Human + Machine: Reimaging Work in the Age of AI by Paul R. Daugherty & H. James Wilson

Author:Paul R. Daugherty & H. James Wilson [Paul R. Daugherty]
Language: eng
Format: epub
Publisher: Harvard Business Review Press
Published: 2018-03-20T04:00:00+00:00

A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.12

Introduced in the 1942 short story “Runaround,” the three laws are certainly still relevant today, but they are merely a starting point. Should, for example, a driverless vehicle try to protect its occupants by swerving to avoid a child running into the street if that action might lead to a collision with a nearby pedestrian? Such questions are why companies that design and deploy sophisticated AI technologies will require AI safety engineers. These individuals must try to anticipate the unintended consequences of an AI system and also address any harmful occurrences with the appropriate urgency.

In a recent Accenture survey, we found that less than one-third of companies have a high degree of confidence in the fairness and auditability of their AI systems, and less than half have similar confidence in the safety of those systems.13 Moreover, past research has found that about one-third of people are fearful of AI, and nearly one-fourth believe the technology will harm society.14 Clearly, those statistics indicate fundamental issues that need to be resolved for the continued usage of AI technologies. That’s where sustainers will play a crucial role.

One of the most important functions is that of ethics compliance manager. These individuals will act as watchdogs and ombudsmen for upholding generally accepted norms of human values and morals. If, for example, an AI system for credit approval is discriminating against people in certain geographic areas, then the ethics compliance manager is responsible for investigating and addressing that ethical—and potentially legal—breach. Other biases might be subtler, for example, a search algorithm that responds with images of only white women when someone queries “loving grandmother.” The ethics compliance manager might work with an algorithm forensics analyst to uncover the reasons for those search results and then implement the appropriate fixes (see table 5-1).

TABLE 5-1

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