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1. A Causal Analysis of Harm

   https://doi.org/10.1007/s11023-024-09689-7  

   Beckers, Sander ; Chockler, Hana ; Halpern, Joseph_Y ( July 2024 , Minds and Machines)

   As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework that addresses when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and “replaced by more well-behaved notions”. As harm is generally something that is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper, which is an expanded version of the conference paper Beckers et al. (Adv Neural Inform Process Syst 35:2365–2376, 2022), we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality. The key features of our definition are that it is based oncontrastivecausation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems.

    

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2. Towards formal definitions of blameworthiness, intention, and moral responsibility

   Halpern, Joseph Y. ; Kleiman-Wiener, Max ( February 2018 , Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18))

   We provide formal definitions of \emph{degree of blameworthiness} and \emph{intention} relative to an \emph{epistemic state} (a probability over causal models and a utility function on outcomes). These, together with a definition of actual causality, provide the key ingredients for moral responsibility judgments. We show that these definitions give insight into commonsense intuitions in a variety of puzzling cases from the literature. 

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3. Parental Perspectives About What It Means to Bully

   https://doi.org/10.1177/0192513X221129865  

   Stives, Kristen L. ; May, David C. ; Bethel, Cindy L. ( September 2022 , Journal of Family Issues)

   While most bullying researchers argue that any definition of bullying must include three core elements (power imbalance, intent to cause harm, and repetitive negative actions), relatively few researchers have examined whether parents define bullying along these three elements. Among those that have, most find that parents focus on the intent to cause harm and ignore or discount the power imbalance and repetition elements. Using qualitative data from 50 parents in a southeastern state, we explore parental definitions of bullying and trace how their definitions match those three elements. We find that most parents include intent to cause harm in their definition but far fewer mention the power imbalance or repetition commonly found in scholarly definitions. Additionally, we uncover a fourth component that was important for several parents: the fact that bullies engage in that activity to build their own self-esteem. Implications for policy and research are discussed.

    

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4. Causal Conceptions of Fairness and their Consequences

   Nilforoshan, Hamed ; Gaebler, Johann D. ; Shroff, Ravi ; Goel, Sharad ( January 2022 , Proceedings of the 39th International Conference on Machine Learning)

   Chaudhuri, Kamalika ; Jegelka, Stefanie ; Song, Le ; Szepesvari, Csaba ; Niu, Gang ; Sabato, Sivan (Ed.)

   Recent work highlights the role of causality in designing equitable decision-making algorithms. It is not immediately clear, however, how existing causal conceptions of fairness relate to one another, or what the consequences are of using these definitions as design principles. Here, we first assemble and categorize popular causal definitions of algorithmic fairness into two broad families: (1) those that constrain the effects of decisions on counterfactual disparities; and (2) those that constrain the effects of legally protected characteristics, like race and gender, on decisions. We then show, analytically and empirically, that both families of definitions almost always—in a measure theoretic sense—result in strongly Pareto dominated decision policies, meaning there is an alternative, unconstrained policy favored by every stakeholder with preferences drawn from a large, natural class. For example, in the case of college admissions decisions, policies constrained to satisfy causal fairness definitions would be disfavored by every stakeholder with neutral or positive preferences for both academic preparedness and diversity. Indeed, under a prominent definition of causal fairness, we prove the resulting policies require admitting all students with the same probability, regardless of academic qualifications or group membership. Our results highlight formal limitations and potential adverse consequences of common mathematical notions of causal fairness. 

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5. Deciding accuracy of differential privacy schemes

   https://doi.org/10.1145/3434289  

   Barthe, Gilles ; Chadha, Rohit ; Krogmeier, Paul ; Sistla, A. Prasad ; Viswanathan, Mahesh ( January 2021 , Proceedings of the ACM on Programming Languages)

   null (Ed.)

   Differential privacy is a mathematical framework for developing statistical computations with provable guarantees of privacy and accuracy. In contrast to the privacy component of differential privacy, which has a clear mathematical and intuitive meaning, the accuracy component of differential privacy does not have a generally accepted definition; accuracy claims of differential privacy algorithms vary from algorithm to algorithm and are not instantiations of a general definition. We identify program discontinuity as a common theme in existing ad hoc definitions and introduce an alternative notion of accuracy parametrized by, what we call, — the of an input x w.r.t.  a deterministic computation f and a distance d , is the minimal distance d ( x , y ) over all y such that f ( y )≠ f ( x ). We show that our notion of accuracy subsumes the definition used in theoretical computer science, and captures known accuracy claims for differential privacy algorithms. In fact, our general notion of accuracy helps us prove better claims in some cases. Next, we study the decidability of accuracy. We first show that accuracy is in general undecidable. Then, we define a non-trivial class of probabilistic computations for which accuracy is decidable (unconditionally, or assuming Schanuel’s conjecture). We implement our decision procedure and experimentally evaluate the effectiveness of our approach for generating proofs or counterexamples of accuracy for common algorithms from the literature. 

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