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Given the ubiquity of artificial intelligence (AI) in modern societies, it is clear that individuals, corporations, and countries will be grappling with the legal and ethical issues of its use. As global problems require global solutions, we propose the establishment of an international AI regulatory agency that — drawing on interdisciplinary expertise — could create a unified framework for the regulation of AI technologies and inform the development of AI policies around the world. We urge that such an organization be developed with all deliberate haste, as issues such as cryptocurrencies, personalized political ad hacking, autonomous vehicles and autonomous weaponized agents, are already a reality, affecting international trade, politics, and war. 

Computer science faculty have a responsibility to teach students to recognize both the larger ethical issues and particular responsibilities that are part and parcel of their work as technologists. This is, however, a kind of teaching for which most of us have not been trained, and one which faculty and students approach with some trepidation. In this article we explore the use of science fiction as an effective tool to enable those teaching AI to engage students and practitioners about the scope and implications of current and future work in computer science. 

We draw on concepts in medical ethics to consider how computer science, and AI in particular, can develop critical tools for thinking concretely about technology's impact on the wellbeing of the people who use it. We focus on patient autonomy---the ability to set the terms of one’s encounter with medicine---and on the mediating concepts of informed consent and decisional capacity, which enable doctors to honor patients' autonomy in messy and non-ideal circumstances. This comparative study is organized around a fictional case study of a heart patient with cardiac implants. Using this case study, we identify points of overlap and of difference between medical ethics and technology ethics, and leverage a discussion of that intertwined scenario to offer initial practical suggestions about how we can adapt the concepts of decisional capacity and informed consent to the discussion of technology design. 

We argue that it is crucial to the future of AI that our students be trained in multiple complementary modes of ethical rea- soning, so that they may make ethical design and implemen- tation choices, ethical career decisions, and that their software will be programmed to take into account the complexities of acting ethically in the world. 

In "The Logic of Campaigning", Dean and Parikh consider a candidate making campaign statements to appeal to the voters. They model these statements as Boolean formulas over variables that repre- sent stances on the issues, and study optimal candidate strategies under three proposed models of voter preferences based on the assignments that satisfy these formulas. We prove that voter utility evaluation is computationally hard under these preference models (in one case, #P-hard), along with certain problems related to candidate strategic reasoning. Our results raise questions about the desirable characteristics of a voter preference model and to what extent a polynomial-time-evaluable function can capture them. 

Conditional preference networks (CP-nets) are an intuitive and expressive representation for qualitative preferences. Such models must somehow be acquired. Psychologists argue that direct elicitation is suspect. On the other hand, learning general CP-nets from pairwise comparisons is NP-hard, and --- for some notions of learning --- this extends even to the simplest forms of CP-nets. We introduce a novel, concise encoding of binary-valued, tree-structured CP-nets that supports the first local-search-based CP-net learning algorithms. While exact learning of binary-valued, tree-structured CP-nets --- for a strict, entailment-based notion of learning --- is already in P, our algorithm is the first space-efficient learning algorithm that gracefully handles noisy (i.e., realistic) comparison sets. 

The generation of preferences represented as CP-nets for experiments and empirical testing has typically been done in an ad hoc manner that may have introduced a large statistical bias in previous experimental work. We present novel polynomial-time algorithms for generating CP-nets with n nodes and maximum in-degree c uniformly at random. We extend this result to several statistical cultures commonly used in the social choice and preference reasoning literature. A CP-net is composed of both a graph and underlying cp-statements; our algorithm is the first to provably generate both the graph structure and cp-statements, and hence the underlying preference orders themselves, uniformly at random. We have released this code as a free and open source project. We use the uniform generation algorithm to investigate the maximum and expected flipping lengths, i.e., the maximum length over all outcomes o1 and o2, of a minimal proof that o1 is preferred to o2. Using our new statistical evidence, we conjecture that, for CP-nets with binary variables and complete conditional preference tables, the expected flipping length is polynomial in the number of preference variables. This has positive implications for the usability of CP-nets as compact preference models. 

The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course. 

The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course.