Actual causality is increasingly well understood. Recent formal approaches, proposed by Halpern and Pearl, have made this concept mature enough to be amenable to automated reasoning. Actual causality is especially vital for building accountable, explainable systems. Among other reasons, causality reasoning is computationally hard due to the requirements of counterfactuality and the minimality of causes. Previous approaches presented either inefficient or restricted, and domain-specific, solutions to the problem of automating causality reasoning. In this paper, we present a novel approach to formulate different notions of causal reasoning, over binary acyclic models, as optimization problems, based on quantifiable notions within counterfactual computations. We contribute and compare two compact, non-trivial, and sound integer linear programming (ILP) and Maximum Satisfiability (MaxSAT) encodings to check causality. Given a candidate cause, both approaches identify what a minimal cause is. Also, we present an ILP encoding to infer causality without requiring a candidate cause. We show that both notions are efficiently automated. Using models with more than $8000$ variables, checking is computed in a matter of seconds, with MaxSAT outperforming ILP in many cases. In contrast, inference is computed in a matter of minutes.
Recent formal approaches towards causality have made the concept ready for incorporation into the technical world. However, causality reasoning is computationally hard; and no general algorithmic approach exists that efficiently infers the causes for effects. Thus, checking causality in the context of complex, multi-agent, and distributed socio-technical systems is a significant challenge. Therefore, we conceptualize an intelligent and novel algorithmic approach towards checking causality in acyclic causal models with binary variables, utilizing the optimization power in the solvers of the Boo…
Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
Actual causality and a closely related concept of responsibility attribution are central to accountable decision making. Actual causality focuses on specific outcomes and aims to identify decisions (actions) that were critical in realizing an outcome of interest. Responsibility attribution is complementary and aims to identify the extent to which decision makers (agents) are responsible for this outcome. In this paper, we study these concepts under a widely used framework for multi-agent sequential decision making under uncertainty: decentralized partially observable Markov decision processes …
Action Languages Based Actual Causality for Computational Ethics: a Sound and Complete Implementation in ASP
Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；National Institute of Informatics（国立信息学研究所）；École Normale Supérieure（巴黎高等师范学院）；Université PSL（巴黎文理研究大学）；Collège de France（法兰西公学院）
University of Tübingen（图宾根大学）；LMU（慕尼黑大学）；causaLens；King’s College London（伦敦国王学院）；Cornell University（康奈尔大学）
As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework to address 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 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 the…
State Key Laboratory for Novel Software Technology \& School of Artificial Intelligence, Nanjing University, China School of Informatics, The University of Edinburgh, UK
Max Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
Responsibility attribution is a key concept of accountable multi-agent decision making. Given a sequence of actions, responsibility attribution mechanisms quantify the impact of each participating agent to the final outcome. One such popular mechanism is based on actual causality, and it assigns (causal) responsibility based on the actions that were found to be pivotal for the considered outcome. However, the inherent problem of pinpointing actual causes and consequently determining the exact responsibility assignment has shown to be computationally intractable. In this paper, we aim to provid…
Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Full Fact（Full Fact事实核查机构）；Qatar University（卡塔尔大学）；Università di Bologna（博洛尼亚大学）；EURECOM（欧洲通信学院）；University of Padova（帕多瓦大学）
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Nowadays, politicians and other key players enjoy direct access to their audiences through social media, bypassing the filters of official cables or traditional media. However, the multiple advantages of free speech and direct communication are dimmed by the misuse of media to spread inaccurate or misleading claims. These phenomena have led to the modern incarnation of the fact-checker -- a professional whose main aim is to examine claims u…
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
Toyota Research Institute of North America（丰田美国研究院）；Toyota Motor North America Frontier Research Center（丰田北美前沿研究中心）；Toyota Motor Corporation（丰田公司）；Toyota Motor North America（丰田北美）
A Computationally Feasible Framework for Causal Probabilistic Explanation
Basis Research Institute（贝西斯研究所）；Sorbus AI（索巴斯人工智能公司）；Massachusetts Institute of Technology（麻省理工学院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；HouseIQ（豪斯IQ公司）
Alexandru Ioan Cuza University of Iaşi（雅西亚历山德鲁·伊万·库扎大学）
Artificial Intelligence problems, ranging form planning/scheduling up to game control, include an essential crucial step: describing a model which accurately defines the problem's required data, requirements, allowed transitions and established goals. The ways in which a model can fail are numerous and often lead to a failure of search strategies to provide a quick, optimal, or even any solution. This paper proposes using SMT (Satisfiability Modulo Theories) solvers, such as Z3, to check the validity of a model. We propose two tests: checking whether a final(goal) state exists in the model's d…
TU Darmstadt（达姆施塔特工业大学）；Hessian Center for AI (hessian.AI)（黑森人工智能中心）；DFKI（德国人工智能研究中心）
Neurally-parameterized Structural Causal Models in the Pearlian notion to causality, referred to as NCM, were recently introduced as a step towards next-generation learning systems. However, said NCM are only concerned with the learning aspect of causal inference but totally miss out on the architecture aspect. That is, actual causal inference within NCM is intractable in that the NCM won't return an answer to a query in polynomial time. This insight follows as corollary to the more general statement on the intractability of arbitrary SCM parameterizations, which we prove in this work through …
