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α: calibrated so average coauthorship-adjusted count equals average raw count
We study human behavior in ultimatum game when interacting with either human or algorithmic opponents. We examine how the type of the AI algorithm (mimicking human behavior, optimising gains, or providing no explanation) and the presence of a human beneficiary affect sending and accepting behaviors. Our experimental data reveal that subjects generally do not differentiate between human and algorithmic opponents, between different algorithms, and between an explained and unexplained algorithm. However, they are more willing to forgo higher payoffs when the algorithm’s earnings benefit a human.