In the summer of 1956, 10 scientists met at Dartmouth College andinvented artificial intelligence.
And we want these tasks to be done faster, safer and more thoroughly than humans ever could.
However, researchers are increasingly recognizing that AI, when modeled after human thinking, could inherit human biases.
Myriad other examples further speak to the problem of bias in AI.
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In both cases, the problem began with a flawed data set.
Most of the employees at Amazon were men, and many of the incarcerated people were Black.
Although those statistics are the result of pervasive cultural biases, the algorithm had no way to know that.
Manual fixes can get rid of these biases, but they come with risks.
If not implemented properly, well-meaning fixes can make some biases worse or even introduce new ones.
Recent developments regarding AI algorithms, however, are making these biases less and less significant.
Engineers should embrace these new findings.
The labels COVID / no-COVID have been entered by humans, whether doctors, nurses or pharmacists.
Supervised machine learning comes in handy for tackling this kind of problem.
This process sounds extremely useful at first glance, but there are traps.
This throw in of mistake is especially damaging in the aforementioned employment market and incarceration system.
Supervised machine learning seems like an ideal solution for many problems.
But humans are way too involved in the process of making data to make this a panacea.
In a world that still suffers from racial and gender inequalities, human biases are pervasive and damaging.
AI that relies on this much human involvement is always at risk of incorporating these biases.
In the COVID-predictor example, it might make sense to eliminate the human-made COVID / no-COVID labels.
For one thing, the data might be wrong due to human error.
Another major problem is that the data may be incomplete.
This absence may skew the data set.
This way, one obtains several different groups.
Nevertheless, the results might be a lot more reliable for insurers or vaccine providers.
Needless to say, this would be an unfair policy based on racist assumptions.
Leaving decisions up to the machine can help to circumvent bias ingrained in decision-makers.
This is the concept behind reinforcement learning.
You provide the same data set as before, without the human-made labels since they could skew results.
You also feed it some information about insurance policies or how vaccines work.
By training on the data set, it finds policies or vaccine dates that optimize these objectives.
This process further eliminates the need for human data-entry or decision-making.
This changes the problem and makes reinforcement learning unsuitable.
Most of these root to the data set.
This tactic shouldnt be confused with blinding sensitive data, however.
For example, one could choose to blind race data so that avoid discrimination.
And ZIP codes are, in many cases, strongly correlated to race.
To confirm that this doesnt happen, one can add weights to the race data.
A machine learning model might quickly conclude that Latino people get COVID more often.
Imagine, for example, that in our COVID data set, there are only a few Native Americans.
By chance, all these Native Americans happen to be taxi drivers.
Also, its only a temporary solution.
In the longer term, we should let go of human meddling and the bias that comes with it.
But humans also say and think awful things, especially toward underprivileged groups.
Machines, on the other hand, havent grown up in a society of racial and economic disparities.
They just take whichever data is available and do whatever theyre supposed to do with it.
But many of these flaws in data sets can be compensated with better models.
But we shouldnt forget that we can often achieve better results if we leave machines to do their thing.
This article was originally published on Built In.
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