16,764 research outputs found
Paradoxes in Fair Computer-Aided Decision Making
Computer-aided decision making--where a human decision-maker is aided by a
computational classifier in making a decision--is becoming increasingly
prevalent. For instance, judges in at least nine states make use of algorithmic
tools meant to determine "recidivism risk scores" for criminal defendants in
sentencing, parole, or bail decisions. A subject of much recent debate is
whether such algorithmic tools are "fair" in the sense that they do not
discriminate against certain groups (e.g., races) of people.
Our main result shows that for "non-trivial" computer-aided decision making,
either the classifier must be discriminatory, or a rational decision-maker
using the output of the classifier is forced to be discriminatory. We further
provide a complete characterization of situations where fair computer-aided
decision making is possible
Quantitative multi-objective verification for probabilistic systems
We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
As machine learning is increasingly used to make real-world decisions, recent
research efforts aim to define and ensure fairness in algorithmic decision
making. Existing methods often assume a fixed set of observable features to
define individuals, but lack a discussion of certain features not being
observed at test time. In this paper, we study fairness of naive Bayes
classifiers, which allow partial observations. In particular, we introduce the
notion of a discrimination pattern, which refers to an individual receiving
different classifications depending on whether some sensitive attributes were
observed. Then a model is considered fair if it has no such pattern. We propose
an algorithm to discover and mine for discrimination patterns in a naive Bayes
classifier, and show how to learn maximum likelihood parameters subject to
these fairness constraints. Our approach iteratively discovers and eliminates
discrimination patterns until a fair model is learned. An empirical evaluation
on three real-world datasets demonstrates that we can remove exponentially many
discrimination patterns by only adding a small fraction of them as constraints
Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study
Many automated system analysis techniques (e.g., model checking, model-based
testing) rely on first obtaining a model of the system under analysis. System
modeling is often done manually, which is often considered as a hindrance to
adopt model-based system analysis and development techniques. To overcome this
problem, researchers have proposed to automatically "learn" models based on
sample system executions and shown that the learned models can be useful
sometimes. There are however many questions to be answered. For instance, how
much shall we generalize from the observed samples and how fast would learning
converge? Or, would the analysis result based on the learned model be more
accurate than the estimation we could have obtained by sampling many system
executions within the same amount of time? In this work, we investigate
existing algorithms for learning probabilistic models for model checking,
propose an evolution-based approach for better controlling the degree of
generalization and conduct an empirical study in order to answer the questions.
One of our findings is that the effectiveness of learning may sometimes be
limited.Comment: 15 pages, plus 2 reference pages, accepted by FASE 2017 in ETAP
Distributional convergence for the number of symbol comparisons used by QuickSort
Most previous studies of the sorting algorithm QuickSort have used the number
of key comparisons as a measure of the cost of executing the algorithm. Here we
suppose that the n independent and identically distributed (i.i.d.) keys are
each represented as a sequence of symbols from a probabilistic source and that
QuickSort operates on individual symbols, and we measure the execution cost as
the number of symbol comparisons. Assuming only a mild "tameness" condition on
the source, we show that there is a limiting distribution for the number of
symbol comparisons after normalization: first centering by the mean and then
dividing by n. Additionally, under a condition that grows more restrictive as p
increases, we have convergence of moments of orders p and smaller. In
particular, we have convergence in distribution and convergence of moments of
every order whenever the source is memoryless, that is, whenever each key is
generated as an infinite string of i.i.d. symbols. This is somewhat surprising;
even for the classical model that each key is an i.i.d. string of unbiased
("fair") bits, the mean exhibits periodic fluctuations of order n.Comment: Published in at http://dx.doi.org/10.1214/12-AAP866 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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