Drawing Substantive Lines

Abstract

Abstract This chapter discusses the benchmarks and standards companies use to distinguish between ethical and unethical uses of advanced analytics and AI. In recent years scholars, governmental bodies, multi-stakeholder groups, industry think tanks, and even individual companies have issued model sets of data ethics and AI ethics principles. These model principles provide an initial reference point for setting substantive standards. However, the breath and ambiguity of these principles, and the conflicts among them, make it difficult for companies to operationalize them in all-things-considered decisions. In our study, most companies accordingly grounded their data ethics decisions, not on abstract ethical principles, but on intuitive benchmarks such as the Golden Rule or what “feels right.” Such gut-level standards, while potentially useful for approximating public expectations, are difficult to teach or apply consistently. Companies need substantive standards that are more actionable than high-level principles, and more standardized than intuitive judgment calls. They need generalizable policies that draw the line between ethical and unethical applications of advanced analytics and AI. How best to generate such company-specific policies remains an open question. One company said they did this by capturing past data ethics decisions and using them as “precedents” to guide future such decisions.

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