Algorithms are at the heart of the Big Data/machine learning/AI changes that are propelling computerized decision-making. In their book, The Ethical Algorithm, Michael Kearns and Aaron Roth, two Computer Science professors at Penn, flag some of the social and ethical choices these changes are forcing upon us. My interview with them touches on many of the hot-button issues surrounding algorithmic decision-making. Michael and Aaron may not agree with my formulation, but the conversation provides a framework for testing it – and leaves me more skeptical about “bias hacking” of algorithmic outputs.
Less controversial, but equally fun, is a dive into the ways in which Big Data and algorithms defeat old-school anonymization – and the ways in which that problem can be solved. Our guests from Philadelphia help me understand the value of differential privacy. And if you wondered why, say, much of the social science and nutrition research of the last 50 years doesn’t hold up to scrutiny, blame Big Data and algorithms that reliably generate significant correlations once in every 20 tries.
Michael and Aaron also take us deep into the unexpected social costs of algorithmic optimization. It turns out that a recommendation engine that produces exactly what we want, even when we didn’t know we wanted it, is great in the moment but maybe not so great for society. Creating markets in areas once governed by social norms can optimize individual choice but at a considerable social cost, and it turns out that algorithms can do the same – optimize individual gratification in the moment while roiling our social and political order in unpredictable ways. We would react badly to a proposal that dating choices become microeconomic transactions (otherwise known as prostitution) but we don’t feel the same way about reducing them to algorithms. Maybe we should.
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The views expressed in this podcast are those of the speakers and do not reflect the opinions of the firm.