Proponents of corporate moral responsibility take certain corporations to be capable of being responsible in ways that do not reduce to the responsibility of their members. If correct, one follow-up question concerns what leads corporations to fail to meet their obligations. We often fail morally when we know what we should do and yet fail to do it, perhaps out of incontinence, akrasia, or weakness of will. However, this kind of failure is much less discussed in the corporate case. And, (...) where it is discussed, the view is that corporations are less prone to weakness. Here, I argue that proponents of corporate responsibility should say that corporations can and often do instantiate weakness of the will, and that this is important to recognize. Weakness of the will requires certain capacities that these proponents typically take corporations to have. And once this is appreciated, we can assess how corporate weakness might proceed differently than how it does for individuals. We can also begin a conversation about how best to meet the distinctive challenges for recognizing and correcting corporate weakness, using a number of resources from management scholarship. (shrink)

Vicarious responsibility exists. It is (also) a species of moral responsibility. Nevertheless, it is seldomly discussed or considered as such in the main debates within moral philosophy. This article presents a case for the relevance of vicarious moral responsibility to several of these discussions. It seeks to provide new insights for the debate between historicism and structuralism and presents a further case for distinguishing between various facets of moral responsibility. Furthermore, the article demonstrates that an agent need not enter into (...) the relation of vicarious responsibility freely in order to be responsible. This idea then allows us to present new cases for compatibilism, demonstrating the consistency of moral responsibility with determinism. (shrink)

There is a widely accepted distinction between being directly responsible for a wrongdoing versus being somehow indirectly or vicariously responsible for the wrongdoing of another person or collective. Often this is couched in analyses of complicity, and complicity’s role in the relationship between individual and collective wrongdoing. Complicity is important because, inter alia, it allows us to make sense of individuals who may be blameless or blameworthy to a relatively low degree for their immediate conduct, but are nevertheless blameworthy to (...) a higher degree for their implication in some larger (or another person’s) wrongdoing. In this paper, I argue that there is a distinctively epistemic kind of complicity. First, I motivate the distinction between direct and vicarious responsibility with three interlocking arguments, respectively appealing to: i) the structure of rational agency; ii) linguistic considerations; iii) the role of “principal vs. accomplice” in legal doctrine. I show how these arguments naturally extend to the epistemic domain, motivating an epistemic form of vicarious responsibility. I then examine complicity as a mechanism of vicarious epistemic responsibility. To fill this out, I engage with an epistemic analogue of the debate about the role of intention versus causal contribution in complicity. I propose a Casual Account of Epistemic Complicity, arguing that it accommodates a wide range of cases, and enables fine-grained explanations of degrees of culpability for epistemic complicity. With an adequate account of epistemic complicity on hand, we can explain what is objectionable about an important class of epistemic agent who, on an individual level, may be epistemically blameless or blameworthy to a relatively low degree, but whose relation to other individuals or collectives nevertheless makes them epistemically blameworthy to a higher degree. I explore some broader implications of this result. (shrink)

Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites of application, this paper (...) posits that reconceptualizing replicability can help bridge the gap. Through a shift from model performance replicability to claim replicability, Machine Learning scientists can be held accountable for producing non-replicable claims that are prone to eliciting harm due to misuse and misinterpretation. In this paper, I make the following contributions. First, I define and distinguish two forms of replicability for ML research that can aid constructive conversations around replicability. Second, I formulate an argument for claim-replicability's advantage over model performance replicability in justifying assigning accountability to Machine Learning scientists for producing non-replicable claims and show how it enacts a sense of responsibility that is actionable. In addition, I characterize the implementation of claim replicability as more of a social project than a technical one by discussing its competing epistemological principles, practical implications on Circulating Reference, Interpretative Labor, and research communication. (shrink)

When a computer system causes harm, who is responsible? This question has renewed significance given the proliferation of autonomous systems enabled by modern artificial intelligence techniques. At the root of this problem is a philosophical difficulty known in the literature as the responsibility gap. That is to say, because of the causal distance between the designers of autonomous systems and the eventual outcomes of those systems, the dilution of agency within the large and complex teams that design autonomous systems, and (...) the impossibility of fully predicting how autonomous systems will behave once deployed, determining who is morally responsible for harms caused by autonomous systems is unclear at a conceptual level. I review past work on this topic, criticizing prior works for suggesting workarounds rather than philosophical answers to the conceptual problem presented by the responsibility gap. The view I develop, drawing on my earlier work on vicarious moral responsibility, explains why computing professionals are ethically required to take responsibility for the systems they design, despite not being blameworthy for the harms these systems may cause. (shrink)