Epistemic Injustice in and through AI

Diana Chamma; Naseem Ahmadpour; Syed Ishtiaque Ahmed; Nusrat Jahan Mim; Wendy Qi Zhang; Catherine di Bona; Thida Sachathep; Heather Horst; Jenna Imad Harb

doi:10.1145/3764687.3767279

Epistemic Injustice in and through AI

Diana Chamma, Naseem Ahmadpour, Syed Ishtiaque Ahmed, Nusrat Jahan Mim, Wendy Qi Zhang, Catherine di Bona, Thida Sachathep, Heather Horst, Jenna Imad Harb

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

Abstract

AI is rapidly transforming knowledge production and practices across a range of domains, yet AI technologies often embed and perpetuate epistemic injustices—privileging dominant perspectives while marginalising others. Despite growing awareness of AI biases, many frameworks used in HCI and AI ethics fail to fully account for how AI models reproduce historical and systemic exclusions. We propose to critically examine epistemic injustice in AI across six domains; generative AI, creative practice, healthcare, work, education and automated decision-making. We explore how AI systems respond to diverse sociocultural, linguistic, and epistemological inputs, revealing biases in representation, accessibility, and credibility. Through reflection and collaborative mapping, we aim to identify research priorities and intervention strategies at individual, community, and broader society levels. By fostering rich dialogue and nuanced evidencing, we seek to advance research on epistemic justice in AI and create pathways for more inclusive and equitable futures.

Original language	English
Title of host publication	37th Australian Conference on Human-Computer Interaction (HCI) (OZCHI ’25)
Subtitle of host publication	Generative Intelligences, Planetary Futures
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	1-6
Number of pages	6
ISBN (Electronic)	979-8-4007-2016-1/25/11
DOIs	https://doi.org/10.1145/3764687.3767279
Publication status	Published - Dec 2025
Event	OzCHI '25: 37th Australian Conference on Human-Computer Interaction (HCI) : Generative Intelligences, Planetary Futures - Sydney, Australia Duration: 29 Nov 2025 → 3 Dec 2025 Conference number: 37 https://www.ozchi.org/2025/

Conference

Conference	OzCHI '25: 37th Australian Conference on Human-Computer Interaction (HCI)
Abbreviated title	OzCHI 2025
Country/Territory	Australia
City	Sydney
Period	29/11/25 → 3/12/25
Other	OzCHI 2025 is the 37th Australian Conference on Human-Computer Interaction. This annual non-profit event is for the Computer-Human Interaction Special Interest Group (CHISIG) and serves as a premier forum for the latest advancements in HCI research and practice. This year's event will be hosted in Sydney, Australia, from 29 November to 3 December 2025. OzCHI is Australasia's leading forum for cutting-edge HCI research and practice. It attracts a diverse international community of researchers, industry practitioners, academics, and students. Participants come from over 25 countries and various fields, including interaction design, user experience, information architecture, software engineering, human factors, information systems analysis, and social sciences. We welcome and encourage international and industry submissions and participation. Theme: Generative Intelligences, Planetary Futures This year's conference theme, Generative Intelligences, Planetary Futures, reflects the evolving role of HCI in collaborating with emerging intelligent technologies to shape sustainable futures for our planet.
Internet address	https://www.ozchi.org/2025/

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10.1145/3764687.3767279Licence: CC BY

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Chamma, D., Ahmadpour, N., Ishtiaque Ahmed, S., Jahan Mim, N., Qi Zhang, W., di Bona, C., Sachathep, T., Horst, H., & Harb, J. I. (2025). Epistemic Injustice in and through AI. In 37th Australian Conference on Human-Computer Interaction (HCI) (OZCHI ’25): Generative Intelligences, Planetary Futures (pp. 1-6). Association for Computing Machinery (ACM). https://doi.org/10.1145/3764687.3767279

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keywords = "Epistemic injustice, generative AI, postcolonial computing, critical theory, participatory design, AI bias",

author = "Diana Chamma and Naseem Ahmadpour and \{Ishtiaque Ahmed\}, Syed and \{Jahan Mim\}, Nusrat and \{Qi Zhang\}, Wendy and \{di Bona\}, Catherine and Thida Sachathep and Heather Horst and Harb, \{Jenna Imad\}",

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doi = "10.1145/3764687.3767279",

language = "English",

pages = "1--6",

booktitle = "37th Australian Conference on Human-Computer Interaction (HCI) (OZCHI {\textquoteright}25)",

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Chamma, D, Ahmadpour, N, Ishtiaque Ahmed, S, Jahan Mim, N, Qi Zhang, W, di Bona, C, Sachathep, T, Horst, H & Harb, JI 2025, Epistemic Injustice in and through AI. in 37th Australian Conference on Human-Computer Interaction (HCI) (OZCHI ’25): Generative Intelligences, Planetary Futures. Association for Computing Machinery (ACM), New York, pp. 1-6, OzCHI '25: 37th Australian Conference on Human-Computer Interaction (HCI)
, Sydney, New South Wales, Australia, 29/11/25. https://doi.org/10.1145/3764687.3767279

Epistemic Injustice in and through AI. / Chamma, Diana; Ahmadpour, Naseem; Ishtiaque Ahmed, Syed et al.
37th Australian Conference on Human-Computer Interaction (HCI) (OZCHI ’25): Generative Intelligences, Planetary Futures. New York: Association for Computing Machinery (ACM), 2025. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

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AU - Sachathep, Thida

AU - Horst, Heather

AU - Harb, Jenna Imad

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AB - AI is rapidly transforming knowledge production and practices across a range of domains, yet AI technologies often embed and perpetuate epistemic injustices—privileging dominant perspectives while marginalising others. Despite growing awareness of AI biases, many frameworks used in HCI and AI ethics fail to fully account for how AI models reproduce historical and systemic exclusions. We propose to critically examine epistemic injustice in AI across six domains; generative AI, creative practice, healthcare, work, education and automated decision-making. We explore how AI systems respond to diverse sociocultural, linguistic, and epistemological inputs, revealing biases in representation, accessibility, and credibility. Through reflection and collaborative mapping, we aim to identify research priorities and intervention strategies at individual, community, and broader society levels. By fostering rich dialogue and nuanced evidencing, we seek to advance research on epistemic justice in AI and create pathways for more inclusive and equitable futures.

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KW - critical theory

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KW - AI bias

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ER -

Epistemic Injustice in and through AI

Abstract

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