12,994 research outputs found
Counterfactual Fairness for Predictions using Generative Adversarial Networks
Fairness in predictions is of direct importance in practice due to legal,
ethical, and societal reasons. It is often achieved through counterfactual
fairness, which ensures that the prediction for an individual is the same as
that in a counterfactual world under a different sensitive attribute. However,
achieving counterfactual fairness is challenging as counterfactuals are
unobservable. In this paper, we develop a novel deep neural network called
Generative Counterfactual Fairness Network (GCFN) for making predictions under
counterfactual fairness. Specifically, we leverage a tailored generative
adversarial network to directly learn the counterfactual distribution of the
descendants of the sensitive attribute, which we then use to enforce fair
predictions through a novel counterfactual mediator regularization. If the
counterfactual distribution is learned sufficiently well, our method is
mathematically guaranteed to ensure the notion of counterfactual fairness.
Thereby, our GCFN addresses key shortcomings of existing baselines that are
based on inferring latent variables, yet which (a) are potentially correlated
with the sensitive attributes and thus lead to bias, and (b) have weak
capability in constructing latent representations and thus low prediction
performance. Across various experiments, our method achieves state-of-the-art
performance. Using a real-world case study from recidivism prediction, we
further demonstrate that our method makes meaningful predictions in practice
Counterfactual Explanation for Fairness in Recommendation
Fairness-aware recommendation eliminates discrimination issues to build
trustworthy recommendation systems.Explaining the causes of unfair
recommendations is critical, as it promotes fairness diagnostics, and thus
secures users' trust in recommendation models. Existing fairness explanation
methods suffer high computation burdens due to the large-scale search space and
the greedy nature of the explanation search process. Besides, they perform
score-based optimizations with continuous values, which are not applicable to
discrete attributes such as gender and race. In this work, we adopt the novel
paradigm of counterfactual explanation from causal inference to explore how
minimal alterations in explanations change model fairness, to abandon the
greedy search for explanations. We use real-world attributes from Heterogeneous
Information Networks (HINs) to empower counterfactual reasoning on discrete
attributes. We propose a novel Counterfactual Explanation for Fairness
(CFairER) that generates attribute-level counterfactual explanations from HINs
for recommendation fairness. Our CFairER conducts off-policy reinforcement
learning to seek high-quality counterfactual explanations, with an attentive
action pruning reducing the search space of candidate counterfactuals. The
counterfactual explanations help to provide rational and proximate explanations
for model fairness, while the attentive action pruning narrows the search space
of attributes. Extensive experiments demonstrate our proposed model can
generate faithful explanations while maintaining favorable recommendation
performance
Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning
The increasing application of Artificial Intelligence and Machine Learning
models poses potential risks of unfair behavior and, in light of recent
regulations, has attracted the attention of the research community. Several
researchers focused on seeking new fairness definitions or developing
approaches to identify biased predictions. However, none try to exploit the
counterfactual space to this aim. In that direction, the methodology proposed
in this work aims to unveil unfair model behaviors using counterfactual
reasoning in the case of fairness under unawareness setting. A counterfactual
version of equal opportunity named counterfactual fair opportunity is defined
and two novel metrics that analyze the sensitive information of counterfactual
samples are introduced. Experimental results on three different datasets show
the efficacy of our methodologies and our metrics, disclosing the unfair
behavior of classic machine learning and debiasing models
Achieving Causal Fairness in Machine Learning
Fairness is a social norm and a legal requirement in today\u27s society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative task to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also subject to fairness requirements. In the literature, machine learning researchers have proposed association-based fairness notions, e.g., statistical parity, disparate impact, equality of opportunity, etc., and developed respective discrimination mitigation approaches. However, these works did not consider that fairness should be treated as a causal relationship. Although it is well known that association does not imply causation, the gap between association and causation is not paid sufficient attention by the fairness researchers and stakeholders.
The goal of this dissertation is to study fairness in machine learning, define appropriate fairness notions, and develop novel discrimination mitigation approaches from a causal perspective. Based on Pearl\u27s structural causal model, we propose to formulate discrimination as causal effects of the sensitive attribute on the decision. We consider different types of causal effects to cope with different situations, including the path-specific effect for direct/indirect discrimination, the counterfactual effect for group/individual discrimination, and the path-specific counterfactual effect for general cases. In the attempt to measure discrimination, the unidentifiable situations pose an inevitable barrier to the accurate causal inference. To address this challenge, we propose novel bounding methods to accurately estimate the strength of unidentifiable fairness notions, including path-specific fairness, counterfactual fairness, and path-specific counterfactual fairness. Based on the estimation of fairness, we develop novel and efficient algorithms for learning fair classification models. Besides classification, we also investigate the discrimination issues in other machine learning scenarios, such as ranked data analysis
- …