Algorithmic fairness: Choices, assumptions, and definitions
A recent wave of research has attempted to define fairness quantitatively. In particular, this
work has explored what fairness might mean in the context of decisions based on the …
work has explored what fairness might mean in the context of decisions based on the …
Fairness in ranking, part ii: Learning-to-rank and recommender systems
In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …
algorithmic rankers, with contributions coming from the data management, algorithms …
Fairness and abstraction in sociotechnical systems
A key goal of the fair-ML community is to develop machine-learning based systems that,
once introduced into a social context, can achieve social and legal outcomes such as …
once introduced into a social context, can achieve social and legal outcomes such as …
Explainability fact sheets: a framework for systematic assessment of explainable approaches
Explanations in Machine Learning come in many forms, but a consensus regarding their
desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of …
desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of …
Data statements for natural language processing: Toward mitigating system bias and enabling better science
EM Bender, B Friedman - Transactions of the Association for …, 2018 - direct.mit.edu
In this paper, we propose data statements as a design solution and professional practice for
natural language processing technologists, in both research and development. Through the …
natural language processing technologists, in both research and development. Through the …
Fairness in information access systems
Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …
challenges for investigating and applying the fairness and non-discrimination concepts that …
Fairness in ranking: A survey
In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …
algorithmic rankers, with contributions coming from the data management, algorithms …
Toward situated interventions for algorithmic equity: lessons from the field
Research to date aimed at the fairness, accountability, and transparency of algorithmic
systems has largely focused on topics such as identifying failures of current systems and on …
systems has largely focused on topics such as identifying failures of current systems and on …
Fairsight: Visual analytics for fairness in decision making
Data-driven decision making related to individuals has become increasingly pervasive, but
the issue concerning the potential discrimination has been raised by recent studies. In …
the issue concerning the potential discrimination has been raised by recent studies. In …
Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
A recent flurry of research activity has attempted to quantitatively define" fairness" for
decisions based on statistical and machine learning (ML) predictions. The rapid growth of …
decisions based on statistical and machine learning (ML) predictions. The rapid growth of …