---
_id: '62028'
abstract:
- lang: eng
text: 'Explainable AI (XAI) methods can support the identification of biases in
automated decision-making (ADM) systems. However, existing research does not sufficiently
address whether these biases originate from the ADM system or mirror underlying
societal inequalities. This distinction is important because it has major implications
for how to act upon an explanation: while the societal bias produced by the ADM
system can be algorithmically fixed, societal inequalities demand societal actions.
To address this gap, we propose the RR-XAI-framework (recognition-redistribution
through XAI) that builds on a distinction between socio-technical and societal
bias and Nancy Fraser''s justice theory of recognition and redistribution. In
our framework, explanations can play two distinct roles: as a socio-technical
diagnosis when they reveal biases produced by the ADM system itself, or as a societal
diagnosis when they expose biases that reflect broader societal inequalities.
We then outline the operationalization of the framework and discuss its applicability
for cases in algorithmic hiring and credit scoring. Based on our findings, we
argue that the diagnostic functions of XAI are contingent on the provision of
such explanations, the resources of the audiences, as well as the current limits
of XAI techniques.'
article_type: original
author:
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
- first_name: Laura
full_name: State, Laura
last_name: State
- first_name: Atoosa
full_name: Kasirzadeh, Atoosa
last_name: Kasirzadeh
citation:
ama: 'Fahimi M, State L, Kasirzadeh A. From Explaining to Diagnosing: A Justice-Oriented
Framework of Explainable AI for Bias Detection. Proceedings of the AAAI/ACM
Conference on AI, Ethics, and Society. 2025;8(1):879-892. doi:10.1609/aies.v8i1.36597'
apa: 'Fahimi, M., State, L., & Kasirzadeh, A. (2025). From Explaining to Diagnosing:
A Justice-Oriented Framework of Explainable AI for Bias Detection. Proceedings
of the AAAI/ACM Conference on AI, Ethics, and Society, 8(1), 879–892.
https://doi.org/10.1609/aies.v8i1.36597'
bibtex: '@article{Fahimi_State_Kasirzadeh_2025, title={From Explaining to Diagnosing:
A Justice-Oriented Framework of Explainable AI for Bias Detection}, volume={8},
DOI={10.1609/aies.v8i1.36597},
number={1}, journal={Proceedings of the AAAI/ACM Conference on AI, Ethics, and
Society}, publisher={Association for the Advancement of Artificial Intelligence
(AAAI)}, author={Fahimi, Miriam and State, Laura and Kasirzadeh, Atoosa}, year={2025},
pages={879–892} }'
chicago: 'Fahimi, Miriam, Laura State, and Atoosa Kasirzadeh. “From Explaining to
Diagnosing: A Justice-Oriented Framework of Explainable AI for Bias Detection.”
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 8, no.
1 (2025): 879–92. https://doi.org/10.1609/aies.v8i1.36597.'
ieee: 'M. Fahimi, L. State, and A. Kasirzadeh, “From Explaining to Diagnosing: A
Justice-Oriented Framework of Explainable AI for Bias Detection,” Proceedings
of the AAAI/ACM Conference on AI, Ethics, and Society, vol. 8, no. 1, pp.
879–892, 2025, doi: 10.1609/aies.v8i1.36597.'
mla: 'Fahimi, Miriam, et al. “From Explaining to Diagnosing: A Justice-Oriented
Framework of Explainable AI for Bias Detection.” Proceedings of the AAAI/ACM
Conference on AI, Ethics, and Society, vol. 8, no. 1, Association for the
Advancement of Artificial Intelligence (AAAI), 2025, pp. 879–92, doi:10.1609/aies.v8i1.36597.'
short: M. Fahimi, L. State, A. Kasirzadeh, Proceedings of the AAAI/ACM Conference
on AI, Ethics, and Society 8 (2025) 879–892.
date_created: 2025-10-31T15:05:38Z
date_updated: 2025-11-18T10:09:40Z
department:
- _id: '756'
- _id: '26'
doi: 10.1609/aies.v8i1.36597
intvolume: ' 8'
issue: '1'
language:
- iso: eng
page: 879-892
publication: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
publication_identifier:
issn:
- 3065-8365
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence (AAAI)
status: public
title: 'From Explaining to Diagnosing: A Justice-Oriented Framework of Explainable
AI for Bias Detection'
type: journal_article
user_id: '118059'
volume: 8
year: '2025'
...
