HBR's 10 Must Reads 2022 by Harvard Business Review

Author:Harvard Business Review
Language: eng
Format: epub
Publisher: Harvard Business Review Press
Published: 2021-10-12T00:00:00+00:00

Moral Risk

Products and services that make decisions autonomously will also need to resolve ethical dilemmas—a requirement that raises additional risks and regulatory and product development challenges. Scholars have now begun to frame these challenges as problems of responsible algorithm design. They include the puzzle of how to automate moral reasoning. Should Tesla, for example, program its cars to think in utilitarian cost-benefit terms or Kantian ones, where certain values cannot be traded off regardless of benefits? Even if the answer is utilitarian, quantification is extremely difficult: How should we program a car to value the lives of three elderly people against, say, the life of one middle-aged person? How should businesses balance trade-offs among, say, privacy, fairness, accuracy, and security? Can all those kinds of risks be avoided?

Moral risks also include biases related to demographic groups. For example, facial-recognition algorithms have a difficult time identifying people of color; skin-lesion-classification systems appear to have unequal accuracy across race; recidivism-prediction instruments give Blacks and Hispanics falsely high ratings, and credit-scoring systems give them unjustly low ones. With many widespread commercial uses, machine-learning systems may be deemed unfair to a certain group on some dimensions.

The problem is compounded by the multiple and possibly mutually incompatible ways to define fairness and encode it in algorithms. A lending algorithm can be calibrated—meaning that its decisions are independent of group identity after controlling for risk level—while still disproportionately denying loans to creditworthy minorities. As a result, a company can find itself in a “damned if you do, damned if you don’t” situation. If it uses algorithms to decide who receives a loan, it may have difficulty avoiding charges that it’s discriminating against some groups according to one of the definitions of fairness. Different cultures may also accept different definitions and ethical trade-offs—a problem for products with global markets. A February 2020 European Commission white paper on AI points to these challenges: It calls for the development of AI with “European values,” but will such AI be easily exported to regions with different values?

Finally, all these problems can also be caused by model instability. This is a situation where inputs that are close to one another lead to decisions that are far apart. Unstable algorithms are likely to treat very similar people very differently—and possibly unfairly.

All these considerations, of course, don’t mean that we should avoid machine learning altogether. Instead, executives need to embrace the opportunities it creates while making sure they properly address the risks.

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