Reports on the lack of ethics in artificial intelligence are many. But most of them are lightweight\u2014full of platitudes about \u201cpublic-private partnerships\u201d and bromides about putting people first. They don\u2019t acknowledge the knotty nature of the social dilemmas AI creates, or how tough it will be to untangle them. The new\u00a0report\u00a0from the AI Now Institute isn\u2019t like that. It takes an unblinking look at a tech industry racing to reshape society along AI lines without any guarantee of reliable and fair results.
\nThe report,\u00a0released two weeks ago, is the brainchild of Kate Crawford (shown above) and Meredith Whittaker, cofounders of\u00a0AI Now, a new research institute based out of New York University. Crawford, Whittaker, and their collaborators lay out a research agenda and a policy roadmap in a dense but approachable 35 pages. Their conclusion doesn\u2019t waffle: Our efforts to hold AI to ethical standards to date, they say, have been a flop.
\n\u201cNew ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, governmental and military interests accountable as they design and employ AI,\u201d they write. When tech giants build AI products, too often \u201cuser consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit-driven business models based on aggregated data profiles\u2026\u201d Meanwhile, AI systems are being introduced in\u00a0policing, education, healthcare, and other environments where the misfiring of an algorithm could ruin a life. Is there anything we can do? Crawford sat down with us this week for a discussion of why ethics in AI is still a mess, and what practical steps might change the picture.
\nQ. Towards the end of the new report, you come right out and say, \u201cCurrent framings of AI ethics are failing.\u201d That sounds dire.
\nKate Crawford:\u00a0There\u2019s a lot of talk about how we come up with ethical codes for this field. We still don\u2019t have one. We have a set of what I think are important efforts spearheaded by different organizations, including\u00a0IEEE,\u00a0Asilomar, and others. But what we\u2019re seeing now is a real air gap between high-level principles\u2014that are clearly very important\u2014and what is happening on the ground in the day-to-day development of large-scale machine learning systems.
\nWe read all of the existing ethical codes that have been published in the last two years that specifically consider AI and algorithmic systems. Then we looked at the difference between the ideals and what was actually happening. What is most urgently needed now is that these ethical guidelines are accompanied by very strong accountability mechanisms. We can say we want AI systems to be guided with the highest ethical principles, but we have to make sure that there is something at stake. Often when we talk about ethics, we forget to talk about power. People will often have the best of intentions. But we\u2019re seeing a lack of thinking about how real power asymmetries are affecting different communities.
\nQ. The underlying message of the report seems to be that we may be moving too fast\u2014we\u2019re not taking the time to do this stuff right.
\nI would probably phrase it differently. Time is a factor, but so is priority. If we spent as much money and hired as many people to think about and work on and empirically test the broader social and economic effects of these systems, we would be coming from a much stronger base. Who is actually creating industry standards that say, ok, this is the basic pre-release trial system you need to go through, this is how you publicly show how you\u2019ve tested your system and with what different types of populations, and these are the confidence bounds you are prepared to put behind your system or product?
\nRead the source article in Wired.
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