Ben Shneiderman is an Emeritus Distinguished University Professor in the Department of Computer Science at the University of Maryland, and a much-honoured pioneer in the field of Human-Computer Interaction. His recent book, Human-Centered AI, is a valuable contribution to the literature discussing challenges for the appropriate use of artificial intelligence and proposing approaches and steps to achieve a safer and more humane future incorporating the likely increased use of AI.
Although there is much that I could discuss, I shall focus primarily on Part 3, Design Metaphors, and Part 4, Governance Structures.
AI and in particular machine learning has made great progress in the last decade. Yet I am deeply concerned about the hype associated with AI, and the risks to society stemming from premature use of the software. We are particularly vulnerable in domains such as medical diagnosis, criminal justice, seniors care, driving, and warfare. Here AI applications have begun or are imminent. Yet much current AIs are unreliable and inconsistent, without common sense; deceptive in hiding that they are algorithms and not people; mute and unable to explain decisions and actions; unfair and unjust; free from accountability and responsibility; and used but not trusted.
Nosedive was the first episode of the third season of the British science fiction television anthology Black Mirror. In this episode, everyone has a mobile phone which, when pointed at another person, reveals his or her name and rating. Everyone has a rating, which ranges from 0 to 5. The following happens continually as you are walking down a street or along the corridor of a building. You give a ‘thumbs up’ or ‘thumbs down’ to each person you pass, based on your instantaneous impression of that person and the nature of the encounter, no matter how trivial or quick the encounter is. A ‘thumps up’ raises that person’s rating a tiny bit; a ‘thumbs down’ lowers it. The other person concurrently rates you. Ratings determine one’s status in life, and the ability to get perks such as housing and travel. Therefore, people are on a never-ending, stressful, and soul-destroying quest to raise their online ratings for real-life rewards. Heroine Lacie desires a better apartment; she has a meltdown as she deals with unsurmountable pressure in the context of her childhood best friend’s wedding.
In this column, in my textbook, and in a speech “What Society Must Require from AI” I am currently giving around the world, I document some of the hype, exaggerated claims, and unrealistic predictions that workers in the field of artificial intelligence (AI) have been making for over 50 years. Here are some examples. Herb Simon, an AI pioneer at Carnegie-Mellon University (CMU), who later won a Novel Prize in Economics, predicted in 1958 that a program would be the world’s best champion by 1967. Marvin Minsky of MIT, and Ray Kurzweil, both AI pioneers, made absurd predictions (in 1967 and 2005) that AI would achieve general human intelligence by 1980 and by 2045. John Anderson, discussed below, made the absurd prediction in 1985 that it was already feasible to build computer systems “as effective as intelligent human tutors”. IBM has recently made numerous false claims about the effectiveness of its Watson technology for domains as diverse as customer support, tax filing, and oncology.