Distributing Blame Among Multiple Entities When Autonomous Technologies Cause Harm.

As autonomous technology emerges, new variations in old questions arise. When autonomous technologies cause harm, who is to blame? The current studies compare reactions toward harms caused by human-controlled vehicles (HCVs) or human soldiers (HSs) to identical harms by autonomous vehicles (AVs) or autonomous robot soldiers. Drivers of HCVs, or HSs, were blamed more than mere users of AVs or HSs who outsourced their duties to ARSs. However, as human drivers/soldiers became less involved in (or were unaware of the preprogramming that led to) the harm, blame was redirected toward other entities (i.e., manufacturers and the tech company's executives), showing the opposite pattern as human drivers/soldiers. Results were robust to how blame was measured (i.e., degrees of blame versus apportionment of total blame). Overall, this research furthers the blame literature, raising questions about why, how (much), and to whom blame is assigned when multiple agents are potentially culpable.

Does the "surprisingly popular" method yield accurate crowdsourced predictions?

The "surprisingly popular" method (SP) of aggregating individual judgments has shown promise in overcoming a weakness of other crowdsourcing methods-situations in which the majority is incorrect. This method relies on participants' estimates of other participants' judgments; when an option is chosen more often than the average metacognitive judgments of that option, it is "surprisingly popular" and is selected by the method. Although SP has been shown to improve group decision making about factual propositions (e.g., state capitals), its application to future outcomes has been limited. In three preregistered studies, we compared SP to other methods of aggregating individual predictions about future events. Study 1 examined predictions of football games, Study 2 examined predictions of the 2018 US midterm elections, and Study 3 examined predictions of basketball games. When applied to judgments made by objectively assessed experts, SP performed slightly better than other aggregation methods. Although there is still more to learn about the conditions under which SP is effective, it shows promise as a means of crowdsourcing predictions of future outcomes.