Understanding Fairness in AI Business Applications: A Case Study on AI Job Interviewing Platforms

Lou Therese Brandner, Lisa Koeritz
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:67-73, 2026.

Abstract

AI-based job interviewing platforms have proliferated in corporate recruitment, marketed not only as efficient alternatives to traditional hiring but as a fairer option: less prone to biases attributed to human decision-makers. Yet these systems have simultaneously drawn criticism for inaccuracy and discriminatory outcomes. This mixed-method study examines how companies offering AI-based interviewing tools communicate their commitment to fairness in public-facing materials, investigating how fairness is framed and its relationship to established Ai ethics operationalisations. Qualitative analysis identifies 3 dominant communicative strategies, each marked by conceptual shortcomings that risk misleading clients and applicants about the genuine equity of these systems. Complementary quantitative analysis reveals that fairness language is densely and strategically concentrated, consistently positive in sentiment, and anchored to a narrow set of recurring discursive patterns. Together, these findings indicate that the AI hiring industry has developed a relatively coherent fairness vocabulary that serves primarily to legitimate commercial products rather than to substantively address the structural challenges of algorithmic discrimination. We conclude that the conceptual integrity of AI fairness must be safeguarded, so that the term retains its critical, normative force rather than being reduced to a rhetorical device.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-brandner26a, title = {Understanding Fairness in AI Business Applications: A Case Study on AI Job Interviewing Platforms}, author = {Brandner, Lou Therese and Koeritz, Lisa}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {67--73}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/brandner26a/brandner26a.pdf}, url = {https://proceedings.mlr.press/v350/brandner26a.html}, abstract = {AI-based job interviewing platforms have proliferated in corporate recruitment, marketed not only as efficient alternatives to traditional hiring but as a fairer option: less prone to biases attributed to human decision-makers. Yet these systems have simultaneously drawn criticism for inaccuracy and discriminatory outcomes. This mixed-method study examines how companies offering AI-based interviewing tools communicate their commitment to fairness in public-facing materials, investigating how fairness is framed and its relationship to established Ai ethics operationalisations. Qualitative analysis identifies 3 dominant communicative strategies, each marked by conceptual shortcomings that risk misleading clients and applicants about the genuine equity of these systems. Complementary quantitative analysis reveals that fairness language is densely and strategically concentrated, consistently positive in sentiment, and anchored to a narrow set of recurring discursive patterns. Together, these findings indicate that the AI hiring industry has developed a relatively coherent fairness vocabulary that serves primarily to legitimate commercial products rather than to substantively address the structural challenges of algorithmic discrimination. We conclude that the conceptual integrity of AI fairness must be safeguarded, so that the term retains its critical, normative force rather than being reduced to a rhetorical device.} }
Endnote
%0 Conference Paper %T Understanding Fairness in AI Business Applications: A Case Study on AI Job Interviewing Platforms %A Lou Therese Brandner %A Lisa Koeritz %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-brandner26a %I PMLR %P 67--73 %U https://proceedings.mlr.press/v350/brandner26a.html %V 350 %X AI-based job interviewing platforms have proliferated in corporate recruitment, marketed not only as efficient alternatives to traditional hiring but as a fairer option: less prone to biases attributed to human decision-makers. Yet these systems have simultaneously drawn criticism for inaccuracy and discriminatory outcomes. This mixed-method study examines how companies offering AI-based interviewing tools communicate their commitment to fairness in public-facing materials, investigating how fairness is framed and its relationship to established Ai ethics operationalisations. Qualitative analysis identifies 3 dominant communicative strategies, each marked by conceptual shortcomings that risk misleading clients and applicants about the genuine equity of these systems. Complementary quantitative analysis reveals that fairness language is densely and strategically concentrated, consistently positive in sentiment, and anchored to a narrow set of recurring discursive patterns. Together, these findings indicate that the AI hiring industry has developed a relatively coherent fairness vocabulary that serves primarily to legitimate commercial products rather than to substantively address the structural challenges of algorithmic discrimination. We conclude that the conceptual integrity of AI fairness must be safeguarded, so that the term retains its critical, normative force rather than being reduced to a rhetorical device.
APA
Brandner, L.T. & Koeritz, L.. (2026). Understanding Fairness in AI Business Applications: A Case Study on AI Job Interviewing Platforms. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:67-73 Available from https://proceedings.mlr.press/v350/brandner26a.html.

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