---
_id: '62229'
abstract:
- lang: eng
text: In 2024, the EU adopted the AI Act, a new set of rules for trustworthy artificial
intelligence. This legal instrument carves a large place for standardisation,
a regulatory technique that consists in crafting so-called harmonised technical
standards, to facilitate legal compliance by industry stakeholders. While EU technical
standards have been used in the past for ensuring product safety, for the first
time the AI Act relies on standardisation to facilitate compliance with fundamental
rights, including the right to non-discrimination and equality. The attempt to
translate inherently open-textured rights and ethical principles into operationalizable
standards raises critical questions. In particular, how will standardisation practices
under the new EU AI Act affect, transform, contest and stabilise notions of equality
and non-discrimination in an increasingly algorithmic society? This paper proposes
a research agenda to address this question and unpack the black box of AI standardisation.
author:
- first_name: Raphaële
full_name: Xenidis, Raphaële
last_name: Xenidis
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
citation:
ama: 'Xenidis R, Fahimi M. Standardising Equality in the Algorithmic Society? A
Research Agenda. In: Proceedings of Fourth European Workshop on Algorithmic
Fairness. PMLR; 2025:310–314.'
apa: Xenidis, R., & Fahimi, M. (2025). Standardising Equality in the Algorithmic
Society? A Research Agenda. Proceedings of Fourth European Workshop on Algorithmic
Fairness, 310–314.
bibtex: '@inproceedings{Xenidis_Fahimi_2025, title={Standardising Equality in the
Algorithmic Society? A Research Agenda}, booktitle={Proceedings of Fourth European
Workshop on Algorithmic Fairness}, publisher={PMLR}, author={Xenidis, Raphaële
and Fahimi, Miriam}, year={2025}, pages={310–314} }'
chicago: Xenidis, Raphaële, and Miriam Fahimi. “Standardising Equality in the Algorithmic
Society? A Research Agenda.” In Proceedings of Fourth European Workshop on
Algorithmic Fairness, 310–314. PMLR, 2025.
ieee: R. Xenidis and M. Fahimi, “Standardising Equality in the Algorithmic Society?
A Research Agenda,” in Proceedings of Fourth European Workshop on Algorithmic
Fairness, 2025, pp. 310–314.
mla: Xenidis, Raphaële, and Miriam Fahimi. “Standardising Equality in the Algorithmic
Society? A Research Agenda.” Proceedings of Fourth European Workshop on Algorithmic
Fairness, PMLR, 2025, pp. 310–314.
short: 'R. Xenidis, M. Fahimi, in: Proceedings of Fourth European Workshop on Algorithmic
Fairness, PMLR, 2025, pp. 310–314.'
date_created: 2025-11-18T09:59:34Z
date_updated: 2025-11-18T10:02:20Z
department:
- _id: '756'
- _id: '26'
language:
- iso: eng
page: 310–314
publication: Proceedings of Fourth European Workshop on Algorithmic Fairness
publisher: PMLR
status: public
title: Standardising Equality in the Algorithmic Society? A Research Agenda
type: conference
user_id: '118059'
year: '2025'
...
---
_id: '62029'
author:
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
- first_name: Katharina
full_name: Kinder-Kurlanda, Katharina
last_name: Kinder-Kurlanda
citation:
ama: Fahimi M, Kinder-Kurlanda K. Friction in the Materialities of Value. Relating
Transparency, Algorithms and Credit Scoring. Digital Culture & Society.
2025;9(2):141-160. doi:10.14361/dcs-2023-0208
apa: Fahimi, M., & Kinder-Kurlanda, K. (2025). Friction in the Materialities
of Value. Relating Transparency, Algorithms and Credit Scoring. Digital Culture
& Society, 9(2), 141–160. https://doi.org/10.14361/dcs-2023-0208
bibtex: '@article{Fahimi_Kinder-Kurlanda_2025, title={Friction in the Materialities
of Value. Relating Transparency, Algorithms and Credit Scoring}, volume={9}, DOI={10.14361/dcs-2023-0208}, number={2},
journal={Digital Culture & Society}, publisher={Transcript Verlag}, author={Fahimi,
Miriam and Kinder-Kurlanda, Katharina}, year={2025}, pages={141–160} }'
chicago: 'Fahimi, Miriam, and Katharina Kinder-Kurlanda. “Friction in the Materialities
of Value. Relating Transparency, Algorithms and Credit Scoring.” Digital Culture
& Society 9, no. 2 (2025): 141–60. https://doi.org/10.14361/dcs-2023-0208.'
ieee: 'M. Fahimi and K. Kinder-Kurlanda, “Friction in the Materialities of Value.
