Big Data, Data Science, and Civil Rights
By Solon Barocas, E. Bradley, Vasant G Honavar, Foster J. Provost. 2017
22 citations recorded by the source corpus.
Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how colleges and universities make admissions and financial aid decisions, and much more. As data-driven decisions increasingly affect every corner of our lives, there is an urgent need to ensure they do not become instruments of discrimination, barriers to equality, threats to social justice, and sources of unfairness. In this paper, we argue for a concrete research agenda aimed at addressing these concerns, comprising five areas of emphasis: (i) Determining if models and modeling procedures exhibit objectionable bias; (ii) Building awareness of fairness into machine learning methods; (iii) Improving the transparency and control of data- and model-driven decision making; (iv) Looking beyond the algorithm(s) for sources of bias and unfairness-in the myriad human decisions made during the problem formulation and modeling process; and (v) Supporting the cross-disciplinary scholarship necessary to do all of that well.
Subfields: Safety Research, AI Ethics, Fairness & Society, Social Sciences & Humanities
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Safety Research
- Vinodkumar Prabhakaran
- Andrew Smart
- Olga Russakovsky
- Donald Martin
- Jill A. Kuhlberg
- William S. Isaac
In AI Ethics, Fairness & Society
In Social Sciences & Humanities
- William S. Isaac
- Klint Qinami
- Andrew Smart
- Jia Deng
- Olga Russakovsky
- Vinodkumar Prabhakaran
- Kaiyu Yang
- Li Fei-Fei
Citations in the corpus
- Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy (2019, background): “In short, datasets often have biased distributions of demographics (gender, race, age, etc.); machine learning models are trained to exploit whatever correlations exist in the data, leading to discriminatory behavior against underrepresented groups [6, 7].”
- Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics (2020, background): “…(ML) based interventions that have the potential of impacting the real lives of people; a step that involves determining the strategic goals driving the interventions and translating those strategic goals into tractable machine learning problems (Barocas et al., 2017; Passi & Barocas, 2019).”
- Algorithms, Platforms, and Ethnic Bias: An Integrative Essay Forthcoming in Phylon: The Clark Atlanta University Review of Race and Culture (2018, unknown): “Effectively, the tracing data determines the future operation of the algorithm (Barocas et al., 2017; Danks & London, 2017).”
- Equal Protection Under the Algorithm : A Legal-Inspired Framework for Identifying Discrimination in Machine Learning (2018, background): “Work in this area generally recognizes that even if protected features are not explicitly included, the feature set may contain correlated features, leading to problematic outcomes (Barocas et al., 2017; Ajunwa et al., 2016).”