Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive a (...) systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges. (shrink)

There is ample evidence that violations of injunctive norms impact ordinary causal attributions. This has struck some as deeply surprising, taking the ordinary concept of causation to be purely descriptive. Our explanation of the findings—the responsibility view—rejects this: we contend that the concept is in fact partly normative, being akin to concepts like responsibility and accountability. Based on this account, we predicted a very different pattern of results for causal attributions when an agent violates a statistical norm. And this pattern (...) has been borne out by the data. These predictions were based on the responsibility attributions that we would make. In this paper, I extend these previous findings, testing responsibility attributions. The results confirm the basis of our predictions, showing the same pattern of effects previously found for causal attributions for both injunctive norms and statistical norms. In fact, the results for responsibility attributions are not statistically significantly different from those previously found for causal attributions. I argue that this close correspondence lends further credence to the responsibility view over competing explanations of the impact of norms on causal attributions. (shrink)

When comparing the roles of the lightning strike and the dry climate in causing the forest fire, one might think that the lightning strike is more of a cause than the dry climate, or one might think that the lightning strike completely caused the fire while the dry conditions did not cause it at all. Psychologists and philosophers have long debated whether such causal judgments are graded; that is, whether people treat some causes as stronger than others. To address this (...) debate, we first reanalyzed data from four recent studies. We found that causal judgments were actually multimodal: although most causal judgments made on a continuous scale were categorical, there was also some gradation. We then tested two competing explanations for this gradation: the confidence explanation, which states that people make graded causal judgments because they have varying degrees of belief in causal relations, and the strength explanation, which states that people make graded causal judgments because they believe that causation itself is graded. Experiment 1 tested the confidence explanation and showed that gradation in causal judgments was indeed moderated by confidence: people tended to make graded causal judgments when they were unconfident, but they tended to make more categorical causal judgments when they were confident. Experiment 2 tested the causal strength explanation and showed that although confidence still explained variation in causal judgments, it did not explain away the effects of normality, causal structure, or the number of candidate causes. Overall, we found that causal judgments were multimodal and that people make graded judgments both when they think a cause is weak and when they are uncertain about its causal role. (shrink)

The Culpable Control Model assumes that causal judgments are irrational: a negative evaluative reaction to an agent would lead individuals to overestimate his causal contribution to a harm. However, the extent to which these judgments deviate from criteria of rationality remains unclear. The two present studies aimed at investigating conditions under which this effect occurs. Participants red a vignette in which the evaluative reaction was operationalized through the agent’s motives (blameworthy, laudable). We also varied the causal link between the agent’s (...) action and the outcome (strong, weak, preempted). In both studies, we found that negative motives influenced causal judgments when a strong action-outcome link could be established. In Study 2, we replicated the effect of motives on causal judgment in the weak link condition. However, participants consistently judged the agent as less causal when the link was weak (vs. strong), regardless of the agent’s motives. These findings show that both the presence and the strength of the actual causal link between the action and the outcome moderate the detrimental effect of negative evaluative reactions on causal judgments. Thus, common sense causal judgments seem to deviate relatively little from the criteria of rationality. (shrink)