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AI Can Help Us Live More Deliberately

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Abstract

A clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart. Descriptions of AI's possible effects on businesses and their employees cycle between utopian hype and alarmist doomsaying. This book from MIT Sloan Management Review avoids both these extremes, providing instead a clear-eyed look at how AI can complement (rather than eliminate) human jobs, with real-world examples from companies that range from Netflix to Walmart. The contributors show that organizations can create business value with AI by cooperating with it rather than relinquishing control to it. The smartest companies know that they don't need AI that mimics humans because they already have access to resources with human capability—actual humans. The book acknowledges the prominent role of such leading technology companies as Facebook, Apple, Amazon, Netflix, and Google in applying AI to their businesses, but it goes beyond the FAANG cohort to look at AI applications in many nontechnology companies, including DHL and Fidelity. The chapters address such topics as retraining workers (who may be more ready for change than their companies are); the importance of motivated and knowledgeable leaders; the danger that AI will entrench less-than-ideal legacy processes; ways that AI could promote gender equality and diversity; AI and the global loneliness epidemic; and the benefits of robot–human collaboration. Contributors Cynthia M. Beath, Megan Beck, Joe Biron, Erik Brynjolfsson, Jacques Bughin, Rumman Chowdhury, Paul R. Daugherty, Thomas H. Davenport, Chris DeBrusk, Berkeley J. Dietvorst, Janet Foutty, James R. Freeland, R. Edward Freeman, Julian Friedland, Lynda Gratton, Francis Hintermann, Vivek Katyal, David Kiron, Frieda Klotz, Jonathan Lang, Barry Libert, Paul Michelman, Daniel Rock, Sam Ransbotham, Jeanne W. Ross, Eva Sage-Gavin, Chad Syverson, Monideepa Tarafdar, Gregory Unruh, Madhu Vazirani, H. James Wilson

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Artificial intelligence (AI) systems have become a new reality of modern life. They have become ubiquitous to virtually all socio-economic activities in business and industry. With the extent of AI's influence on our lives, it is an imperative to focus our attention on the ethics of AI. While humans develop their moral and ethical framework via self-awareness and reflection, the current generation of AI lacks these abilities. Drawing from the concept of human-AI hybrid, this chapter offers managerial and developers' action towards responsible machine learning for ethical artificial intelligence. The actions consist of privacy by design, development of explainable AI, identification and removal of inherent biases, and most importantly, using AI as a moral enabler. Application of these action would not only help towards ethical AI; it would also help in supporting moral development of human-AI hybrid.
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We replicated and extended Shoda, Mischel, and Peake’s (1990) famous “marshmallow” study, which showed strong bivariate correlations between a child’s ability to delay gratification just before entering school and both adolescent achievement and socioemotional behaviors. Concentrating on children whose mothers had not completed college, we found that an additional minute waited at age 4 predicted a gain of approximately 1/10th of a SD in age-15 achievement. But this bivariate correlation was only half the size of those reported in the original studies, and was reduced by two-thirds in the presence of controls for family background, early cognitive ability, and the home environment. Most of the variation in adolescent achievement came from being able to wait at least 20 seconds. Associations between delay time and age-15 measures of behavioral outcomes were much smaller and rarely statistically significant.
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Publisher Summary This chapter focuses on norms, which can be demonstrated to affect human action systematically and powerfully. Three distinct types of norms that are effective: social norms of the descriptive kind, which guides the behavior via the perception of how most others would behave; social norms of the injunctive kind, which guides the behavior via the perception of how most others would approve/disapprove of a person's conduct; and personal norms, which guides the behavior via the perception of how a person would approve/disapprove of his own conduct. At a given time, an individual's actions are likely to conform to the dictates of the type of norm that are familiar even when the other types of norms dictate contrary conduct. The chapter discusses those injunctive social norms—once activated—is likely to lead to beneficial social conduct across the greatest number of situations and populations. By focusing subjects on one or another type of norm, the action of a particular kind of norm was stimulated, without activating the other kinds.
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What follows is an exploration of innovative new ways to deliver privacy notice. Unlike traditional notice that relies upon text or symbols to convey information, emerging strategies of “visceral” notice leverage a consumer’s very experience of a product or service to warn or inform. A regulation might require that a cell phone camera make a shutter sound so people know their photo is being taken. Or a law could incentivize websites to be more formal (as opposed to casual) wherever they collect personal information, as formality tends to place people on greater guard about what they disclose. The thesis of this Article is that, for a variety of reasons, experience as a form of privacy disclosure is worthy of further study before we give in to calls to abandon notice as a regulatory strategy in privacy and elsewhere.
The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure
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