AI Terminology Matters

In the daily news and social media, AI terms are part of the popular lexicon for better or for worse. AI technology is both praised and feared in different corners of society. Big data practitioners and even educators add confusion by misusing AI terms and concepts.

“Algorithm” and “machine learning” may be the most prevalent terms that are picked up in the popular dialogue, including in the important fields of ethics and policy. The ACM and SIGAI could have a critical educational role in the public sphere. In the area of policy, the correct use of AI terms and concepts is important for establishing credibility with the scientific community and for creating policy that addresses the real problems.

In recent weeks, interesting articles have appeared by writers diverse in the degree of scientific expertise. A June issue of The Atlantic has an article by Henry Kissinger entitled “How the Enlightenment Ends” with the thesis that society is not prepared for AI. While some of the understanding of AI concepts can be questioned, the conclusion is reasonable: “AI developers, as inexperienced in politics and philosophy as I am in technology, should ask themselves some of the questions I have raised here in order to build answers into their engineering efforts. The U.S. government should consider a presidential commission of eminent thinkers to help develop a national vision. This much is certain: If we do not start this effort soon, before long we shall discover that we started too late.”

In May, The Atlantic had an article about the other extreme of scientific knowledge by Kevin Hartnett entitled “How a Pioneer of Machine Learning Became One of Its Sharpest Critics”. He writes about an interview with Judea Pearl about his current thinking, with Dana Mackenzie, in The Book of Why: The New Science of Cause and Effect. The interview includes a criticism of deep learning research and the need for a more fundamental approach.

Back to policy, I recently attended a DC event of the Center for Data Innovation on a proposed policy framework to create accountability in the use of algorithms. They have a report on the same topic. The event was another reminder of the diverse groups in dialogue in the public sphere on critical issues for AI and the need to bring together the policymakers and the scientific community. SIGAI can have a big role to play.

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