62,402 research outputs found
A Discussion of Discrimination and Fairness in Insurance Pricing
Indirect discrimination is an issue of major concern in algorithmic models.
This is particularly the case in insurance pricing where protected policyholder
characteristics are not allowed to be used for insurance pricing. Simply
disregarding protected policyholder information is not an appropriate solution
because this still allows for the possibility of inferring the protected
characteristics from the non-protected ones. This leads to so-called proxy or
indirect discrimination. Though proxy discrimination is qualitatively different
from the group fairness concepts in machine learning, these group fairness
concepts are proposed to 'smooth out' the impact of protected characteristics
in the calculation of insurance prices. The purpose of this note is to share
some thoughts about group fairness concepts in the light of insurance pricing
and to discuss their implications. We present a statistical model that is free
of proxy discrimination, thus, unproblematic from an insurance pricing point of
view. However, we find that the canonical price in this statistical model does
not satisfy any of the three most popular group fairness axioms. This seems
puzzling and we welcome feedback on our example and on the usefulness of these
group fairness axioms for non-discriminatory insurance pricing.Comment: 14 page
Fairness Under Demographic Scarce Regime
Most existing works on fairness assume the model has full access to
demographic information. However, there exist scenarios where demographic
information is partially available because a record was not maintained
throughout data collection or due to privacy reasons. This setting is known as
demographic scarce regime. Prior research have shown that training an attribute
classifier to replace the missing sensitive attributes (proxy) can still
improve fairness. However, the use of proxy-sensitive attributes worsens
fairness-accuracy trade-offs compared to true sensitive attributes. To address
this limitation, we propose a framework to build attribute classifiers that
achieve better fairness-accuracy trade-offs. Our method introduces uncertainty
awareness in the attribute classifier and enforces fairness on samples with
demographic information inferred with the lowest uncertainty. We show
empirically that enforcing fairness constraints on samples with uncertain
sensitive attributes is detrimental to fairness and accuracy. Our experiments
on two datasets showed that the proposed framework yields models with
significantly better fairness-accuracy trade-offs compared to classic attribute
classifiers. Surprisingly, our framework outperforms models trained with
constraints on the true sensitive attributes.Comment: 14 pages, 7 page
Fair Visual Recognition via Intervention with Proxy Features
Deep learning models often learn to make predictions that rely on sensitive
social attributes like gender and race, which poses significant fairness risks,
especially in societal applications, e.g., hiring, banking, and criminal
justice. Existing work tackles this issue by minimizing information about
social attributes in models for debiasing. However, the high correlation
between target task and social attributes makes bias mitigation incompatible
with target task accuracy. Recalling that model bias arises because the
learning of features in regard to bias attributes (i.e., bias features) helps
target task optimization, we explore the following research question: \emph{Can
we leverage proxy features to replace the role of bias feature in target task
optimization for debiasing?} To this end, we propose \emph{Proxy Debiasing}, to
first transfer the target task's learning of bias information from bias
features to artificial proxy features, and then employ causal intervention to
eliminate proxy features in inference. The key idea of \emph{Proxy Debiasing}
is to design controllable proxy features to on one hand replace bias features
in contributing to target task during the training stage, and on the other hand
easily to be removed by intervention during the inference stage. This
guarantees the elimination of bias features without affecting the target
information, thus addressing the fairness-accuracy paradox in previous
debiasing solutions. We apply \emph{Proxy Debiasing} to several benchmark
datasets, and achieve significant improvements over the state-of-the-art
debiasing methods in both of accuracy and fairness
Microfoundations of Social Capital
We show that the standard trust question routinely used in social capital research is importantly related to cooperation behavior and we provide evidence on the microfoundation of this relation. We run a large-scale public goods experiment over the internet in Denmark using a design that enables us to disentangle preferences for cooperation from beliefs about others’ cooperation. We find that the standard trust question is a proxy for cooperation preferences rather than beliefs about others’ cooperation. Moreover, we show that the “fairness question”, a recently proposed alternative to the standard trust question, is also related to cooperation behavior but operates through beliefs rather than preferences.Social capital, Trust, Fairness, Public goods, Cooperation, Experiment
The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features
AI systems have been known to amplify biases in real world data. Explanations
may help human-AI teams address these biases for fairer decision-making.
