Research
I apply Resource Allocation and
market
design methods to real settings, including Kidney
Exchange and Blood Donation. This
work combines theory (such as matching and complexity analysis), empirical methods (such as
data-driven simulations), and real-world experiments. To ensure that these systems align with
stakeholder interests, I also study AI and Human Decision
Making. My academic advisor is John P Dickerson.
I apply resource allocation and market design methods to real settings, including Kidney Exchange and Blood Donation.
Patients with kidney failure have only two options: a lifetime on dialysis, or kidney
transplantation. Dialysis is far more expensive and burdensome than transplantation,
however
donor kidneys are scarce — on average, 20 people die each day in
the US while waiting for a
transplant.
Furthermore, many patients in need of a kidney have willing living donors, but
cannot undergo
transplantation due to medical incompatibilities.
To address this supply-demand mismatch, kidney exchange allows patients with willing
living
donors to swap their donors in order to find a compatible (or better) patient-donor
match.
Formulated as an optimization problem, kidney exchange is NP-hard and APX-hard,
though modern
exchanges are solvable in a reasonable amount of time.
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Robustness to Uncertainty
There are many sources of uncertainty in real kidney exchanges—due to medical, moral,
and policy
factors. These sources of uncertainty are difficult to characterize, and can severely
impact the
outcome of an exchange. We investigate techniques from robust optimization to address
this problem.
Publications:
-
Duncan C McElfresh, John P Dickerson, Ke Ren, and Hoda Bidkhori.
"Distributionally
Robust Cycle and Chain Packing With Application To Organ Exchange." 2021 Winter
Simulation Conference (WSC).
-
Duncan C McElfresh, Michael Curry, Sarah E Booker, Morgan Stuart, Darren
Stewart,
Ruthanne Leishman, Tuomas
Sandholm, and John P Dickerson. "Who can be matched via kidney exchange?"
[abstract]. Am
J Transplant. 2021; 21 (suppl 3). Abstract presented as a poster at the 2021
American Tranpsplant Congress (ATC).
-
Duncan C McElfresh, Michael Curry, Sarah E Booker, Morgan Stuart, Darren
Stewart,
Ruthanne Leishman, Tuomas
Sandholm, and John P Dickerson. "Improving Policy-constrained Kidney Exchange Via
Pre-screening." Am J Transplant. 2021; 21 (suppl 3). Abstract presented as a
talk at
the 2021 American Tranpsplant Congress (ATC).
- McElfresh, Duncan C, Michael Curry,
Tuomas Sandholm,
and John P Dickerson, "Improving Policy-Constrained Kidney Exchange via
Pre-Screening.”
Advances in Neural Information Processing Systems 33: Annual Conference on
Neural
Information Processing Systems (NeurIPS), 2020.
-
Bidkhori, Hoda, John P Dickerson, Ke Ren, and Duncan C
McElfresh. “Kidney exchange with Inhomogeneous Edge Existence Uncertainty.”
Conference on Uncertainty in Artificial Intelligence (UAI). 2020.
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McElfresh, Duncan C, Hoda Bidkhori, and John P Dickerson, "Scalable
Robust Kidney
Exchange.” Conference on Artificial Intelligence
(AAAI), 2019.
Fairness in Kidney Exchange
How can we prioritize marginalized patients, without severely impacting the overall
exchange? We
study several different methods for enforcing this notion of fairness, and
demonstrate their
effects on data collected from real exchanges.
Publications:
-
McElfresh, Duncan C, and John P Dickerson, "Balancing
lexicographic fairness and a utilitarian objective with application to kidney
exchange."
Conference on Artificial Intelligence (AAAI), 2018.
Ethics and Kidney Exchange
Designing a kidney exchange program requires input from medical professionals,
policymakers,
computer scientists, and ethicists. A "good" program should be both technically- and
morally-sound — however technical experts (e.g., computer scientists) and
stakeholders (e.g.,
medical professionals) often work independently. We propose a formal division of
labor between
technical experts and stakeholders, and outline a framework through which these
experts can
collaborate. Through this framework we analyze existing kidney exchange programs and
survey the
technical literature on kidney exchange algorithms. We identify areas for future
collaboration
between technical experts and stakeholders.
Relevant work:
-
Presentation: McElfresh, Duncan C, Patricia Mayer, Gabriel
Schnickel, and
John P Dickerson. "Ok Google: Who Gets the Kidney?": Artificial Intelligence
and
Transplant Algorithms. Panel presentation and discussion at the annual
meeting of
the American Society of Bioethics and Humanities (ASBH), Anaheim, CA. (slides)
Immunosuppression
Recent advances in immunosuppression treatments allow some patients to receive a kidney
transplant from donors who are otherwise medically incompatible. We develop a
theoretical
model for this setting, and provide an optimization framework for a wide variety of
objectives (e.g., prioritizing certain types of patients or transplants). Simulations
indicate that even a small number of immunosuppressants (10 or 20) can double
the
number of transplants facilitated by real-sized exchanges with hundreds of patients.
Publications:
-
Haris Aziz, Ágnes Cseh, John P Dickerson, and Duncan C McElfresh. “Optimal
Kidney
Exchange with Immunosuppressants.”
