Building, Shifting, & Employing Power: A Taxonomy of Responses From Below to Algorithmic Harm
Open accessAbstract
A large body of research has attempted to ensure that algorithmic systems adhere to notions of fairness and transparency. Increasingly, researchers have highlighted that mitigating algorithmic harms requires explicitly taking power structures into account. Those with power over algorithmic systems often fail to sufficiently address algorithmic harms and rarely consult those directly harmed by algorithmic systems. Left to their own devices, people respond to algorithmic harms they encounter in a wide variety of ways, but we lack broader, overarching understandings of these responses. In this work, we synthesize documented, historical cases into a taxonomy of responses “from below” to algorithmic harm. Our taxonomy connects different types of responses to existing theorizations of power from fields including anthropology, human-computer interaction, and communication, centering how people employ, shift, and build power in their responses to algorithmic harm. Based on our taxonomy, we highlight an opportunity space for the FAccT community to engage with and support such action from below.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
While adaptations reshape use and interpretation of technologies in ways not originally intended by designers, they also place disproportionate labor demands on the othered.
“While these reshape the use and interpretation of technologies in ways not originally intended by designers they also place disproportionate labor demands on the ‘othered’ [2, 6, 33, 34].”
From AI and Non-Western Art Worlds: Reimagining Critical AI Futures through Artistic Inquiry and Situated Dialogue (2025) · cites this paper as [34]
The indexed passages describe a taxonomy of responses to algorithmic harm, but do not address whether adaptations change technologies’ intended use or impose disproportionate labor on ‘othered’ people. Searches for adaptations, unintended use, and disproportionate labor found no passage establishing those points. (LLM, conf=0.68)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Friedman, B., & Nissenbaum, H. (1996) · ACM Transactions on Information Systems · doi:10.1145/230538.230561 · Cited by 1,203 · cited 1× · see citing works
“It can be challenging to pin down issues and productive directions forward due to the opaque nature of algorithmic systems and the emergent nature of algorithmic behavior in real-world use contexts [32, 58].”
BackgroundTaxonomy of Risks posed by Language Models
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P.-S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533088 · Cited by 764 · see citing works
Algorithmic injustice: A relational ethics approach
Birhane, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100205 · Cited by 707 · see citing works
Participation Is not a Design Fix for Machine Learning
Sloane, M., Moss, E., Awomolo, O., & Forlano, L. (2022) · Equity and Access in Algorithms, Mechanisms, and Optimization · doi:10.1145/3551624.3555285 · Cited by 267 · see citing works
A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M. B., & De Choudhury, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287587 · Cited by 214 · see citing works
Kawakami, A., Sivaraman, V., Cheng, H.-F., Stapleton, L., Cheng, Y., Qing, D., Perer, A., Wu, Z. S., Zhu, H., & Holstein, K. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517439 · Cited by 143 · see citing works
Self-citationDeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517441 · Cited by 98 · see citing works
Self-citationTo Live in Their Utopia: Why Algorithmic Systems Create Absurd Outcomes
Alkhatib, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445740 · Cited by 81 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [3][3] [n. d.]. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT). https://facctconference.org/ Google Scholar
Cited by
Sorted by citations
Zhang, R., Li, H., Meng, H., Zhan, J., Gan, H., & Lee, Y.-C. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713429 · Cited by 123 · see citing works
“By focusing on mechanisms rather than solely on harm content or consequences, this typology complements existing taxonomies and frameworks in the field [26, 84, 98].”
Qadri, R., Mirowski, P., & Denton, R. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714049 · Cited by 12 · see citing works
- Authors
- DeVrio, A., Eslami, M., & Holstein, K.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- The 2024 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2024-06-03
- Pages
- 1093-1106
- OpenAlex
- W4399362837
- Cited by
- 21
- References
- 8
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 1
- FWCI
- 9.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by)
- Funders
- NSF (National Science Foundation) (2040942)
- Cisco Research
- Jacobs Foundation
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Keywords
- algorithmic harms, power structures, algorithmic fairness, algorithmic transparency, user resistance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
