24,982 research outputs found
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
There has been much discussion of the right to explanation in the EU General
Data Protection Regulation, and its existence, merits, and disadvantages.
Implementing a right to explanation that opens the black box of algorithmic
decision-making faces major legal and technical barriers. Explaining the
functionality of complex algorithmic decision-making systems and their
rationale in specific cases is a technically challenging problem. Some
explanations may offer little meaningful information to data subjects, raising
questions around their value. Explanations of automated decisions need not
hinge on the general public understanding how algorithmic systems function.
Even though such interpretability is of great importance and should be pursued,
explanations can, in principle, be offered without opening the black box.
Looking at explanations as a means to help a data subject act rather than
merely understand, one could gauge the scope and content of explanations
according to the specific goal or action they are intended to support. From the
perspective of individuals affected by automated decision-making, we propose
three aims for explanations: (1) to inform and help the individual understand
why a particular decision was reached, (2) to provide grounds to contest the
decision if the outcome is undesired, and (3) to understand what would need to
change in order to receive a desired result in the future, based on the current
decision-making model. We assess how each of these goals finds support in the
GDPR. We suggest data controllers should offer a particular type of
explanation, unconditional counterfactual explanations, to support these three
aims. These counterfactual explanations describe the smallest change to the
world that can be made to obtain a desirable outcome, or to arrive at the
closest possible world, without needing to explain the internal logic of the
system
Eliminating Latent Discrimination: Train Then Mask
How can we control for latent discrimination in predictive models? How can we
provably remove it? Such questions are at the heart of algorithmic fairness and
its impacts on society. In this paper, we define a new operational fairness
criteria, inspired by the well-understood notion of omitted variable-bias in
statistics and econometrics. Our notion of fairness effectively controls for
sensitive features and provides diagnostics for deviations from fair decision
making. We then establish analytical and algorithmic results about the
existence of a fair classifier in the context of supervised learning. Our
results readily imply a simple, but rather counter-intuitive, strategy for
eliminating latent discrimination. In order to prevent other features proxying
for sensitive features, we need to include sensitive features in the training
phase, but exclude them in the test/evaluation phase while controlling for
their effects. We evaluate the performance of our algorithm on several
real-world datasets and show how fairness for these datasets can be improved
with a very small loss in accuracy
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
As virtually all aspects of our lives are increasingly impacted by
algorithmic decision making systems, it is incumbent upon us as a society to
ensure such systems do not become instruments of unfair discrimination on the
basis of gender, race, ethnicity, religion, etc. We consider the problem of
determining whether the decisions made by such systems are discriminatory,
through the lens of causal models. We introduce two definitions of group
fairness grounded in causality: fair on average causal effect (FACE), and fair
on average causal effect on the treated (FACT). We use the Rubin-Neyman
potential outcomes framework for the analysis of cause-effect relationships to
robustly estimate FACE and FACT. We demonstrate the effectiveness of our
proposed approach on synthetic data. Our analyses of two real-world data sets,
the Adult income data set from the UCI repository (with gender as the protected
attribute), and the NYC Stop and Frisk data set (with race as the protected
attribute), show that the evidence of discrimination obtained by FACE and FACT,
or lack thereof, is often in agreement with the findings from other studies. We
further show that FACT, being somewhat more nuanced compared to FACE, can yield
findings of discrimination that differ from those obtained using FACE.Comment: 7 pages, 2 figures, 2 tables.To appear in Proceedings of the
International Conference on World Wide Web (WWW), 201
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