Employing AI to Better Understand Our Morals

Lúis Moniz Pereira, The Anh Han, António Barata Lopes

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Abstract

We present a summary of research that we have conducted employing AI to better understand human morality. This summary adumbrates theoretical fundamentals and considers how to regulate development of powerful new AI technologies. The latter research aim is benevolent AI, with fair distribution of benefits associated with the development of these and related technologies, avoiding disparities of power and wealth due to unregulated competition. Our approach avoids statistical models employed in other approaches to solve moral dilemmas, because these are “blind” to natural constraints on moral agents, and risk perpetuating mistakes. Instead, our approach employs, for instance, psychologically realistic counterfactual reasoning in group dynamics. The present paper reviews studies involving factors fundamental to human moral motivation, including egoism vs. altruism, commitment vs. defaulting, guilt vs. non-guilt, apology plus forgiveness, counterfactual collaboration, among other factors fundamental in the motivation of moral action. These being basic elements in most moral systems, our studies deliver generalizable conclusions that inform efforts to achieve greater sustainability and global benefit, regardless of cultural specificities in constituents.
Original languageEnglish
Article number10
JournalEntropy
Volume24
Issue number1
DOIs
Publication statusPublished - 21 Dec 2021

Bibliographical note

Funding Information:
Funding: L.M.P. and T.A.H. acknowledge support from Future of Life Institute grant 372 RFP2-154, and L.M.P. L.M.P is also supported by NOVA LINCS (UIDB/04516/2020) with the financial support of FCT—“Fundação para a Ciência e a Tecnologia”, Portugal, through national funds. T.A.H. also acknowledges support from Berkeley Existential Risk Initiative (BERI) collaboration fund, and support by a Leverhulme Research Fellowship (RF-2020-603/9). A.B.L. acknowledges the support of ANQEP—“Agência Nacional para a Qualificação e Ensino Profissional”.

Funding Information:
L.M.P. and T.A.H. acknowledge support from Future of Life Institute grant 372 RFP2-154, and L.M.P. L.M.P is also supported by NOVA LINCS (UIDB/04516/2020) with the financial support of FCT??Funda??o para a Ci?ncia e a Tecnologia?, Portugal, through national funds. T.A.H. also acknowledges support from Berkeley Existential Risk Initiative (BERI) collaboration fund, and support by a Leverhulme Research Fellowship (RF-2020-603/9). A.B.L. acknowledges the support of ANQEP??Ag?ncia Nacional para a Qualifica??o e Ensino Profissional?.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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