Relating Transparency, Algorithms and Credit Scoring,” Digital Culture &
Society, vol. 9, no. 2, pp. 141–160, 2025, doi: 10.14361/dcs-2023-0208.'
mla: Fahimi, Miriam, and Katharina Kinder-Kurlanda. “Friction in the Materialities
of Value. Relating Transparency, Algorithms and Credit Scoring.” Digital Culture
& Society, vol. 9, no. 2, Transcript Verlag, 2025, pp. 141–60, doi:10.14361/dcs-2023-0208.
short: M. Fahimi, K. Kinder-Kurlanda, Digital Culture & Society 9 (2025) 141–160.
date_created: 2025-10-31T15:07:42Z
date_updated: 2025-12-04T18:42:03Z
doi: 10.14361/dcs-2023-0208
extern: '1'
intvolume: ' 9'
issue: '2'
language:
- iso: ger
page: 141-160
publication: Digital Culture & Society
publication_identifier:
issn:
- 2364-2114
- 2364-2122
publication_status: published
publisher: Transcript Verlag
status: public
title: Friction in the Materialities of Value. Relating Transparency, Algorithms and
Credit Scoring
type: journal_article
user_id: '118059'
volume: 9
year: '2025'
...
---
_id: '62033'
abstract:
- lang: eng
text: AbstractThe literature addressing bias and
fairness in AI models (fair-AI) is growing at a fast
pace, making it difficult for novel researchers and practitioners to have a bird’s-eye
view picture of the field. In particular, many policy initiatives, standards,
and best practices in fair-AI have been proposed for setting principles, procedures,
and knowledge bases to guide and operationalize the management of bias and fairness.
The first objective of this paper is to concisely survey the state-of-the-art
of fair-AI methods and resources, and the main policies on bias in AI, with the
aim of providing such a bird’s-eye guidance for both researchers and practitioners.
The second objective of the paper is to contribute to the policy advice and best
practices state-of-the-art by leveraging from the results of the NoBIAS research
project. We present and discuss a few relevant topics organized around the NoBIAS
architecture, which is made up of a Legal Layer, focusing on the European Union
context, and a Bias Management Layer, focusing on understanding, mitigating, and
accounting for bias.
article_number: '31'
author:
- first_name: Jose M.
full_name: Alvarez, Jose M.
last_name: Alvarez
- first_name: Alejandra Bringas
full_name: Colmenarejo, Alejandra Bringas
last_name: Colmenarejo
- first_name: Alaa
full_name: Elobaid, Alaa
last_name: Elobaid
- first_name: Simone
full_name: Fabbrizzi, Simone
last_name: Fabbrizzi
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
- first_name: Antonio
full_name: Ferrara, Antonio
last_name: Ferrara
- first_name: Siamak
full_name: Ghodsi, Siamak
last_name: Ghodsi
- first_name: Carlos
full_name: Mougan, Carlos
last_name: Mougan
- first_name: Ioanna
full_name: Papageorgiou, Ioanna
last_name: Papageorgiou
- first_name: Paula
full_name: Reyero, Paula
last_name: Reyero
- first_name: Mayra
full_name: Russo, Mayra
last_name: Russo
- first_name: Kristen M.
full_name: Scott, Kristen M.
last_name: Scott
- first_name: Laura
full_name: State, Laura
last_name: State
- first_name: Xuan
full_name: Zhao, Xuan
last_name: Zhao
- first_name: Salvatore
full_name: Ruggieri, Salvatore
last_name: Ruggieri
citation:
ama: Alvarez JM, Colmenarejo AB, Elobaid A, et al. Policy advice and best practices
on bias and fairness in AI. Ethics and Information Technology. 2024;26(2).
