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      Trust and Cooperation

      research-article
      * ,
      Frontiers in Robotics and AI
      Frontiers Media S.A.
      ethics, cooperation, trust, society, evolution, unknown unknowns, existential threat

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          Abstract

          We AI researchers are concerned about the potential impact of artificially intelligent systems on humanity. In the first half of this essay, I argue that ethics is an evolved body of cultural knowledge that (among other things) encourages individual behavior that promotes the welfare of the society (which in turn promotes the welfare of its individual members). The causal paths involved suggest that trust and cooperation play key roles in this process. In the second half of the essay, I consider whether the key role of trust exposes our society to existential threats. This possibility arises because decision-making agents (humans, AIs, and others) necessarily rely on simplified models to cope with the unbounded complexity of our physical and social world. By selecting actions to maximize a utility measure, a well-formulated game theory model can be a powerful and valuable tool. However, a poorly-formulated game theory model may be uniquely harmful, in cases where the action it recommends deliberately exploits the vulnerability and violates the trust of cooperative partners. Widespread use of such models can erode the overall levels of trust in the society. Cooperation is reduced, resources are constrained, and there is less ability to meet challenges or take advantage of opportunities. Loss of trust will affect humanity’s ability to respond to existential threats such as climate change.

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          Most cited references91

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          NOT SO DIFFERENT AFTER ALL: A CROSS-DISCIPLINE VIEW OF TRUST.

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            Dissecting racial bias in an algorithm used to manage the health of populations

            Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
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              The tragedy of the commons.

              (1968)
              The population problem has no technical solution; it requires a fundamental extension in morality.
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                Author and article information

                Contributors
                Journal
                Front Robot AI
                Front Robot AI
                Front. Robot. AI
                Frontiers in Robotics and AI
                Frontiers Media S.A.
                2296-9144
                29 April 2022
                2022
                : 9
                : 676767
                Affiliations
                Computer Science and Engineering , University of Michigan , Ann Arbor, MI, United States
                Author notes

                Edited by: Martim Brandão, King’s College London, United Kingdom

                Reviewed by: David Gunkel, Northern Illinois University, United States

                Saeed Hamood Alsamhi, Ibb University, Yemen

                *Correspondence: Benjamin Kuipers, kuipers@ 123456umich.edu

                This article was submitted to Ethics in Robotics and Artificial Intelligence, a section of the journal Frontiers in Robotics and AI

                Article
                676767
                10.3389/frobt.2022.676767
                9100567
                35572370
                99b81924-46e2-42a2-80a5-868c8978baf4
                Copyright © 2022 Kuipers.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 06 March 2021
                : 21 February 2022
                Categories
                Robotics and AI
                Original Research

                ethics,cooperation,trust,society,evolution,unknown unknowns,existential threat

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