Rabbit Plague.

I don't know what 'fairness' should actually mean in modern society, but we definitely need to be aware that the US tech industry is hijacking the term to fit their agenda, and need to be checked. And the exact nature of the biases they're injecting should definitely be made transparent and put to public debate.

Not even a paragraph in and I see problems with this. Seems somebody bias has already gotten in their own way.

This is the first step - redefine the meanings of words to fit your agenda.

I could be wrong about the intent. But when you can redefine fairness the you can make it fair to take from one person and give to another. Or deny one person and Grant access to another based on things that don't matter.

Also. The attack on human bias is totally silly. It's a key trait that had made us and other species successful. Without these biases we might pick the wrong fruit eat it and die. We might try to cuddel into a cave with bears in the winter.

Our entire existence and reasoning skills is based off bases.

Also. Never in my life have a seen ripe or unripe bannans and described them by their color. Is this a education problem?

But even if I had there is nothing wrong with pointing out when something is not atypical. Besides if I plan on eating bananas in a few days I might want the "green" ones. Or if I was going to make a yummy smoothie the "brown" ones are the ones I want.

Also bananas are a bad analogy for what they were getting at. Every banana has the potential to be green, then yellow, then brown. So none of these states are out of the ordinary.

I don't need or want to be unconsciously retrained by ML. I think we are going to need laws against big cooperations using their power and tech to change socal constructs. It's not their place and we can never be sure they are doing it for good or even the outcome will be good for human kind. It's also a power that can be abused. We already have internal documents comming from Google suggesting swaying public opinion on on political canadates. And this type of research is exactly where you would start.

Every ML model can have different bias on different features. That’s what makes model a model. The fairness means you should make sure that your model is not biased towards above specific attributes and explicitly test for it. This is not some liberal propaganda by tech but required by law in many instances even if ML was not involved. This only applies to models used for certain classes of decision making, for example, loan eligibility or hiring or FaceID login. It doesn’t apply to things like breast cancer predictions or shopping recommendation system.

To sport the classic over simplification, ML is just glorified curve fitting. But in reality, it's not like we created a special new species with intelligence that we have to careful teach not to be racist. _We_ just have to be careful not to be racist with it.

This isn't a normative statement, just descriptive. Whether or not google should bias its results is a completely different discussion.

I can easily write you a completely unbiased “model” predicting who comitted crimes:

    func probability_of_comitting_crime():
        return 1/human_population

Ethically bankrupt advertising corporations have no business teaching people about ethics.

I'll just call their attention to their own rule on this: "Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith."

The weak interpretation of these dead comments is that they're just racists who hate dark-skinned people. There are much stronger interpretations and those should be discussed.

Especially in the context of Google, where it's been demonstrated over and over through video leaks, court filings and the Damore firing, that a single and very heavy-handed ideology has power and will not allow discussion. From a centrist position is seems obvious Google's internal thought process will be itself heavily biased, and the possibility of certain factual hypothesis being true about the physical world (not normative statements on what to do) will be denied for ideological reasons. It's like a group of fundamentalist Christian scientists studying geology. This needs to be acknowledged in any discussion of Google, ML, and 'bias'.