doi:10.1007/s10676-024-09746-w
apa: Alvarez, J. M., Colmenarejo, A. B., Elobaid, A., Fabbrizzi, S., Fahimi, M.,
Ferrara, A., Ghodsi, S., Mougan, C., Papageorgiou, I., Reyero, P., Russo, M.,
Scott, K. M., State, L., Zhao, X., & Ruggieri, S. (2024). Policy advice and
best practices on bias and fairness in AI. Ethics and Information Technology,
26(2), Article 31. https://doi.org/10.1007/s10676-024-09746-w
bibtex: '@article{Alvarez_Colmenarejo_Elobaid_Fabbrizzi_Fahimi_Ferrara_Ghodsi_Mougan_Papageorgiou_Reyero_et
al._2024, title={Policy advice and best practices on bias and fairness in AI},
volume={26}, DOI={10.1007/s10676-024-09746-w},
number={231}, journal={Ethics and Information Technology}, publisher={Springer
Science and Business Media LLC}, author={Alvarez, Jose M. and Colmenarejo, Alejandra
Bringas and Elobaid, Alaa and Fabbrizzi, Simone and Fahimi, Miriam and Ferrara,
Antonio and Ghodsi, Siamak and Mougan, Carlos and Papageorgiou, Ioanna and Reyero,
Paula and et al.}, year={2024} }'
chicago: Alvarez, Jose M., Alejandra Bringas Colmenarejo, Alaa Elobaid, Simone Fabbrizzi,
Miriam Fahimi, Antonio Ferrara, Siamak Ghodsi, et al. “Policy Advice and Best
Practices on Bias and Fairness in AI.” Ethics and Information Technology
26, no. 2 (2024). https://doi.org/10.1007/s10676-024-09746-w.
ieee: 'J. M. Alvarez et al., “Policy advice and best practices on bias and
fairness in AI,” Ethics and Information Technology, vol. 26, no. 2, Art.
no. 31, 2024, doi: 10.1007/s10676-024-09746-w.'
mla: Alvarez, Jose M., et al. “Policy Advice and Best Practices on Bias and Fairness
in AI.” Ethics and Information Technology, vol. 26, no. 2, 31, Springer
Science and Business Media LLC, 2024, doi:10.1007/s10676-024-09746-w.
short: J.M. Alvarez, A.B. Colmenarejo, A. Elobaid, S. Fabbrizzi, M. Fahimi, A. Ferrara,
S. Ghodsi, C. Mougan, I. Papageorgiou, P. Reyero, M. Russo, K.M. Scott, L. State,
X. Zhao, S. Ruggieri, Ethics and Information Technology 26 (2024).
date_created: 2025-10-31T15:17:11Z
date_updated: 2025-11-18T09:56:30Z
doi: 10.1007/s10676-024-09746-w
extern: '1'
intvolume: ' 26'
issue: '2'
language:
- iso: eng
publication: Ethics and Information Technology
publication_identifier:
issn:
- 1388-1957
- 1572-8439
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Policy advice and best practices on bias and fairness in AI
type: journal_article
user_id: '118059'
volume: 26
year: '2024'
...
---
_id: '62031'
abstract:
- lang: eng
text: 'The field of fair AI aims to counter biased algorithms through computational
modelling. However, it faces increasing criticism for perpetuating the use of
overly technical and reductionist methods. As a result, novel approaches appear
in the field to address more socially-oriented and interdisciplinary (SOI) perspectives
on fair AI. In this paper, we take this dynamic as the starting point to study
the tension between computer science (CS) and SOI research. By drawing on STS
and CSCW theory, we position fair AI research as a matter of ''organizational
alignment'': what makes research ''doable'' is the successful alignment of three
levels of work organization (the social world, the laboratory, and the experiment).
Based on qualitative interviews with CS researchers, we analyze the tasks, resources,
and actors required for doable research in the case of fair AI. We find that CS
researchers engage with SOI research to some extent, but organizational conditions,
articulation work, and ambiguities of the social world constrain the doability
of SOI research for them. Based on our findings, we identify and discuss problems
for aligning CS and SOI as fair AI continues to evolve.'
author:
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
- first_name: Mayra
full_name: Russo, Mayra
last_name: Russo
- first_name: Kristen M.
full_name: Scott, Kristen M.
