54 research outputs found
On the Trade-offs between Adversarial Robustness and Actionable Explanations
As machine learning models are increasingly being employed in various
high-stakes settings, it becomes important to ensure that predictions of these
models are not only adversarially robust, but also readily explainable to
relevant stakeholders. However, it is unclear if these two notions can be
simultaneously achieved or if there exist trade-offs between them. In this
work, we make one of the first attempts at studying the impact of adversarially
robust models on actionable explanations which provide end users with a means
for recourse. We theoretically and empirically analyze the cost (ease of
implementation) and validity (probability of obtaining a positive model
prediction) of recourses output by state-of-the-art algorithms when the
underlying models are adversarially robust vs. non-robust. More specifically,
we derive theoretical bounds on the differences between the cost and the
validity of the recourses generated by state-of-the-art algorithms for
adversarially robust vs. non-robust linear and non-linear models. Our empirical
results with multiple real-world datasets validate our theoretical results and
show the impact of varying degrees of model robustness on the cost and validity
of the resulting recourses. Our analyses demonstrate that adversarially robust
models significantly increase the cost and reduce the validity of the resulting
recourses, thus shedding light on the inherent trade-offs between adversarial
robustness and actionable explanation
Towards a Unified Framework for Fair and Stable Graph Representation Learning
As the representations output by Graph Neural Networks (GNNs) are
increasingly employed in real-world applications, it becomes important to
ensure that these representations are fair and stable. In this work, we
establish a key connection between counterfactual fairness and stability and
leverage it to propose a novel framework, NIFTY (uNIfying Fairness and
stabiliTY), which can be used with any GNN to learn fair and stable
representations. We introduce a novel objective function that simultaneously
accounts for fairness and stability and develop a layer-wise weight
normalization using the Lipschitz constant to enhance neural message passing in
GNNs. In doing so, we enforce fairness and stability both in the objective
function as well as in the GNN architecture. Further, we show theoretically
that our layer-wise weight normalization promotes counterfactual fairness and
stability in the resulting representations. We introduce three new graph
datasets comprising of high-stakes decisions in criminal justice and financial
lending domains. Extensive experimentation with the above datasets demonstrates
the efficacy of our framework.Comment: Accepted to UAI'2
On the Privacy Risks of Algorithmic Recourse
As predictive models are increasingly being employed to make consequential
decisions, there is a growing emphasis on developing techniques that can
provide algorithmic recourse to affected individuals. While such recourses can
be immensely beneficial to affected individuals, potential adversaries could
also exploit these recourses to compromise privacy. In this work, we make the
first attempt at investigating if and how an adversary can leverage recourses
to infer private information about the underlying model's training data. To
this end, we propose a series of novel membership inference attacks which
leverage algorithmic recourse. More specifically, we extend the prior
literature on membership inference attacks to the recourse setting by
leveraging the distances between data instances and their corresponding
counterfactuals output by state-of-the-art recourse methods. Extensive
experimentation with real world and synthetic datasets demonstrates significant
privacy leakage through recourses. Our work establishes unintended privacy
leakage as an important risk in the widespread adoption of recourse methods
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