Scoring AI-generated policy recommendations with Risk-Adjusted Gain in Net Present Happiness

Abstract Ethical considerations for assessing the collective benefit of an AI’s policy recommendations are different from assessing the ethical consequences from interacting with an individual. The study of population ethics provides a framework for studying collective benefit or harm in abstract terms. Research into happiness has made significant strides in identifying some key drivers of subjective well-being as measured both individually and collectively across societies. This research examines models from population ethics and statistical studies of subjective well-being to create a measure of benefit with which to judge AI recommendations. These models include refining estimations of the interaction between cultural aspects and economic development and incorporating measures of inequality of happiness and satisfaction through a society. When the impacts of a proposed policy are simulated for multiple successive years, risk discounting is used to measure Net Present Happiness, thus solving the conundrum of considering future generations in ethical considerations as posed in population ethics. Lastly, the Risk-Adjusted Gain in Net Present Happiness is proposed as a reasonable approach to ranking AI policy recommendations and as an AI objective function.