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Found 22 papers, 2 papers with code
ShaRP: Explaining Rankings with Shapley Values
Using ShaRP, we show that even when the scoring function used by an algorithmic ranker is known and linear, the weight of each feature does not correspond to its Shapley value contribution.
Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
Building upon an agent-based framework for simulating recourse, this paper demonstrates how much effort is needed to overcome disparities in initial circumstances.
A New Paradigm for Counterfactual Reasoning in Fairness and Recourse
The traditional paradigm for counterfactual reasoning in this literature is the interventional counterfactual, where hypothetical interventions are imagined and simulated.
A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy
Our theoretical results center on the private mean estimation problem, while our empirical results center on extensive experiments on private data synthesis to demonstrate the effectiveness of stratification on a variety of private mechanisms.
Setting the Right Expectations: Algorithmic Recourse Over Time
The bulk of the literature on algorithmic recourse to-date focuses primarily on how to provide recourse to a single individual, overlooking a critical element: the effects of a continuously changing context.
The Unbearable Weight of Massive Privilege: Revisiting Bias-Variance Trade-Offs in the Context of Fair Prediction
In this paper we revisit the bias-variance decomposition of model error from the perspective of designing a fair classifier: we are motivated by the widely held socio-technical belief that noise variance in large datasets in social domains tracks demographic characteristics such as gender, race, disability, etc.
The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
The ``impossibility theorem'' -- which is considered foundational in algorithmic fairness literature -- asserts that there must be trade-offs between common notions of fairness and performance when fitting statistical models, except in two special cases: when the prevalence of the outcome being predicted is equal across groups, or when a perfectly accurate predictor is used.
On Fairness and Stability: Is Estimator Variance a Friend or a Foe?
We demonstrate when group-wise statistical bias analysis gives an incomplete picture, and what group-wise variance analysis can tell us in settings that differ in the magnitude of statistical bias.
Counterfactuals for the Future
Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past.
Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
In this work we use Equal Oppportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness.
Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
Increasingly, laws are being proposed and passed by governments around the world to regulate Artificial Intelligence (AI) systems implemented into the public and private sectors.
Spending Privacy Budget Fairly and Wisely
Our methods are based on the insights that feature importance can inform how privacy budget is allocated, and, further, that per-group feature importance and fairness-related performance objectives can be incorporated in the allocation.
An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring
Our approach is to (a) develop a methodology for an external audit of stability of predictions made by algorithmic personality tests, and (b) instantiate this methodology in an audit of two systems, Humantic AI and Crystal.
Disaggregated Interventions to Reduce Inequality
We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models.
Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy
Through our EOP-framework we hope to answer what it means for an ADS to be fair from a moral and political philosophy standpoint, and to pave the way for similar scholarship from ethics and legal experts.
Fairness in Ranking: A Survey
In this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking.
Fairness and Friends
Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithm to be “fair.” Most of these propositions are inspired by, but inadequately grounded in, scholarship from political philosophy.
Causal intersectionality for fair ranking
In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings.
Teaching Responsible Data Science: Charting New Pedagogical Territory
Recounting our own experience, and leveraging literature on pedagogical methods in data science and beyond, we propose the notion of an "object-to-interpret-with".
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions
FairPrep is based on a developer-centered design, and helps data scientists follow best practices in software engineering and machine learning.
Balanced Ranking with Diversity Constraints
Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged populations, or to improve the overall representativeness of the selected set.
Computational Social Choice Meets Databases
At the conceptual level, we give rigorous semantics to queries in this framework by introducing the notions of necessary answers and possible answers to queries.
