10 research outputs found
Recycling privileged learning and distribution matching for fairness
Equipping machine learning models with ethical and legal constraints is a serious issue; without this, the future of machine learning is at risk. This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions. In legal scholarships, the notion of fairness itself is evolving and multi-faceted. We set an overarching goal to develop a unified machine learning framework that is able to handle any definitions of fairness, their combinations, and also new definitions that might be stipulated in the future. To achieve our goal, we recycle two well-established machine learning techniques, privileged learning and distribution matching, and harmonize them for satisfying multi-faceted fairness definitions. We consider protected characteristics such as race and gender as privileged information that is available at training but not at test time; this accelerates model training and delivers fairness through unawareness. Further, we cast demographic parity, equalized odds, and equality of opportunity as a classical two-sample problem of conditional distributions, which can be solved in a general form by using distance measures in Hilbert Space. We show several existing models are special cases of ours. Finally, we advocate returning the Pareto frontier of multi-objective minimization of error and unfairness in predictions. This will facilitate decision makers to select an operating point and to be accountable for it
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
A critical concern in data-driven decision making is to build models whose
outcomes do not discriminate against some demographic groups, including gender,
ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of
the sensitive attributes is essential, while, in practice, these attributes may
not be available due to legal and ethical requirements. To address this
challenge, this paper studies a model that protects the privacy of the
individuals sensitive information while also allowing it to learn
non-discriminatory predictors. The method relies on the notion of differential
privacy and the use of Lagrangian duality to design neural networks that can
accommodate fairness constraints while guaranteeing the privacy of sensitive
attributes. The paper analyses the tension between accuracy, privacy, and
fairness and the experimental evaluation illustrates the benefits of the
proposed model on several prediction tasks
Fairness for Robust Log Loss Classification
Developing classification methods with high accuracy that also avoid unfair
treatment of different groups has become increasingly important for data-driven
decision making in social applications. Following the first principles of
distributional robustness, we derive a new classifier that incorporates
fairness criteria into its worst-case logarithmic loss minimization. This
construction takes the form of a minimax game and produces a parametric
exponential family conditional distribution that resembles truncated logistic
regression. We demonstrate the advantages of our approach on three benchmark
fairness datasets
Bias and unfairness in machine learning models: a systematic literature review
One of the difficulties of artificial intelligence is to ensure that model
decisions are fair and free of bias. In research, datasets, metrics,
techniques, and tools are applied to detect and mitigate algorithmic unfairness
and bias. This study aims to examine existing knowledge on bias and unfairness
in Machine Learning models, identifying mitigation methods, fairness metrics,
and supporting tools. A Systematic Literature Review found 40 eligible articles
published between 2017 and 2022 in the Scopus, IEEE Xplore, Web of Science, and
Google Scholar knowledge bases. The results show numerous bias and unfairness
detection and mitigation approaches for ML technologies, with clearly defined
metrics in the literature, and varied metrics can be highlighted. We recommend
further research to define the techniques and metrics that should be employed
in each case to standardize and ensure the impartiality of the machine learning
model, thus, allowing the most appropriate metric to detect bias and unfairness
in a given context