Responding to Fears of AI.

• Author: Hemphill, Thomas A.

Turning Point: Policymaking in the Era of Artificial Intelligence

By Darrell M. West and John R. Allen

277 pp.; Brookings

Institution Press, 2020

Artificial intelligence is here. How can society make the best use of it?" asks Darrell West, vice president and director of governance studies and the Center for Technology Innovation at the Brookings Institution, and John Allen, Brookings' president. In their new book Turning Point, they attempt to answer that question, focusing on artificial intelligence (AI) data, text, or images and undertake mten applications in health care, education, transportation, e-commerce, and national defense. They also review the "techlash" movement against digital commerce and offer suggestions for ethical safeguards as well as lay out their vision of building responsible AI in society. While West and Martin include sections on AI policymaking in China and the European Union in their study, this review focuses on their thoughts on U.S. AI policymaking.

Exploring AI I In Chapter One, "What is AI?" the authors embrace the definition offered by Indian engineers Shukla Shubhendu and Jaiswal Vijay: "machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgement, and intention." This definition differentiates AI from mechanical devices or traditional computer software, as Al-based computer systems learn from data, text, or images and undertake intentional and intelligent decisions based on that analysis.

AI operating today is considered "artificial narrow intelligence" (ANI), which is defined as supporting specific processes with well-defined rules (and, incidentally, does not have any "intelligence" or "common sense"). The next phase of AI, "artificial general intelligence" (AGI), would consist of software that has cognitive abilities similar to humans and a sense of consciousness; so far, it remains technologically aspirational. Machine learning (ML), an important part of AI, consists of algorithms that can classify and learn from data, pictures, text, or objects without relying on rules-based programming. AI is dependent on data analytics, which involve the application of statistical techniques to uncover trends or patterns in large data sets. For AI to make informed decisions, effective ML and data analytics are prerequisites.

AI applied I In Chapter Two, "Healthcare," West and Allen note AI opportunities that already exist in assisting physician diagnostics in the fields of dermatology ("skin cancer"), ophthalmology ("diabetic retinopathy"), radiology ("detecting breast cancer"), and oncology ("offering personalized treatment of cancer at the molecular level"). Moreover, ML (specifically, "natural language processing") is being used to analyze text-based medical records to anticipate patient risks. In new drug clinical trials, the application of AI and ML can reduce the time necessary to bring new drugs to market. In addition, AI can more efficiently scan research studies, molecular databases, and conference proceedings to identify possible drug candidates. And AI and ML can combat health care fraud, abuse, and waste (estimated by the U.S. Government Accountability Office at $75 billion annually) by identifying suspicious treatment plans or lab test usage. Yet, AI problems in health care are pervasive and include having unrepresentative or incomplete data or using AI operationally in a manner that promotes biases based...