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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 the 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. Diffusing such individual pivotality for algorithmic choices increased the share of selfish decisions and weakened revealed prosocial preferences. This does not result from a change in the structure of incentives. Rather, our results show that Big Data offers an excuse for selfish behavior through lower responsibility for one’s and others’ fate.
We investigate the effect of the tone of news on investor stock price expectations and beliefs. In an experimental study we ask subjects to estimate a future stock price for twelve real listed companies. As additional information we provide them with historical stock prices and extracts from real newspaper articles. We propose a way to manipulate the tone of news extracts without distorting its content. Subjects in different treatment groups read news items that are written either in positive or negative tone for each stock. We find that subjects tend to predict a significantly higher (lower) return for stocks after reading positive (negative) tone news. The effect is especially pronounced for stocks with poor past performance. Subjects are more likely to be optimistic (pessimistic) about the economy and to buy (sell) stocks after reading positive (negative) than negative (positive) tone news. Our results show that the news media might affect not only how investors perceive information, but also what they do in response to it.