Integrating (not “adding”) ethics and critical thinking into data science

“Can we solve problems stemming from human bias by turning decisions over to machines? In theory, more and more decisions increasingly handled by algorithms should mean that human biases and prejudices should be eliminated. Algorithms are, after all, “neutral” and “objective”. They apply the same rules to everybody regardless of race, gender, ethnicity or ability. The reality, however, is far from this”.

Abeba Birhane

One does notThere is no area untouched by data science and computer science. From medicine, to the criminal justice system, to banking & insurance, to social welfare, data driven solutions and automated systems are proposed and developed for various social problems. The fact that computer science is intersecting with various social, cultural and political spheres means leaving the realm of the “purely technical” and dealing with human culture, values, meaning, and questions of morality; questions that need more than technical “solutions”, if they can be solved at all. Critical engagement and ethics are, therefore, imperative to the growing field of computer science.

And the need for ethical and critical engagement is becoming more apparent as not a day goes by without a headline about some catastrophic consequences of careless practices, be it a discriminatory automated hiring system or implementation of a facial recognition system that undermines privacy. With the realization that the…

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  1. It has been demonstrated AI became unbeatable by a human chess player. It’s a fact: transistors doesn’t get tired as fast as neurons. However, would AI ever be able to feel when a kid in front of it needs to win prior to get discouraged?
    How many years of learning errors would be required prior AI will “learn” common sense and humanity in decision making? And what will be the social and environmental cost of this learning period if not offset by human control?


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