last_name: Scott
- first_name: Maria-Esther
full_name: Vidal, Maria-Esther
last_name: Vidal
- first_name: Bettina
full_name: Berendt, Bettina
last_name: Berendt
- first_name: Katharina
full_name: Kinder-Kurlanda, Katharina
last_name: Kinder-Kurlanda
citation:
ama: Fahimi M, Russo M, Scott KM, Vidal M-E, Berendt B, Kinder-Kurlanda K. Articulation
Work and Tinkering for Fairness in Machine Learning. Proceedings of the ACM
on Human-Computer Interaction. 2024;8(CSCW2):1-23. doi:10.1145/3686973
apa: Fahimi, M., Russo, M., Scott, K. M., Vidal, M.-E., Berendt, B., & Kinder-Kurlanda,
K. (2024). Articulation Work and Tinkering for Fairness in Machine Learning. Proceedings
of the ACM on Human-Computer Interaction, 8(CSCW2), 1–23. https://doi.org/10.1145/3686973
bibtex: '@article{Fahimi_Russo_Scott_Vidal_Berendt_Kinder-Kurlanda_2024, title={Articulation
Work and Tinkering for Fairness in Machine Learning}, volume={8}, DOI={10.1145/3686973},
number={CSCW2}, journal={Proceedings of the ACM on Human-Computer Interaction},
publisher={Association for Computing Machinery (ACM)}, author={Fahimi, Miriam
and Russo, Mayra and Scott, Kristen M. and Vidal, Maria-Esther and Berendt, Bettina
and Kinder-Kurlanda, Katharina}, year={2024}, pages={1–23} }'
chicago: 'Fahimi, Miriam, Mayra Russo, Kristen M. Scott, Maria-Esther Vidal, Bettina
Berendt, and Katharina Kinder-Kurlanda. “Articulation Work and Tinkering for Fairness
in Machine Learning.” Proceedings of the ACM on Human-Computer Interaction
8, no. CSCW2 (2024): 1–23. https://doi.org/10.1145/3686973.'
ieee: 'M. Fahimi, M. Russo, K. M. Scott, M.-E. Vidal, B. Berendt, and K. Kinder-Kurlanda,
“Articulation Work and Tinkering for Fairness in Machine Learning,” Proceedings
of the ACM on Human-Computer Interaction, vol. 8, no. CSCW2, pp. 1–23, 2024,
doi: 10.1145/3686973.'
mla: Fahimi, Miriam, et al. “Articulation Work and Tinkering for Fairness in Machine
Learning.” Proceedings of the ACM on Human-Computer Interaction, vol. 8,
no. CSCW2, Association for Computing Machinery (ACM), 2024, pp. 1–23, doi:10.1145/3686973.
short: M. Fahimi, M. Russo, K.M. Scott, M.-E. Vidal, B. Berendt, K. Kinder-Kurlanda,
Proceedings of the ACM on Human-Computer Interaction 8 (2024) 1–23.
date_created: 2025-10-31T15:10:31Z
date_updated: 2025-11-18T09:56:04Z
doi: 10.1145/3686973
extern: '1'
intvolume: ' 8'
issue: CSCW2
language:
- iso: eng
page: 1-23
publication: Proceedings of the ACM on Human-Computer Interaction
publication_identifier:
issn:
- 2573-0142
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: Articulation Work and Tinkering for Fairness in Machine Learning
type: journal_article
user_id: '118059'
volume: 8
year: '2024'
...
---
_id: '62228'
abstract:
- lang: eng
text: This chapter highlights the intricate nature of data and their profound social
implications. It examines the acts of rendering data visible and the inherent
power dynamics and imbalances that accompany such processes. Our dialogue unfolds
in three interconnected parts, each focusing on the intersection of in/visibility
and power. Part 1 attends to the challenges of producing knowledge about and with
data, emphasizing the relativity, fluidity, and instability inherent in data.
It explores frameworks that uncover the often invisible infrastructures of algorithms,
rendering visible the actors, technologies, and divergent values involved in data
manipulation. Part 2 presents empirical case studies that analyse the consequences
of data visibility while contemplating the methodological opportunities and challenges
of foregrounding the embedded values and norms within data. Part 3 discusses tool-based
interventions aimed at bringing alternative data framings and narratives to the
fore. It examines the complexities of tracing data across various contexts and
the value, utility, and obstacles associated with creating visual representations
of data and their flows. By critically engaging with the complexities of data
in/visibility, this chapter challenges existing gatekeepers and fosters a deeper
understanding of the multifaceted nature of data and its socio-political ramifications.
author:
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
- first_name: Petter
full_name: Falk, Petter
last_name: Falk
- first_name: Jonathan W. Y.
full_name: Gray, Jonathan W. Y.
last_name: Gray
- first_name: Juliane
full_name: Jarke, Juliane
last_name: Jarke
- first_name: Katharina
full_name: Kinder-Kurlanda, Katharina
last_name: Kinder-Kurlanda
- first_name: Evan
full_name: Light, Evan
last_name: Light
- first_name: Ellouise
full_name: McGeachey, Ellouise
last_name: McGeachey
- first_name: Itzelle Medina
full_name: Perea, Itzelle Medina
last_name: Perea
- first_name: Nikolaus
full_name: Poechhacker, Nikolaus
last_name: Poechhacker
- first_name: Lindsay
full_name: Poirier, Lindsay
last_name: Poirier
- first_name: Theo
full_name: Röhle, Theo
last_name: Röhle
- first_name: Tamar
full_name: Sharon, Tamar
last_name: Sharon
- first_name: Marthe
full_name: Stevens, Marthe
last_name: Stevens
- first_name: Bernard van
full_name: Gastel, Bernard van
last_name: Gastel
- first_name: Quinn
full_name: White, Quinn
last_name: White
- first_name: Irina
full_name: Zakharova, Irina
last_name: Zakharova
citation:
ama: 'Fahimi M, Falk P, Gray JWY, et al. In/visibilities in Data Studies: Methods,
Tools, and Interventions. In: Dialogues in Data Power. Bristol University
Press; 2024:52–79.'
