Artificial intelligence, ethics, and intergenerational responsibility

Arbeitspapier

In more and more situations, artificially intelligent algorithms have to model humans' (social) preferences on whose behalf they increasingly make decisions. They can learn these preferences through the repeated observation of human behavior in social encounters. In such a context, do individuals adjust the selfishness or prosociality of their behavior when it is common knowledge that their actions produce various externalities through the training of an algorithm? In an online experiment, we let participants' choices in dictator games train an algorithm. Thereby, they create an externality on future decision making of an intelligent system that affects future participants. We show that individuals who are aware of the consequences of their training on the payoffs of a future generation behave more prosocially, but only when they bear the risk of being harmed themselves by future algorithmic choices. In that case, the externality of artificially intelligence training induces a significantly higher share of egalitarian decisions in the present.

Show object

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SAFE Working Paper ; No. 335

Classification and Topics

• Classification: Wirtschaft ; Econometric and Statistical Methods: Special Topics: Other ; Design of Experiments: Laboratory, Individual ; Household Behavior: General ; Externalities ; Equity, Justice, Inequality, and Other Normative Criteria and Measurement ; Technological Change: Choices and Consequences; Diffusion Processes
• Subject: Artificial Intelligence ; Morality ; Prosociality ; Generations ; Externalities

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Klockmann, Victor ; von Schenk, Alicia ; Villeval, Marie-Claire
• Event: Veröffentlichung
• (who): Leibniz Institute for Financial Research SAFE
• (where): Frankfurt a. M.
• (when): 2022

Further information

• DOI: doi:10.2139/ssrn.4002578
• Handle: http://hdl.handle.net/10419/249178