AI can be a black box, which often renders us unable to answer crucial questions about its operations. In this article I explain when explainable AI is important, and why.
(Originally appeared in the July/August 2022 print edition of HBR). The sources of problems in AI are many. You need a committee—comprising ethicists, lawyers, technologists, business strategists, and bias scouts—to review any AI your firm develops or buys to identify the ethical risks it presents and address how to mitigate them. This article describes how to set up such a committee effectively.
Companies are quickly learning that AI doesn’t just scale solutions — it also scales risk. In this environment, data and AI ethics are business necessities, not academic curiosities.
This article looks at four risks — the lack of third-party protections, the threat of privacy violations, the zero-state problem, and bad governance — and offers advice for how blockchain developers and users can mitigate potential harm.
When it comes to AI, focusing on fairness and bias ignores a huge swath of ethical risks; many of these ethical problems defy technical solutions.
There’s a growing demand for transparency around why an AI solution was chosen, how it was designed and developed, on what grounds it was deployed, how it’s monitored and updated, and the conditions under which it may be retired.
While concerns about AI and ethical violations have become common in companies, turning these anxieties into actionable conversations can be tough.
When most organizations think about AI ethics, they often overlook some of the sources of greatest risk: procurement officers, senior leaders who lack the expertise to vet ethical risk in AI projects, and data scientists and engineers who don’t understand the ethical risks of AI.
Data and analytics leaders are in the organizationally unique position to spearhead ethical data practices. Here are four key practices that chief data officers/scientists and chief analytics officers (CDAOs) should employ when creating their own ethical data and business practice framework.
As work from home has become the new normal, many employers have started to worry about just how much work their employees are doing.