apa: 'Fahimi, M., Falk, P., Gray, J. W. Y., Jarke, J., Kinder-Kurlanda, K., Light,
E., McGeachey, E., Perea, I. M., Poechhacker, N., Poirier, L., Röhle, T., Sharon,
T., Stevens, M., Gastel, B. van, White, Q., & Zakharova, I. (2024). In/visibilities
in Data Studies: Methods, Tools, and Interventions. In Dialogues in Data Power
(pp. 52–79). Bristol University Press.'
bibtex: '@inbook{Fahimi_Falk_Gray_Jarke_Kinder-Kurlanda_Light_McGeachey_Perea_Poechhacker_Poirier_et
al._2024, title={In/visibilities in Data Studies: Methods, Tools, and Interventions},
booktitle={Dialogues in Data Power}, publisher={Bristol University Press}, author={Fahimi,
Miriam and Falk, Petter and Gray, Jonathan W. Y. and Jarke, Juliane and Kinder-Kurlanda,
Katharina and Light, Evan and McGeachey, Ellouise and Perea, Itzelle Medina and
Poechhacker, Nikolaus and Poirier, Lindsay and et al.}, year={2024}, pages={52–79}
}'
chicago: 'Fahimi, Miriam, Petter Falk, Jonathan W. Y. Gray, Juliane Jarke, Katharina
Kinder-Kurlanda, Evan Light, Ellouise McGeachey, et al. “In/Visibilities in Data
Studies: Methods, Tools, and Interventions.” In Dialogues in Data Power,
52–79. Bristol University Press, 2024.'
ieee: 'M. Fahimi et al., “In/visibilities in Data Studies: Methods, Tools,
and Interventions,” in Dialogues in Data Power, Bristol University Press,
2024, pp. 52–79.'
mla: 'Fahimi, Miriam, et al. “In/Visibilities in Data Studies: Methods, Tools, and
Interventions.” Dialogues in Data Power, Bristol University Press, 2024,
pp. 52–79.'
short: 'M. Fahimi, P. Falk, J.W.Y. Gray, J. Jarke, K. Kinder-Kurlanda, E. Light,
E. McGeachey, I.M. Perea, N. Poechhacker, L. Poirier, T. Röhle, T. Sharon, M.
Stevens, B. van Gastel, Q. White, I. Zakharova, in: Dialogues in Data Power, Bristol
University Press, 2024, pp. 52–79.'
date_created: 2025-11-18T09:58:30Z
date_updated: 2025-11-18T10:02:15Z
department:
- _id: '756'
- _id: '26'
language:
- iso: eng
page: 52–79
publication: Dialogues in Data Power
publication_identifier:
isbn:
- 978-1-5292-3832-7
publisher: Bristol University Press
status: public
title: 'In/visibilities in Data Studies: Methods, Tools, and Interventions'
type: book_chapter
user_id: '118059'
year: '2024'
...
---
_id: '62230'
abstract:
- lang: eng
text: 'Algorithms have risen to become one, if not the central technology for producing,
circulating, and evaluating knowledge in multiple societal arenas. In this book,
scholars from the social sciences, humanities, and computer science argue that
this shift has, and will continue to have, profound implications for how knowledge
is produced and what and whose knowledge is valued and deemed valid. To attend
to this fundamental change, the authors propose the concept of algorithmic regimes
and demonstrate how they transform the epistemological, methodological, and political
foundations of knowledge production, sensemaking, and decision-making in contemporary
societies. Across sixteen chapters, the volume offers a diverse collection of
contributions along three perspectives on algorithmic regimes: the methods necessary
to research and design algorithmic regimes, the ways in which algorithmic regimes
reconfigure sociotechnical interactions, and the politics engrained in algorithmic
regimes.'
author:
- first_name: Katharina
full_name: Kinder-Kurlanda, Katharina
last_name: Kinder-Kurlanda
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
citation:
ama: 'Kinder-Kurlanda K, Fahimi M. Making Algorithms Fair: Ethnographic Insights
from Machine Learning Interventions. In: Jarke J, Prietl B, Egbert S, Boeva Y,
Heuer H, Arnold M, eds. Algorithmic Regimes. Methods, Interactions, and Politics.