Typically, explanations focus on salient input features. If a model is biased
against some protected group, explanations may include features that
demonstrate this bias, but when biases are realized through proxy features, the
relationship between this proxy feature and the protected one may be less clear
to a human. In this work, we study the effect of the presence of protected and
proxy features on participants' perception of model fairness and their ability
to improve demographic parity over an AI alone. Further, we examine how
different treatments -- explanations, model bias disclosure and proxy
correlation disclosure -- affect fairness perception and parity. We find that
explanations help people detect direct biases but not indirect biases.
Additionally, regardless of bias type, explanations tend to increase agreement
with model biases. Disclosures can help mitigate this effect for indirect
biases, improving both unfairness recognition and the decision-making fairness.
We hope that our findings can help guide further research into advancing
explanations in support of fair human-AI decision-making
Testing Fair Wage Theory
Fairness considerations often are invoked to explain wage differences that appear unrelated to worker characteristics or job conditions, but non-experimental tests of fair wage models are rare and weak because of the limits of available market-generated data. In particular, such data rarely permit researchers to (a) identify suitable reference points that employees and employers might use in determining what is fair and (b) control for employees’ marginal output and its value. This study utilizes a unique dataset from the baseball labor market that solves both problems. We find no support for fair wage theory in this market. We also find that fairness premia can be illusory: Wages appear to be adjusted upward for reasons of fairness in regressions that control for variation in individuals’ physical output, but such premia evaporate when the value of that output (which can be market- or firm-specific) is held constant. This suggests that avoiding proxy measures of workers’ marginal revenue products in wage studies might reduce the number of labor market "anomalies" economists must resolve.fairness, efficiency wages, wage differentials
Estimating and Controlling for Fairness via Sensitive Attribute Predictors
The responsible use of machine learning tools in real world high-stakes
decision making demands that we audit and control for potential biases against
underrepresented groups. This process naturally requires access to the
sensitive attribute one desires to control, such as demographics, gender, or
other potentially sensitive features. Unfortunately, this information is often
unavailable. In this work we demonstrate that one can still reliably estimate,
and ultimately control, for fairness by using proxy sensitive attributes
derived from a sensitive attribute predictor. Specifically, we first show that
with just a little knowledge of the complete data distribution, one may use a
sensitive attribute predictor to obtain bounds of the classifier's true
fairness metric. Second, we demonstrate how one can provably control a
classifier's worst-case fairness violation with respect to the true sensitive
attribute by controlling for fairness with respect to the proxy sensitive
attribute. Our results hold under assumptions that are significantly milder
than previous works, and we illustrate these results with experiments on
synthetic and real datasets
Model Debiasing via Gradient-based Explanation on Representation
Machine learning systems produce biased results towards certain demographic
groups, known as the fairness problem. Recent approaches to tackle this problem
learn a latent code (i.e., representation) through disentangled representation
learning and then discard the latent code dimensions correlated with sensitive
attributes (e.g., gender). Nevertheless, these approaches may suffer from
incomplete disentanglement and overlook proxy attributes (proxies for sensitive
attributes) when processing real-world data, especially for unstructured data,
causing performance degradation in fairness and loss of useful information for
downstream tasks. In this paper, we propose a novel fairness framework that
performs debiasing with regard to both sensitive attributes and proxy
attributes, which boosts the prediction performance of downstream task models
without complete disentanglement. The main idea is to, first, leverage
gradient-based explanation to find two model focuses, 1) one focus for
predicting sensitive attributes and 2) the other focus for predicting
downstream task labels, and second, use them to perturb the latent code that
guides the training of downstream task models towards fairness and utility
goals. We show empirically that our framework works with both disentangled and
non-disentangled representation learning methods and achieves better
fairness-accuracy trade-off on unstructured and structured datasets than
previous state-of-the-art approaches
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