157 research outputs found
Building Ethically Bounded AI
The more AI agents are deployed in scenarios with possibly unexpected
situations, the more they need to be flexible, adaptive, and creative in
achieving the goal we have given them. Thus, a certain level of freedom to
choose the best path to the goal is inherent in making AI robust and flexible
enough. At the same time, however, the pervasive deployment of AI in our life,
whether AI is autonomous or collaborating with humans, raises several ethical
challenges. AI agents should be aware and follow appropriate ethical principles
and should thus exhibit properties such as fairness or other virtues. These
ethical principles should define the boundaries of AI's freedom and creativity.
However, it is still a challenge to understand how to specify and reason with
ethical boundaries in AI agents and how to combine them appropriately with
subjective preferences and goal specifications. Some initial attempts employ
either a data-driven example-based approach for both, or a symbolic rule-based
approach for both. We envision a modular approach where any AI technique can be
used for any of these essential ingredients in decision making or decision
support systems, paired with a contextual approach to define their combination
and relative weight. In a world where neither humans nor AI systems work in
isolation, but are tightly interconnected, e.g., the Internet of Things, we
also envision a compositional approach to building ethically bounded AI, where
the ethical properties of each component can be fruitfully exploited to derive
those of the overall system. In this paper we define and motivate the notion of
ethically-bounded AI, we describe two concrete examples, and we outline some
outstanding challenges.Comment: Published at AAAI Blue Sky Track, winner of Blue Sky Awar
How Hard Is It to Control an Election by Breaking Ties?
We study the computational complexity of controlling the result of an
election by breaking ties strategically. This problem is equivalent to the
problem of deciding the winner of an election under parallel universes
tie-breaking. When the chair of the election is only asked to break ties to
choose between one of the co-winners, the problem is trivially easy. However,
in multi-round elections, we prove that it can be NP-hard for the chair to
compute how to break ties to ensure a given result. Additionally, we show that
the form of the tie-breaking function can increase the opportunities for
control. Indeed, we prove that it can be NP-hard to control an election by
breaking ties even with a two-stage voting rule.Comment: Revised and expanded version including longer proofs and additional
result
Incorporating Behavioral Constraints in Online AI Systems
AI systems that learn through reward feedback about the actions they take are
increasingly deployed in domains that have significant impact on our daily
life. However, in many cases the online rewards should not be the only guiding
criteria, as there are additional constraints and/or priorities imposed by
regulations, values, preferences, or ethical principles. We detail a novel
online agent that learns a set of behavioral constraints by observation and
uses these learned constraints as a guide when making decisions in an online
setting while still being reactive to reward feedback. To define this agent, we
propose to adopt a novel extension to the classical contextual multi-armed
bandit setting and we provide a new algorithm called Behavior Constrained
Thompson Sampling (BCTS) that allows for online learning while obeying
exogenous constraints. Our agent learns a constrained policy that implements
the observed behavioral constraints demonstrated by a teacher agent, and then
uses this constrained policy to guide the reward-based online exploration and
exploitation. We characterize the upper bound on the expected regret of the
contextual bandit algorithm that underlies our agent and provide a case study
with real world data in two application domains. Our experiments show that the
designed agent is able to act within the set of behavior constraints without
significantly degrading its overall reward performance.Comment: 9 pages, 6 figure
Heuristics in Multi-Winner Approval Voting
In many real world situations, collective decisions are made using voting.
Moreover, scenarios such as committee or board elections require voting rules
that return multiple winners. In multi-winner approval voting (AV), an agent
may vote for as many candidates as they wish. Winners are chosen by tallying up
the votes and choosing the top- candidates receiving the most votes. An
agent may manipulate the vote to achieve a better outcome by voting in a way
that does not reflect their true preferences. In complex and uncertain
situations, agents may use heuristics to strategize, instead of incurring the
additional effort required to compute the manipulation which most favors them.
In this paper, we examine voting behavior in multi-winner approval voting
scenarios with complete information. We show that people generally manipulate
their vote to obtain a better outcome, but often do not identify the optimal
manipulation. Instead, voters tend to prioritize the candidates with the
highest utilities. Using simulations, we demonstrate the effectiveness of these
heuristics in situations where agents only have access to partial information
Computational Aspects of Multi-Winner Approval Voting
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
Equilibria Under the Probabilistic Serial Rule
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.Comment: arXiv admin note: text overlap with arXiv:1401.6523, this paper
supersedes the equilibria section in our previous report arXiv:1401.652
Lessons Learned from Development of a Software Tool to Support Academic Advising
We detail some lessons learned while designing and testing a
decision-theoretic advising support tool for undergraduates at a large state
university. Between 2009 and 2011 we conducted two surveys of over 500 students
in multiple majors and colleges. These surveys asked students detailed
questions about their preferences concerning course selection, advising, and
career paths. We present data from this study which may be helpful for faculty
and staff who advise undergraduate students. We find that advising support
software tools can augment the student-advisor relationship, particularly in
terms of course planning, but cannot and should not replace in-person advising.Comment: 5 Figures, revised version including more figures and
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