Amsterdam University Press; 2024:309–330.'
apa: 'Kinder-Kurlanda, K., & Fahimi, M. (2024). Making Algorithms Fair: Ethnographic
Insights from Machine Learning Interventions. In J. Jarke, B. Prietl, S. Egbert,
Y. Boeva, H. Heuer, & M. Arnold (Eds.), Algorithmic Regimes. Methods, Interactions,
and Politics. (pp. 309–330). Amsterdam University Press.'
bibtex: '@inbook{Kinder-Kurlanda_Fahimi_2024, place={Amsterdam}, title={Making Algorithms
Fair: Ethnographic Insights from Machine Learning Interventions}, booktitle={Algorithmic
Regimes. Methods, Interactions, and Politics.}, publisher={Amsterdam University
Press}, author={Kinder-Kurlanda, Katharina and Fahimi, Miriam}, editor={Jarke,
Juliane and Prietl, Bianca and Egbert, Simon and Boeva, Yana and Heuer, Hendrik
and Arnold, Maike}, year={2024}, pages={309–330} }'
chicago: 'Kinder-Kurlanda, Katharina, and Miriam Fahimi. “Making Algorithms Fair:
Ethnographic Insights from Machine Learning Interventions.” In Algorithmic
Regimes. Methods, Interactions, and Politics., edited by Juliane Jarke, Bianca
Prietl, Simon Egbert, Yana Boeva, Hendrik Heuer, and Maike Arnold, 309–330. Amsterdam:
Amsterdam University Press, 2024.'
ieee: 'K. Kinder-Kurlanda and M. Fahimi, “Making Algorithms Fair: Ethnographic Insights
from Machine Learning Interventions,” in Algorithmic Regimes. Methods, Interactions,
and Politics., J. Jarke, B. Prietl, S. Egbert, Y. Boeva, H. Heuer, and M.
Arnold, Eds. Amsterdam: Amsterdam University Press, 2024, pp. 309–330.'
mla: 'Kinder-Kurlanda, Katharina, and Miriam Fahimi. “Making Algorithms Fair: Ethnographic
Insights from Machine Learning Interventions.” Algorithmic Regimes. Methods,
Interactions, and Politics., edited by Juliane Jarke et al., Amsterdam University
Press, 2024, pp. 309–330.'
short: 'K. Kinder-Kurlanda, M. Fahimi, in: J. Jarke, B. Prietl, S. Egbert, Y. Boeva,
H. Heuer, M. Arnold (Eds.), Algorithmic Regimes. Methods, Interactions, and Politics.,
Amsterdam University Press, Amsterdam, 2024, pp. 309–330.'
date_created: 2025-11-18T10:00:38Z
date_updated: 2025-11-18T10:02:25Z
department:
- _id: '756'
- _id: '26'
editor:
- first_name: Juliane
full_name: Jarke, Juliane
last_name: Jarke
- first_name: Bianca
full_name: Prietl, Bianca
last_name: Prietl
- first_name: Simon
full_name: Egbert, Simon
last_name: Egbert
- first_name: Yana
full_name: Boeva, Yana
last_name: Boeva
- first_name: Hendrik
full_name: Heuer, Hendrik
last_name: Heuer
- first_name: Maike
full_name: Arnold, Maike
last_name: Arnold
language:
- iso: eng
page: 309–330
place: Amsterdam
publication: Algorithmic Regimes. Methods, Interactions, and Politics.
publication_identifier:
isbn:
- 978-94-6372-848-5
publisher: Amsterdam University Press
status: public
title: 'Making Algorithms Fair: Ethnographic Insights from Machine Learning Interventions'
type: book_chapter
user_id: '118059'
year: '2024'
...
---
_id: '62231'
abstract:
- lang: eng
text: Explainable artificial intelligence (XAI) is a rapidly growing research field
that has received a lot of attention during the last few years. An important goal
of the field is to use its methods to detect (social) bias and discrimination.
