1,923 research outputs found
Adapting a Kidney Exchange Algorithm to Align with Human Values
The efficient and fair allocation of limited resources is a classical problem
in economics and computer science. In kidney exchanges, a central market maker
allocates living kidney donors to patients in need of an organ. Patients and
donors in kidney exchanges are prioritized using ad-hoc weights decided on by
committee and then fed into an allocation algorithm that determines who gets
what--and who does not. In this paper, we provide an end-to-end methodology for
estimating weights of individual participant profiles in a kidney exchange. We
first elicit from human subjects a list of patient attributes they consider
acceptable for the purpose of prioritizing patients (e.g., medical
characteristics, lifestyle choices, and so on). Then, we ask subjects
comparison queries between patient profiles and estimate weights in a
principled way from their responses. We show how to use these weights in kidney
exchange market clearing algorithms. We then evaluate the impact of the weights
in simulations and find that the precise numerical values of the weights we
computed matter little, other than the ordering of profiles that they imply.
However, compared to not prioritizing patients at all, there is a significant
effect, with certain classes of patients being (de)prioritized based on the
human-elicited value judgments
AI Methods in Bioethics
Commentary about the role of AI in bioethics for the 10th anniversary issue of AJOB: Empirical Bioethic
Scalable Robust Kidney Exchange
In barter exchanges, participants directly trade their endowed goods in a
constrained economic setting without money. Transactions in barter exchanges
are often facilitated via a central clearinghouse that must match participants
even in the face of uncertainty---over participants, existence and quality of
potential trades, and so on. Leveraging robust combinatorial optimization
techniques, we address uncertainty in kidney exchange, a real-world barter
market where patients swap (in)compatible paired donors. We provide two
scalable robust methods to handle two distinct types of uncertainty in kidney
exchange---over the quality and the existence of a potential match. The latter
case directly addresses a weakness in all stochastic-optimization-based methods
to the kidney exchange clearing problem, which all necessarily require explicit
estimates of the probability of a transaction existing---a still-unsolved
problem in this nascent market. We also propose a novel, scalable kidney
exchange formulation that eliminates the need for an exponential-time
constraint generation process in competing formulations, maintains provable
optimality, and serves as a subsolver for our robust approach. For each type of
uncertainty we demonstrate the benefits of robustness on real data from a
large, fielded kidney exchange in the United States. We conclude by drawing
parallels between robustness and notions of fairness in the kidney exchange
setting.Comment: Presented at AAAI1
Computational ethics
Technological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework – computational ethics – that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions.publishedVersio
Bi-Objective Optimization of Kidney Exchanges
Matching people to their preferences is an algorithmic topic with real world applications. One such application is the kidney exchange. The best cure for patients whose kidneys are failing is to replace it with a healthy one. Unfortunately, biological factors (e.g., blood type) constrain the number of possible replacements. Kidney exchanges seek to alleviate some of this pressure by allowing donors to give their kidney to a patient besides the one they most care about and in turn the donor for that patient gives her kidney to the patient that this first donor most cares about. Roth et al.~first discussed the classic kidney exchange problem. Freedman et al.~expanded upon this work by optimizing an additional objective in addition to maximal matching. In this work, I implement the traditional kidney exchange algorithm as well as expand upon more recent work by considering multi-objective optimization of the exchange. In addition I compare the use of 2-cycles to 3-cycles. I offer two hypotheses regarding the results of my implementation. I end with a summary and a discussion about potential future work
Approaches to the Algorithmic Allocation of Public Resources: A Cross-disciplinary Review
Allocation of scarce resources is a recurring challenge for the public
sector: something that emerges in areas as diverse as healthcare, disaster
recovery, and social welfare. The complexity of these policy domains and the
need for meeting multiple and sometimes conflicting criteria has led to
increased focus on the use of algorithms in this type of decision. However,
little engagement between researchers across these domains has happened,
meaning a lack of understanding of common problems and techniques for
approaching them. Here, we performed a cross disciplinary literature review to
understand approaches taken for different areas of algorithmic allocation
including healthcare, organ transplantation, homelessness, disaster relief, and
welfare. We initially identified 1070 papers by searching the literature, then
six researchers went through them in two phases of screening resulting in 176
and 75 relevant papers respectively. We then analyzed the 75 papers from the
lenses of optimization goals, techniques, interpretability, flexibility, bias,
ethical considerations, and performance. We categorized approaches into
human-oriented versus resource-oriented perspective, and individual versus
aggregate and identified that 76% of the papers approached the problem from a
human perspective and 60% from an aggregate level using optimization
techniques. We found considerable potential for performance gains, with
optimization techniques often decreasing waiting times and increasing success
rate by as much as 50%. However, there was a lack of attention to responsible
innovation: only around one third of the papers considered ethical issues in
choosing the optimization goals while just a very few of them paid attention to
the bias issues. Our work can serve as a guide for policy makers and
researchers wanting to use an algorithm for addressing a resource allocation
problem
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