• Corpus ID: 236975962

Retiring Adult: New Datasets for Fair Machine Learning

  title={Retiring Adult: New Datasets for Fair Machine Learning},
  author={Frances Ding and Moritz Hardt and John Miller and Ludwig Schmidt},
Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit… 

Tackling Documentation Debt: A Survey on Algorithmic Fairness Datasets

This work surveys over two hundred datasets employed in algorithmic fairness research, producing standardized and searchable documentation for each of them, and summarizes the merits and limitations of Adult, COMPAS, and German Credit, calling into question their suitability as general-purpose fairness benchmarks.

Algorithmic fairness datasets: the story so far

This work surveys over two hundred datasets employed in algorithmic fairness research, and produces standardized and searchable documentation for each of them, rigorously identifying the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which this unifying documentation effort supports multiple contributions.

Data-Centric Factors in Algorithmic Fairness

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Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation

This work grapple with questions that arise along three stages of the machine learning pipeline when incorporating intersectionality as multiple demographic attributes: which demographic attributes to include as dataset labels, how to handle the progressively smaller size of subgroups during model training, and how to move beyond existing evaluation metrics when benchmarking model fairness for more subgroups.

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This paper overviews real‐world datasets used for fairness‐aware ML by identifying relationships between the different attributes, particularly with respect to protected attributes and class attribute, using a Bayesian network.

FLEA: Provably Fair Multisource Learning from Unreliable Training Data

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Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation

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FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data

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Achieving Downstream Fairness with Geometric Repair

It is argued that fairer classification outcomes can be produced through the development of setting-speci fic interventions, and it is shown that attaining distributional parity minimizes rate disparities across all thresholds in the up/downstream setting.

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation

This work suggests that tree-based ensemble models make anective baseline for tabular data, and are a sensible default when subgroup robustness is desired, even when compared to robustness- and fairness-enhancing methods.



AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

A new open-source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license, to help facilitate the transition of fairness research algorithms for use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms.

It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks

It is analyzed how current research and publication practices in algorithmic fairness can be ill-suited for meaningful engagement with fairness in CJ applications and can exacerbate previously delineated issues with data quality, real-world relevance, and inadvertent normative implications.

Learning Fair Representations

We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the

Unbiased look at dataset bias

A comparison study using a set of popular datasets, evaluated based on a number of criteria including: relative data bias, cross-dataset generalization, effects of closed-world assumption, and sample value is presented.

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

This work empirically demonstrates that its algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks.

Lessons from archives: strategies for collecting sociocultural data in machine learning

It is argued that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures for sociocultural data collection.

How Copyright Law Can Fix Artificial Intelligence's Implicit Bias Problem

As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist

Equality of Opportunity in Supervised Learning

This work proposes a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features and shows how to optimally adjust any learned predictor so as to remove discrimination according to this definition.

Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy

This paper examines ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods, and considers three key factors within the person subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology.