If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
If we want AI systems to make decisions, or to support humans in making them, we need to make sure they are aware of the ethical principles that are involved in such decisions, so they can guide towards decisions that are conform to the ethical principles. Complex decisions that we make on a daily basis are based on our own subjective preferences over the possible options. In this respect, the CP-net formalism is a convenient and expressive way to model preferences over decisions with multiple features. However, often the subjective preferences of the decision makers may need to be checked against exogenous priorities such as those provided by ethical principles, feasibility constraints, or safety regulations. Hence, it is essential to have principled ways to evaluate if preferences are compatible with such priorities. To do this, we describe also such priorities via CP-nets and we define a notion of distance between the ordering induced by two CPnets. We also provide tractable approximation algorithms for computing the distance and we define a procedure that uses the distance to check if the preferences are close enough to the ethical principles. We then provide an experimental evaluation showing that the quality of the decision with respect to the subjective preferences does not significantly degrade when conforming to the ethical principles.
Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.
Burton, Emanuelle (University of Kentucky) | Goldsmith, Judy (University of Kentucky) | Koenig, Sven (University of Southern California) | Kuipers, Benjamin (University of Michigan) | Mattei, Nicholas (IBM Research) | Walsh, Toby (University of New South Wales and Data61)
Aziz, Haris (Data61 and University of New South Wales) | Lev, Omer (University of Toronto) | Mattei, Nicholas (Data61 and University of New South Wales) | Rosenschein, Jeffrey S. (The Hebrew University of Jerusalem) | Walsh, Toby (Data61 and University of New South Wales)
We study an important crowdsourcing setting where agents evaluate one another and, based on these evaluations, a subset of agents are selected. This setting is ubiquitous when peer review is used for distributing awards in a team, allocating funding to scientists, and selecting publications for conferences. The fundamental challenge when applying crowdsourcing in these settings is that agents may misreport their reviews of others to increase their chances of being selected. We propose a new strategyproof (impartial) mechanism called Dollar Partition that satisfies desirable axiomatic properties. We then show, using a detailed experiment with parameter values derived from target real world domains, that our mechanism performs better on average, and in the worst case, than other strategyproof mechanisms in the literature.
Allen, Thomas E. (University of Kentucky) | Goldsmith, Judy (University of Kentucky) | Justice, Hayden Elizabeth (The Gatton Academy, WKU) | Mattei, Nicholas (Data61 and University of New South Wales) | Raines, Kayla (University of Kentucky)
Conditional preference networks (CP-nets) are a commonly studied compact formalism for modeling preferences. To study the properties of CP-nets or the performance of CP-net algorithms on average, one needs to generate CP-nets in an equiprobable manner. We discuss common problems with naive generation, including sampling bias, which invalidates the base assumptions of many statistical tests and can undermine the results of an experimental study. We provide a novel algorithm for provably generating acyclic CP-nets uniformly at random. Our method is computationally efficient and allows for multi-valued domains and arbitrary bounds on the indegree in the dependency graph.
A key front for ethical questions in artificial intelligence, and computer science more generally, is teaching students how to engage with the questions they will face in their professional careers based on the tools and technologies we teach them. In past work (and current teaching) we have advocated for the use of science fiction as an appropriate tool which enables AI researchers to engage students and the public on the current state and potential impacts of AI. We present teaching suggestions for E.M. Forster's 1909 story, "The Machine Stops," to teach topics in computer ethics. In particular, we use the story to examine ethical issues related to being constantly available for remote contact, physically isolated, and dependent on a machine --- all without mentioning computer games or other media to which students have strong emotional associations. We give a high-level view of common ethical theories and indicate how they inform the questions raised by the story and afford a structure for thinking about how to address them.
Aziz, Haris (NICTA and University of New South Wales) | Gaspers, Serge (NICTA and University of New South Wales) | Mackenzie, Simon (NICTA and University of New South Wales) | Mattei, Nicholas (NICTA and University of New South Wales) | Narodytska, Nina (Carnegie Mellon University) | Walsh, Toby (NICTA and University of New South Wales)
The probabilistic serial (PS) rule is a prominent randomized rule for assigning indivisible goods to agents. Although it is well known for its good fairness and welfare properties, it is not strategyproof. In view of this, we address several fundamental questions regarding equilibria under PS. Firstly, we show that Nash deviations under the PS rule can cycle. Despite the possibilities of cycles, we prove that a pure Nash equilibrium is guaranteed to exist under the PS rule. We then show that verifying whether a given profile is a pure Nash equilibrium is coNP-complete, and computing a pure Nash equilibrium is NP-hard. For two agents, we present a linear-time algorithm to compute a pure Nash equilibrium which yields the same assignment as the truthful profile. Finally, we conduct experiments to evaluate the quality of the equilibria that exist under the PS rule, finding that the vast majority of pure Nash equilibria yield social welfare that is at least that of the truthful profile.
The cultural and political implications of modern AI research are not some far off concern, they are things that affect the world in the here and now. From advanced control systems with advanced visualizations and image processing techniques that drive the machines of the modern military to the slow creep of a mechanized workforce, ethical questions surround us. Part of dealing with these ethical questions is not just speculating on what could be but teaching our students how to engage with these ethical questions. We explore the use of science fiction as an appropriate tool to enable AI researchers to help engage students and the public on the current state and potential impacts of AI.
Aziz, Haris (NICTA and University of New South Wales) | Gaspers, Serge (NICTA and University of New South Wales) | Gudmundsson, Joachim (University of Sydney) | Mackenzie, Simon (NICTA and University of New South Wales) | Mattei, Nicholas (NICTA and University of New South Wales) | Walsh, Toby (NICTA and University of New South Wales)
We study computational aspects of three prominent voting rules that use approval ballots to elect multiple winners. These rules are satisfaction approval voting, proportional approval voting, and reweighted approval voting. We first show that computing the winner for proportional approval voting is NP-hard, closing a long standing open problem. As none of the rules are strategyproof, even for dichotomous preferences, we study various strategic aspects of the rules. In particular, we examine the computational complexity of computing a best response for both a single agent and a group of agents. In many settings, we show that it is NP-hard for an agent or agents to compute how best to vote given a fixed set of approval ballots from the other agents.