The guidelines acknowledge that AI can have a negative impact on children, people with disabilities, and other groups that have been historically disadvantaged. It also emphasizes continuous auditing and oversight.

In 2018, Microsoft launched a research and advocacy program focused on fairness in AI. Like Singapore’s FEAT, Microsoft’s “AI Fairness Checklist” asserts that no single definition of fairness exists and that the goal of any AI-powered system should be to minimize harm. Last year, the U.S. Federal Trade Commission put out its own statement calling for “truth, fairness, and equity” in the use of AI in financial decision-making. The statement urged financial institutions to tell the truth about their data, aim for transparency, and to “do more good than harm” or the FTC will challenge the use of that model as unfair.

Concerns over bias and fairness of AI systems must be addressed, says ServiceNow’s Abuhamad, but not by focusing on universal notions of fairness. “Instead,” she says, “be fully transparent about what your vision of fairness is and how you’ve evaluated your algorithms based on that vision.”