7 research outputs found
The Disparate Effects of Strategic Manipulation
When consequential decisions are informed by algorithmic input, individuals
may feel compelled to alter their behavior in order to gain a system's
approval. Models of agent responsiveness, termed "strategic manipulation,"
analyze the interaction between a learner and agents in a world where all
agents are equally able to manipulate their features in an attempt to "trick" a
published classifier. In cases of real world classification, however, an
agent's ability to adapt to an algorithm is not simply a function of her
personal interest in receiving a positive classification, but is bound up in a
complex web of social factors that affect her ability to pursue certain action
responses. In this paper, we adapt models of strategic manipulation to capture
dynamics that may arise in a setting of social inequality wherein candidate
groups face different costs to manipulation. We find that whenever one group's
costs are higher than the other's, the learner's equilibrium strategy exhibits
an inequality-reinforcing phenomenon wherein the learner erroneously admits
some members of the advantaged group, while erroneously excluding some members
of the disadvantaged group. We also consider the effects of interventions in
which a learner subsidizes members of the disadvantaged group, lowering their
costs in order to improve her own classification performance. Here we encounter
a paradoxical result: there exist cases in which providing a subsidy improves
only the learner's utility while actually making both candidate groups
worse-off--even the group receiving the subsidy. Our results reveal the
potentially adverse social ramifications of deploying tools that attempt to
evaluate an individual's "quality" when agents' capacities to adaptively
respond differ.Comment: 29 pages, 4 figure
'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
Data-driven decision-making consequential to individuals raises important
questions of accountability and justice. Indeed, European law provides
individuals limited rights to 'meaningful information about the logic' behind
significant, autonomous decisions such as loan approvals, insurance quotes, and
CV filtering. We undertake three experimental studies examining people's
perceptions of justice in algorithmic decision-making under different scenarios
and explanation styles. Dimensions of justice previously observed in response
to human decision-making appear similarly engaged in response to algorithmic
decisions. Qualitative analysis identified several concerns and heuristics
involved in justice perceptions including arbitrariness, generalisation, and
(in)dignity. Quantitative analysis indicates that explanation styles primarily
matter to justice perceptions only when subjects are exposed to multiple
different styles---under repeated exposure of one style, scenario effects
obscure any explanation effects. Our results suggests there may be no 'best'
approach to explaining algorithmic decisions, and that reflection on their
automated nature both implicates and mitigates justice dimensions.Comment: 14 pages, 3 figures, ACM Conference on Human Factors in Computing
Systems (CHI'18), April 21--26, Montreal, Canad
On Fairness, Diversity and Randomness in Algorithmic Decision Making
Consider a binary decision making process where a single machine learning
classifier replaces a multitude of humans. We raise questions about the
resulting loss of diversity in the decision making process. We study the
potential benefits of using random classifier ensembles instead of a single
classifier in the context of fairness-aware learning and demonstrate various
attractive properties: (i) an ensemble of fair classifiers is guaranteed to be
fair, for several different measures of fairness, (ii) an ensemble of unfair
classifiers can still achieve fair outcomes, and (iii) an ensemble of
classifiers can achieve better accuracy-fairness trade-offs than a single
classifier. Finally, we introduce notions of distributional fairness to
characterize further potential benefits of random classifier ensembles.Comment: Presented as a poster at the 2017 Workshop on Fairness,
Accountability, and Transparency in Machine Learning (FAT/ML 2017