We need to decouple AI from human brains and biases


In the summer of 1956, 10 scientists met at Dartmouth College and invented artificial intelligence. Researchers from fields like mathematics, engineering, psychology, economics, and political science got together to find out whether they could describe learning and human thinking so precisely that it could be replicated with a machine. Hardly a decade later, these same scientists contributed to dramatic breakthroughs in robotics, natural language processing, and computer vision.

Although a lot of time has passed since then, robotics, natural language processing, and computer vision remain some of the hottest research areas to this day. One could say that we’re focused on teaching AI to move like a human, speak like a human and see like a human.

The case for doing this is clear: With AI, we want machines to automate tasks like driving, reading legal contracts or shopping for groceries. And we want these tasks to be done faster, safer and more thoroughly than humans ever could. This way, humans will have more time for fun activities while machines take on the boring tasks in our lives.

However, researchers are increasingly recognizing that AI, when modeled after human thinking, could inherit human biases. This problem is manifest in Amazon’s recruiting algorithm, which famously discriminated against women, and the U.S. government’s COMPAS algorithm, which disproportionately punishes Black people. Myriad other examples further speak to the problem of bias in AI.

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. Instead, it concluded that it should replicate the data it was fed, exacerbating the biases embedded in the data.

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. New methods limit the risk of bias polluting the results, whether from the data set or the engineers themselves. Also, emerging techniques mean that the engineers themselves will need to interfere with the AI less, eliminating more boring and repetitive tasks.

When human knowledge is king

Imagine the following scenario: You have a big data set of people from different walks of life, tracking whether they have had COVID or not. The labels COVID / no-COVID have been entered by humans, whether doctors, nurses or pharmacists. Healthcare providers might be interested in predicting whether or not a new entry is likely to have had COVID already.

Supervised machine learning comes in handy for tackling this kind of problem. An algorithm can take in all the data and start to understand how different variables, such as a person’s occupation, gross income, family status, race or ZIP code, influence whether they’ve caught the disease or not. The algorithm can estimate how likely it is, for example, for a Latina nurse with three children from New York to have had COVID already. As a consequence, the date of her vaccination or her insurance premiums may get adjusted in order to save more lives through efficient allocation of limited resources.

This process sounds extremely useful at first glance, but there are traps. For example, an overworked healthcare provider might have mislabeled data points, leading to errors in the data set and, ultimately, to unreliable conclusions. This type 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.