84 research outputs found
On Measuring Bias in Online Information
Bias in online information has recently become a pressing issue, with search
engines, social networks and recommendation services being accused of
exhibiting some form of bias. In this vision paper, we make the case for a
systematic approach towards measuring bias. To this end, we discuss formal
measures for quantifying the various types of bias, we outline the system
components necessary for realizing them, and we highlight the related research
challenges and open problems.Comment: 6 pages, 1 figur
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
Equity of Attention: Amortizing Individual Fairness in Rankings
Rankings of people and items are at the heart of selection-making,
match-making, and recommender systems, ranging from employment sites to sharing
economy platforms. As ranking positions influence the amount of attention the
ranked subjects receive, biases in rankings can lead to unfair distribution of
opportunities and resources, such as jobs or income.
This paper proposes new measures and mechanisms to quantify and mitigate
unfairness from a bias inherent to all rankings, namely, the position bias,
which leads to disproportionately less attention being paid to low-ranked
subjects. Our approach differs from recent fair ranking approaches in two
important ways. First, existing works measure unfairness at the level of
subject groups while our measures capture unfairness at the level of individual
subjects, and as such subsume group unfairness. Second, as no single ranking
can achieve individual attention fairness, we propose a novel mechanism that
achieves amortized fairness, where attention accumulated across a series of
rankings is proportional to accumulated relevance.
We formulate the challenge of achieving amortized individual fairness subject
to constraints on ranking quality as an online optimization problem and show
that it can be solved as an integer linear program. Our experimental evaluation
reveals that unfair attention distribution in rankings can be substantial, and
demonstrates that our method can improve individual fairness while retaining
high ranking quality.Comment: Accepted to SIGIR 201
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
Automated data-driven decision making systems are increasingly being used to
assist, or even replace humans in many settings. These systems function by
learning from historical decisions, often taken by humans. In order to maximize
the utility of these systems (or, classifiers), their training involves
minimizing the errors (or, misclassifications) over the given historical data.
However, it is quite possible that the optimally trained classifier makes
decisions for people belonging to different social groups with different
misclassification rates (e.g., misclassification rates for females are higher
than for males), thereby placing these groups at an unfair disadvantage. To
account for and avoid such unfairness, in this paper, we introduce a new notion
of unfairness, disparate mistreatment, which is defined in terms of
misclassification rates. We then propose intuitive measures of disparate
mistreatment for decision boundary-based classifiers, which can be easily
incorporated into their formulation as convex-concave constraints. Experiments
on synthetic as well as real world datasets show that our methodology is
effective at avoiding disparate mistreatment, often at a small cost in terms of
accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web
Conference (WWW), 2017. Code available at:
https://github.com/mbilalzafar/fair-classificatio
Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy
Fairness concerns about algorithmic decision-making systems have been mainly
focused on the outputs (e.g., the accuracy of a classifier across individuals
or groups). However, one may additionally be concerned with fairness in the
inputs. In this paper, we propose and formulate two properties regarding the
inputs of (features used by) a classifier. In particular, we claim that fair
privacy (whether individuals are all asked to reveal the same information) and
need-to-know (whether users are only asked for the minimal information required
for the task at hand) are desirable properties of a decision system. We explore
the interaction between these properties and fairness in the outputs (fair
prediction accuracy). We show that for an optimal classifier these three
properties are in general incompatible, and we explain what common properties
of data make them incompatible. Finally we provide an algorithm to verify if
the trade-off between the three properties exists in a given dataset, and use
the algorithm to show that this trade-off is common in real data
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