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Algorithms, Big Data, and Inequality

  • Algorithms Team


This project has produced over $927,000 in external grants and 39 publications thus far. Research topics include algorithmic management among cultural workers, agency of data subjects, estimation of causal effects from data for counterfactual fairness and comparing compliance procedures and research proposals for non-discrimination in statistical models.

About the Project

The proliferation of algorithms is reconfiguring our socio-economic systems in profound and complex ways. This collaborative project brings together scholars from across the social sciences to critically examine the design, understanding, and use of algorithmic systems. The researchers share a concern with how the concomitant rise of big data, machine learning, and digital surveillance has the potential to exacerbate social inequalities among vulnerable communities. The program of research will therefore develop a more holistic understanding of algorithmic bias across disciplinary boundaries and within such empirical domains as work/employment, finances/creditworthiness, health, and social media. Through such research, we aim to understand—and ultimately challenge—the kinds of data-driven inequality and discrimination that are defining social life in the algorithmic age.

About the Team

Martin T. Wells

Project Leader

Social Statistics, ILR

Statistical Science, CIS

Department Office: 1190 Comstock Hall

CCSS Office: 739 Rhodes Hall


Ifeoma Ajunwa

Project Member

Organizational Behavior, ILR

Department Office: 396 Ives Hall Faculty Wing

CCSS Office: 738 Rhodes Hall


Solon Barocas

Project Member

Information Science, CIS

Department Office: 211 Gates Hall


Brooke Erin Duffy

Project Member

Communication, CALS

Department Office: 478 Mann Library

CCSS Office: 741 Rhodes Hall


Malte Ziewitz

Project Member

Science & Technology Studies, CAS

Department Office: 313 Morrill Hall


Project Outcomes

  • Project Updates

    • Ajunwa (ILR School) and Wells are working on the project titled, "Mapping the Eco-system of Hiring Platforms." This project seeks to map the eco-system of hiring platforms by interviewing the developers of hiring platforms and creating a network view of the end users and saturation by industry/field.
    • Barocas (Information Science) along with Cornell graduate student Manish Raghavan (Computer Science) and Professors Karen Levy (Information Science) and Jon Kleinberg (Computer and Information Science) are investigating the practices of an emerging set of companies that specialize in algorithmic pre-employment assessments, with the goal of establishing what companies mean when they assert that their tools reduce or eliminate bias from the hiring process.
    • Duffy (Communication) and Cornell students Annika Pinch (Psychology) and Shruti Sannon (Communication) are collaborating on a study examining the impact of algorithmic systems on work in the media and creative industries. Through in-depth interviews with online content creators and gig workers, their study shows how cultural producers respond to the demands of algorithmic learning to achieve efficiencies of work.
    • Wells and colleagues are developing statistical methods for estimation of causal effects from observational data and are applying these methods to assessing various fairness measures in machine learning. This approach to causal inference formulates the distinction between associational and causal concepts by noting an associational concept is any relationship that can be defined in terms of a joint distribution of observed variables, and a causal concept is any relationship that cannot be defined from the distribution alone.
    • Ziewitz (Science & Technology Studies) and Organization Studies scholar Maximilian Heimstädt have been following recent attempts in New York City to introduce legislation for algorithmic accountability.

  • Funding

    • Ajunwa and Wells received an ILR School Theme Grant on Technology and Work ($20K).
    • Ajunwa’s NSF CAREER Award proposal, “The Development, Design, and Ethical Issues of Algorithmic Hiring Tools”, was recommended for funding in the amount of $526,877.
    • Ziewitz received an NSF CAREER Award for a project on “Understanding and Advancing Fair Representation in Algorithmic Systems” ($400,300).
    • Ziewitz and Stephen Hilgartner were awarded $178,361 by the Cornell Data Science Curriculum Initiative to establish the “Data Science & Society Lab.”
    • Wells’ research has been supported by an NSF grant ($150,000) for “Variable Selection When P>>N: Beyond the Linear Regression and Normal Errors Model” and a Department of Labor grant ($244,603) for “Criminal Record Inaccuracies and the Impact of a Record Education Intervention on Employment-Related Outcomes.”
    • Barocas received a grant from the MacArthur Foundation to support a new initiative at Cornell University on Artificial Intelligence, Policy, and Practice ($900,000), co-led by Jon Kleinberg (Cornell University), Karen Levy (Cornell University), and Helen Nissenbaum (Cornell Tech). He was also awarded a grant from the NSF for a new project on “Emerging Cultures of Data Science Ethics in the Academy and Industry” ($401,914), a collaboration with Anna Lauren Hoffmann (University of Washington), Karen Levy (Cornell University), and Deirdre Mulligan (University of California, Berkeley).

  • Key Publications: Ifeoma Ajunwa

    The Quantified Worker. Cambridge University Press. (Forthcoming)

    The Paradox of Automation as Anti-Bias Intervention. Cardozo Law Review. (Forthcoming)

    Age Discrimination by Algorithms. Berkeley Journal of Employment and Labor Law.

    Algorithms at Work: Productivity Monitoring Applications and Wearable Technology. St. Louis Law Journal.

    Ajunwa & Daniel Greene. Platforms at Work: Data Intermediaries in the Organization of the Workplace. Research in the Sociology of Work.

  • Key Publications: Solon Barocas

    Barocas, Moritz Hardt, & Arvind Narayanan. Fairness and Machine Learning. MIT Press, forthcoming.

    Samir Passi & Barocas. Problem Formulation and Fairness. Conference on Fairness, Accountability, and Transparency, 2019.

    Andrew Selbst & Barocas. The Intuitive Appeal of Explainable Machines. Fordham Law Review.

    Mitchell, Shira, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum. "Algorithmic Fairness: Choices, Assumptions, and Definitions." Annual Review of Statistics and Its Application 8 (2021).

    Donahue, Kate, and Solon Barocas, "Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness," In Proceedings of the 2021 Conference on Fairness, Accountability, and Transparency. 2021.

  • Key Publications: Brooke Erin Duffy

    Duffy et al. Imagining and Resisting Algorithmic Change: Networked Creative Communities. Presented at the 69th Annual Conference of the International Communication Association.

    Duffy, B. E. (2020). Algorithmic precarity in cultural work. Communication and the Public, 1-5. Online first:

    Petre, C., Duffy, B. E. & Hund, E. (2019). “Gaming the system”: Platform paternalism and the politics of algorithmic visibility. Social Media + Society, Oct-Dec., 1–12.

    Coverage in Wired 

    Coverage in Karma

    Poell, T., Nieborg, D., & Duffy, B. E. (in progress). Platforms and cultural production. London, UK: Polity Press.

  • Key Publications: Martin Wells

    Wells et al. Facilitating High‐Dimensional Transparent Classification Via Empirical Bayes Variable Selection. Applied Stochastic Models in Business and Industry.

    Wells et al. Exponential Family Word Embeddings: An Iterative Approach for Learning Word Vectors. 32nd Annual Conference on Neural Information Processing Systems.

  • Key Publications: Malte Ziewitz

    Ziewitz. Rethinking Gaming: The Ethical Work of Optimization in Web Search. Social Studies of Science.

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