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.
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)
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.
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.
We introduce a method for generating CP-nets uniformly at random. As CP-nets encode a subset of partial orders, ensuring that we generate samples uniformly at random is not a trivial task. We present algorithms for counting CP-nets, ranking and computing the rank of an arbitrary CP-net for a given number of nodes, and generating a CP-net given its rank. We also show how to generate all CP-nets with a given number of nodes.
Many of the seminal papers in preference handling have used food preferences as motivating examples for their work. As foodies, the authors find this particularly motivating. While we think that there is both research and commercial potential in preference-based software for restaurants, we believe that serious application of the MPREF community's technology to the problem of personal preference-driven presentation of menus, seating, etc., will require significant further innovation. We broadly survey the current use of preferences in making the dining-out experience more enjoyable, and we look at the states of the art for preference representation and reasoning, and for restaurant software. We illustrate some of our points with a short story.
Positional scoring rules in voting compute the score of an alternative by summing the scores for the alternative induced by every vote. This summation principle ensures that all votes contribute equally to the score of an alternative. We relax this assumption and, instead, aggregate scores by taking into account the rank of a score in the ordered list of scores obtained from the votes. This defines a new family of voting rules, rank-dependent scoring rules (RDSRs), based on ordered weighted average (OWA) operators, which, include all scoring rules, and many others, most of which of new. We study some properties of these rules, and show, empirically, that certain RDSRs are less manipulable than Borda voting, across a variety of statistical cultures.
We study the computational complexity of optimal bribery and manipulation schemes for sports tournaments with uncertain information: cup; challenge or caterpillar; and round robin. Our results carry over to the equivalent voting rules: sequential pair-wise elections, cup, and Copeland, when the set of candidates is exactly the set of voters. This restriction creates new difficulties for most existing algorithms. The complexity of bribery and manipulation are well studied, almost always assuming deterministic information about votes and results. We assume that for candidates i and j the probability that i beats j and the costs of lowering each probability by fixed increments are known to the manipulators. We provide complexity analyses for cup, challenge, and round robin competitions ranging from polynomial time to NP^PP. This shows that the introduction of uncertainty into the reasoning process drastically increases the complexity of bribery problems in some instances.
The undergraduate computer science curriculum is generally focused on skills and tools; most students are not exposed to much research in the field, and do not learn how to navigate the research literature. We describe how science fiction reviews were used as a gateway to research reviews. Students learn a little about current or recent research on a topic that stirs their imagination, and learn how to search for, read critically, and compare technical papers on a topic related their chosen science fiction book, movie, or TV show.