Despite these positive intentions, aspects of XAI can be in conflict with feminist
approaches and values. Therefore, our conceptual contribution brings forward both
a careful assessment of current XAI methods, as well as visions for carefully
doing XAI from a feminist perspective. We conclude with a discussion on the possibilities
for caring XAI, and the challenges that might lie along the way.
author:
- first_name: Laura
full_name: State, Laura
last_name: State
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
citation:
ama: 'State L, Fahimi M. Careful Explanations: A Feminist Perspective on XAI. In:
Proceedings of the 2nd European Workshop on Algorithmic Fairness. CEUR
Workshop Proceedings; 2023.'
apa: 'State, L., & Fahimi, M. (2023). Careful Explanations: A Feminist Perspective
on XAI. Proceedings of the 2nd European Workshop on Algorithmic Fairness.'
bibtex: '@inproceedings{State_Fahimi_2023, place={Winterthur, Switzerland}, title={Careful
Explanations: A Feminist Perspective on XAI}, booktitle={Proceedings of the 2nd
European Workshop on Algorithmic Fairness}, publisher={CEUR Workshop Proceedings},
author={State, Laura and Fahimi, Miriam}, year={2023} }'
chicago: 'State, Laura, and Miriam Fahimi. “Careful Explanations: A Feminist Perspective
on XAI.” In Proceedings of the 2nd European Workshop on Algorithmic Fairness.
Winterthur, Switzerland: CEUR Workshop Proceedings, 2023.'
ieee: 'L. State and M. Fahimi, “Careful Explanations: A Feminist Perspective on
XAI,” 2023.'
mla: 'State, Laura, and Miriam Fahimi. “Careful Explanations: A Feminist Perspective
on XAI.” Proceedings of the 2nd European Workshop on Algorithmic Fairness,
CEUR Workshop Proceedings, 2023.'
short: 'L. State, M. Fahimi, in: Proceedings of the 2nd European Workshop on Algorithmic
Fairness, CEUR Workshop Proceedings, Winterthur, Switzerland, 2023.'
date_created: 2025-11-18T10:01:00Z
date_updated: 2025-11-18T10:02:09Z
department:
- _id: '756'
- _id: '26'
language:
- iso: eng
place: Winterthur, Switzerland
publication: Proceedings of the 2nd European Workshop on Algorithmic Fairness
publisher: CEUR Workshop Proceedings
status: public
title: 'Careful Explanations: A Feminist Perspective on XAI'
type: conference
user_id: '118059'
year: '2023'
...
---
_id: '62032'
alternative_title:
- Theoretical Approaches and Empirical Studies
citation:
ama: Fahimi M, Flatschart E, Schaffar W, eds. State and Statehood in the Global
South. Springer International Publishing; 2022. doi:10.1007/978-3-030-94000-3
apa: Fahimi, M., Flatschart, E., & Schaffar, W. (Eds.). (2022). State and
Statehood in the Global South. Springer International Publishing. https://doi.org/10.1007/978-3-030-94000-3
bibtex: '@book{Fahimi_Flatschart_Schaffar_2022, place={Cham}, title={State and Statehood
in the Global South}, DOI={10.1007/978-3-030-94000-3},
publisher={Springer International Publishing}, year={2022} }'
chicago: 'Fahimi, Miriam, Elmar Flatschart, and Wolfram Schaffar, eds. State
and Statehood in the Global South. Cham: Springer International Publishing,
2022. https://doi.org/10.1007/978-3-030-94000-3.'
ieee: 'M. Fahimi, E. Flatschart, and W. Schaffar, Eds., State and Statehood in
the Global South. Cham: Springer International Publishing, 2022.'
mla: Fahimi, Miriam, et al., editors. State and Statehood in the Global South.
Springer International Publishing, 2022, doi:10.1007/978-3-030-94000-3.
short: M. Fahimi, E. Flatschart, W. Schaffar, eds., State and Statehood in the Global
South, Springer International Publishing, Cham, 2022.
date_created: 2025-10-31T15:16:00Z
date_updated: 2025-11-18T09:56:13Z
doi: 10.1007/978-3-030-94000-3
editor:
- first_name: Miriam
full_name: Fahimi, Miriam
id: '118059'
last_name: Fahimi
orcid: 0000-0002-0619-3160
- first_name: Elmar
full_name: Flatschart, Elmar
last_name: Flatschart
- first_name: Wolfram
full_name: Schaffar, Wolfram
last_name: Schaffar
extern: '1'
language:
- iso: eng
place: Cham
publication_identifier:
isbn:
- '9783030939991'
- '9783030940003'
publication_status: published
publisher: Springer International Publishing
status: public
title: State and Statehood in the Global South
type: book_editor
user_id: '118059'
year: '2022'
...