We present what we call the Interpretation Problem, whereby any rule in symbolic form is open to infinite interpretation in ways that we might disapprove of and argue that any attempt to build morality into machines is subject to it. We show how the Interpretation Problem in Artificial Intelligence is an illustration of Wittgenstein's general claim that no rule can contain the criteria for its own application, and that the risks created by this problem escalate in proportion to the degree to which to machine is causally connected to the world, in what we call the Law of Interpretative Exposure. Using game theory, we attempt to define the structure of normative spaces and argue that any rule-following within a normative space is guided by values that are external to that space and which cannot themselves be represented as rules. In light of this, we categorise the types of mistakes an artificial moral agent could make into Mistakes of Intention and Instrumental Mistakes, and we propose ways of building morality into machines by getting them to interpret the rules we give in accordance with these external values, through explicit moral reasoning, the Show, not Tell paradigm, the adjustment of causal power and structure of the agent, and relational values, with the ultimate aim that the machine develop a virtuous character and that the impact of the Interpretation Problem is minimised.
Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians.…
Indian Institute of Technology, Ropar（印度理工学院罗帕尔分校）；CSIR-Central Scientific Instruments Organisation（CSIR中央科学仪器组织）；Academy of Scientific and Innovative Research（科学与创新研究院）；Tokai Gakuen University（东海学园大学）；Tokyo Institute of Technology（东京工业大学）
Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ing…
Whose Morality Do They Speak? Unraveling Cultural Bias in Multilingual Language Models
Research Center Trustworthy Data Science and Security of the University Alliance Ruhr（鲁尔大学联盟可信数据科学与安全研究中心）；Faculty of Informatics, Technical University Dortmund（多特蒙德工业大学信息学院）
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）
We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete ability to…
Simon Fraser University（西蒙菲莎大学）；New York University（纽约大学）；University of Cambridge（剑桥大学）；ETH Zürich（苏黎世联邦理工学院）；Facebook AI Research（Facebook人工智能研究院）
Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to wrangle with how learning systems that interact with humans should be constrained to behave ethically. One proposal in this vein is the construction of morality models that can take in arbitrary text and output a moral judgment about the situation described. In this work, we focus on a single case study of the recently proposed Delphi model and offer a critique of the project's proposed method of automating morality judgments. Through an audit of Delphi, we exam…
An ambitious goal for machine learning is to create agents that behave ethically: The capacity to abide by human moral norms would greatly expand the context in which autonomous agents could be practically and safely deployed, e.g. fully autonomous vehicles will encounter charged moral decisions that complicate their deployment. While ethical agents could be trained by rewarding correct behavior under a specific moral theory (e.g. utilitarianism), there remains widespread disagreement about the nature of morality. Acknowledging such disagreement, recent work in moral philosophy proposes that e…
Charles Sturt University（查尔斯特大学）；University in Hartford（哈特福德大学）；University of Connecticut（康涅狄格大学）；Ohio Northern University（俄亥俄北方大学）
The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists a…
Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative ethics through the lens of computational complexity. First, we introduce computational complexity for the…
Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning
Paul G. Allen School of Computer Science and Engineering, University of Washington（保罗·G·阿伦计算机科学与工程学院，华盛顿大学）；Institute for Learning and Brain Sciences, University of Washington（学习与脑科学研究所，华盛顿大学）
This paper concerns the ethics and morality of algorithms and computational systems, and has been circulating internally at Facebook for the past couple years. The paper reviews many Nobel laureates' work, as well as the work of other prominent scientists such as Richard Dawkins, Andrei Kolmogorov, Vilfredo Pareto, and John von Neumann. The paper draws conclusions based on such works, as summarized in the title. The paper argues that the standard approach to modern machine learning and artificial intelligence is bound to be biased and unfair, and that longstanding traditions in the professions…
