Algorithmic Bias Was Born in the 1980s

Apparently, the creator of that algorithm literally and consciously added bias against females and non-caucasian to his program, which made it pretty much an open and shut case.

The modern approach would be to take some NLP machine learning kit, which would do a wonderful job learning and reproducing the human assessors' biases, without any overt evidence (because the reasoning is always opaque), without ever being explicitly instructed to do so, and possibly even without the author of the program being aware of it.

On two occasions I have been asked, — "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" In one case a member of the Upper, and in the other a member of the Lower, House put this question. I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

-Charles Babbage, Passages from the Life of a Philosopher, 1864

They say that those who don't learn from the past are doomed to repeat it, yet somehow statistical data driven algorithms that literally learn from the past provide guarantees of repeating it.

Algorithmic bias really was born a long time before then. There were "computers" and "algorithms" long before the 80s. Perhaps algorithmic bias in the age of personal computers was born in the 80s.

Is the code available for review? I didn't see any mention of it being available in the article. Maybe if ieee has it, they can post it to github? Did the guy hard code a list of "european" names directly into the code or did he store it in a file or even a db? Did it just check the surnames or the first and middle names also?

Also, is bias still in the college admissions system in the UK? I know we have a form of it in the US.

Seems a bit different from modern day algorithmic discrimination. In the 2016 pro publica example, for instance, the algorithm doesn't explicitly consider race but just picks up the fact that black inmates more often have other features correlated with reoffending (they are younger, for instance). Now that might very well indicate that there's a systematic bias against younger black people in the training data and that they are more likely to be singled out for arrest than others, but the algorithm did an okay job given the unbalanced arrest prevalence in the training data. Of course, there's still a lesson of not blindly trusting the algorithm but trying to understand why the data caused it to make its decisions.

But at least no one went to such lengths as to encode ethnicity as a separate feature to weigh in the process. I wish the article gave more detail about the algorithm - was it a manually constructed decision tree or what? Because I wonder what the guy was doing. Surely even in the 70/80s you should have been aware that encoding ethnicity as some explicit variable is very discriminatory.

It would be interesting to see how the code classified names as European/non-European. Given that apparently no ML was involved, I assume it was based on a list of European names, and if your name is not on the list, you're not European and you lose 15 points ? Or some kind of pattern matching (Reged or similar), again if your name doesn't match, you lose 15 points ? It seems very strange that someone would go to such lengths to explicitly code this kind of bias.

> After all, Franglen had tested the machine against humans and found a 90 to 95 percent correlation of outcomes

False. Algorithmic bias was present from the very beginning. One way or another, directly or indirectly, algorithms only do what people tell them to do.