Conference on Artificial Intelligence (AAAI). 2021.
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Blood is a scarce resource that can save the lives of those in need, and managing the
blood
supply chain has been a topic of research for decades. We consider an aspect of the
blood supply
chain that is seldom addressed by the literature: coordinating a network of donors
to meet
demand from a network of recipients.
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In a collaboration with Facebook, we deployed the first large-scale algorithmic system
for
matching blood donors with recipients. We focused on the Facebook
Blood Donation Tool, a platform that connects prospective blood donors with
nearby
recipients. There are many objectives in this setting, including: increasing the number
of
blood donations, treating recipients fairly, and respecting user preferences. To
formalize
these goals we developed an online matching framework, and matching policies for
automatic
donor notification. Both simulations and a fielded experiment demonstrate that our
methods
increase expected donation rates by 5% which—when
generalized to the entire Blood Donation Tool—corresponds to an increase in tens of
thousands of donations every few months.
Relevant work:
-
McElfresh, Duncan C, Christian Kroer, Sergey Pupyrev, Eric
Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P
Dickerson. “Matching
Algorithms for Blood Donation” Under review at PNAS. An earlier version appeared at
The 21st ACM Conference on Economics and
Computation
(EC), 2020.
-
Poster and Presentation: McElfresh, Duncan C, Christian Kroer,
Sergey
Pupyrev, Eric Sodomka, John P Dickerson. “Matching Algorithms for Blood
Donation.”
Workshop on Mechanism Design for Social Good MD4SG, 2019.
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AI and Human Decision Making
AI is increasingly used to influence, or make, important decisions in a wide range of domains,
including medicine, education, criminal justice, and financial services. Indeed, two key
examples
are kidney exchange and blood donation. I study the interaction between
stakeholders and algorithmic decision tools, with an eye toward developing responsible decision
support tools.
AI and ML researchers have proposed several mathematical definitions of fairness, however it is
not
clear if stakeholders understand or agree with these notions. We develop and validate a
comprehension score to measure peoples’ understanding of mathematical fairness. Using a
hypothetical decision scenario related to hiring, we translate several mathematical fairness
definitions into “rules” that a hiring manager must follow. Using our comprehension
score, we find that most people do not understand these rules, and those who do often disagree
with
them. This raises questions about the usefulness of algorithmic fairness: can an AI system be
truly “fair” if its stakeholders do not understand its behavior?
Publications ∨
Publications ∧
-
Saha, Debjani, Candice Schumann, Duncan C McElfresh, John P
Dickerson, Michelle L Mazurek and Michael Carl Tschantz. “Measuring Non-Expert
Comprehension
of Machine Learning Fairness Metrics.” Proceedings of the Thirty-seventh
International
Conference on Machine Learning (ICML). 2020.
-
Saha, Debjani, Candice Schumann, Duncan C McElfresh, John P Dickerson,
Michelle L
Mazurek and Michael Carl Tschantz. “Human Comprehension of Fairness in Machine
Learning." AAAI/ACM
Conference on Artificial Intelligence, Ethics, and Society (AIES),
2020.
Many modern
AI methods, are guided by models of stake-
holder preferences, which are often learned
through observed decisions (such as product
purchases) or through hypothetical decisions
(e.g., surveys). There are many cases where
stakeholders may be unwilling to express a
preference: for example, if more information
is needed to arrive at a decision, or of all available options are bad; in these cases we say
they
are indecisive. Drawing from moral philosophy
and psychology, we develop a class of indecision models, which can be fit to observed data; in
two
survey studies we that indecision is common,
and several causes of indecision are plausible. This raises many questions for the use of AI in
decision making: from a theoretical perspective, how should we aggregate indecisive voters if
indecision has multiple meanings? From an empirical perspective, how can we identify an
indecisive
agent, and what characteristics of a decision scenario lead to indecision?
Publications ∨
Publications ∧
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McElfresh, Duncan C, Lok Chan, Kenzie Doyle, Walter Sinnott-Armstrong,
Vincent
Conitzer, Jana Schaich Borg,John P Dickerson. “Indecision Modeling.”
Conference on Artificial Intelligence (AAAI). 2021. (poster)
Using a survey study we simulate the effect of an AI tool on decision making: we suggest random
predictions of participant preferences, and we attribute this prediction to an "AI system," or a
human "expert." We find that participants follow these prediction—even though they are random—
both
when the predictions are attributed to “AI”, and to "experts" (compared with a control group
that
receives no prediction). This has serious implications for AI in decision making: if random
decision
support can influence a stakeholder’s behavior, it is easy to imagine that adversarial AI
systems
can easily manipulate a stakeholder’s decisions. Furthermore, if AI tools are both trained on
and
influence stakeholders’ behavior, it is difficult to define what a "correct" decision is.
Publications ∨
Publications ∧
-
Chan, Lok, Kenzie Doyle, Duncan C McElfresh, Vincent Conitzer, John P
Dickerson,
Jana
Schaich Borg and Walter Sinnott-Armstrong. “Artificial Artificial Intelligence:
Measuring
Influence of AI "Assessments" on Moral Decision-Making.” AAAI/ACM Conference on
Artificial Intelligence, Ethics, and Society (AIES),
2020.
