Artificial Intelligence, Ethics, and Diffused Pivotality

Abstract

With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the sources of training data and thus the impact of each individual's decisions on the algorithm. Reducing or diffusing pivotality for algorithmic choices increased the share of selfish decisions. Once the generated training data exclusively affected others' payoffs, individuals opted for more egalitarian payoff allocations. These results suggest that Big Data offers a "moral wiggle room" for selfish behavior.