# "This is not a data problem": Algorithms and Power in Public Higher Education in Canada

arXiv: https://arxiv.org/abs/2403.13969 · HTML: https://mlsanity.com/paper/2403.13969/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 71 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [The Problem with Metrics is a Fundamental Problem for AI](https://mlsanity.com/paper/2002.08512/) (2020)
- [A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms](https://mlsanity.com/paper/2206.14983/) (2022)
- [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023)
- [The Principles of Data-Centric AI (DCAI)](https://mlsanity.com/paper/2211.14611/) (2022)
- [A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System](https://mlsanity.com/paper/2003.03541/) (2020)
- [A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare](https://mlsanity.com/paper/2107.03487/) (2021)
- [Algorithms and Decision-Making in the Public Sector](https://mlsanity.com/paper/2106.03673/) (2021)
- [Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation](https://mlsanity.com/paper/2302.11623/) (2023)
- [Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-Making](https://mlsanity.com/paper/2308.05224/) (2023)
- [An Uncommon Task: Participatory Design in Legal AI](https://mlsanity.com/paper/2203.06246/) (2022)
- [Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-Welfare](https://mlsanity.com/paper/2302.08497/) (2023)
- [Counterfactual Prediction Under Outcome Measurement Error](https://mlsanity.com/paper/2302.11121/) (2023)
- [Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis](https://mlsanity.com/paper/2203.05169/) (2022)
- [A Human-Centered Review of Algorithms in Decision-Making in Higher Education](https://mlsanity.com/paper/2302.05839/) (2023)

## Built on by (most cited first, 6 of 6)

- [Talking About the Assumption in the Room](https://mlsanity.com/paper/2502.13268/) (2025)
- [How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Language](https://mlsanity.com/paper/2506.06816/) (2025)
- [Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practices](https://mlsanity.com/paper/2508.10071/) (2025)
- [Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures](https://mlsanity.com/paper/2604.15514/) (2026)
- [Fairness Audits of Institutional Risk Models in Deployed ML Pipelines](https://mlsanity.com/paper/2604.19468/) (2026)
- [Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems](https://mlsanity.com/paper/2609.38552/) (2026)

## Abstract (arXiv)

Algorithmic decision-making is increasingly being adopted across public higher education. The expansion of data-driven practices by post-secondary institutions has occurred in parallel with the adoption of New Public Management approaches by neoliberal administrations. In this study, we conduct a qualitative analysis of an in-depth ethnographic case study of data and algorithms in use at a public college in Ontario, Canada. We identify the data, algorithms, and outcomes in use at the college. We assess how the college's processes and relationships support those outcomes and the different stakeholders' perceptions of the college's data-driven systems. In addition, we find that the growing reliance on algorithmic decisions leads to increased student surveillance, exacerbation of existing inequities, and the automation of the faculty-student relationship. Finally, we identify a cycle of increased institutional power perpetuated by algorithmic decision-making, and driven by a push towards financial sustainability.