Publicação
Employing AI to Better Understand Our Morals
| Resumo: | 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. |
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| Autores principais: | Pereira, Luís Moniz |
| Outros Autores: | Han, The Anh; Lopes, António Barata |
| Assunto: | AI governance Artificial intelligence Evolutionary game theory Human morality Machine ethics Information Systems Mathematical Physics Physics and Astronomy (miscellaneous) Electrical and Electronic Engineering |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | 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. |
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