Fetching the paper…
Reading the bibliography…
The responsible AI (RAI) community has introduced numerous processes and artifacts (e.g., Model Cards, Transparency Notes, Data Cards) to facilitate transparency and support the governance of AI systems.
Saturation in qualitative research: exploring its conceptualization and operationalization
Saunders, B.; Sim, J.; Kingstone, T.; Baker, S.; Waterfield, J.; Bartlam, B.; Burroughs, H.; and Jinks, C. 2018 · 1907
Earlier work this paper cites.
InterpretML: A Unified Framework for Machine Learning Interpretability
Nori, H.; Jenkins, S.; Koch, P.; and Caruana, R. 2019 · 1909
Earlier work this paper cites.
Up the anthropologist: Perspectives gained from studying up
Nader, L. 1972 · 1972
Earlier work this paper cites.
Contextual design
Beyer, H.; and Holtzblatt, K. 1999 · 1999
Earlier work this paper cites.
Using thematic analysis in psychology
Braun, V.; and Clarke, V. 2006 · 2006
Earlier work this paper cites.
Front-of-package nutrition rating systems and symbols: Phase I report
Boon, C. S.; Lichtenstein, A. H.; and Wartella, E. A. 2010 · 2010
Earlier work this paper cites.
Against scale: Provocations and resistances to scale thinking
Hanna, A.; and Park, T. M. 2020 · 2010
Earlier work this paper cites.
Knowledge Inventory: State of the Art of Natural Hazards Research in the Social Sciences and Further Research Needs for Social Capacity Building
Kuhlicke, C.; and Steinführer, A. 2010 · 2010
Earlier work this paper cites.
Fear, duty, and regulatory compliance: lessons from three research projects
Kagan, R. A.; Gunningham, N.; and Thornton, D. 2011 · 2011
Earlier work this paper cites.
Linking social capacities and risk communication in Europe: a gap between theory and practice?
Höppner, C.; Whittle, R.; Bründl, M.; and Buchecker, M. 2012 · 2012
Earlier work this paper cites.
Chapter 8-Interviews and focus groups
Lazar, J.; Feng, J. H.; and Hochheiser, H. 2017 · 2017
Earlier work this paper cites.
Piloting for interviews in qualitative research: Operationalization and lessons learnt
Majid, M. A. A.; Othman, M.; Mohamad, S. F.; Lim, S. A. H.; Yusof, A.; et al. 2017 · 2017
Earlier work this paper cites.
Binary governance: Lessons from the GDPR’s approach to algorithmic accountability
Kaminski, M. E. 2018 · 2018
Earlier work this paper cites.
Algorithms of oppression: How search engines reinforce racism
Noble, S. U. 2018 · 2018
Earlier work this paper cites.
Algorithmic impact assessments: A practical framework for public agency
Reisman, D.; Schultz, J.; Crawford, K.; and Whittaker, M. 2018 · 2018
Earlier work this paper cites.
Using regulatory enforcement theory to explain compliance with quality and patient safety regulations: the case of internal audits
Weske, U.; Boselie, P.; Van Rensen, E. L.; and Schneider, M. M. 2018 · 2018
Earlier work this paper cites.
Reflecting on reflexive thematic analysis
Braun, V.; and Clarke, V. 2019 · 2019
Earlier work this paper cites.
Model cards for model reporting
Mitchell, M.; Wu, S.; Zaldivar, A.; Barnes, P.; Vasserman, L.; Hutchinson, B.; Spitzer, E.; Raji, I. D.; and Gebru, T. 2019 · 2019
Cited alongside, same era.
Physician compliance with quality and patient safety regulations: The role of perceived enforcement approaches and commitment
Weske, U.; Boselie, P.; van Rensen, E.; and Schneider, M. 2019 · 2019
Cited alongside, same era.
Nurses’ Compliance with Patient Safety Standards in an Accredited Hospital
Ywakaim Andrawes, S.; Faisal Fakhry, S.; and Abd El Azeem, H. 2019 · 2019
Cited alongside, same era.
Fairlearn: A toolkit for assessing and improving fairness in AI
Bird, S.; Dudík, M.; Edgar, R.; Horn, B.; Lutz, R.; Milan, V.; Sameki, M.; Wallach, H.; and Walker, K. 2020 · 2020
Cited alongside, same era.
Co-designing checklists to understand organizational challenges and opportunities around fairness in AI
Madaio, M. A.; Stark, L.; Wortman Vaughan, J.; and Wallach, H. 2020 · 2020
Cited alongside, same era.
An institutionalist approach to AI ethics: justifying the priority of government regulation over self-regulation
Ferretti, T. 2022 · 2022
Later among the works it cites.
“Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms
Balayn, A.; Yurrita, M.; Yang, J.; and Gadiraju, U. 2023 · 2023
Later among the works it cites.
Enhancing AI fairness through impact assessment in the European Union: a legal and computer science perspective
Calvi, A.; and Kotzinos, D. 2023 · 2023
Later among the works it cites.
Washington watches as Big Tech pitches its own rules for AI
Chatterjee, M.; and Bordelon, B. 2023 · 2023
Later among the works it cites.
Why waiting for A.I. laws, regulations from government could be a catastrophic mistake
Cohen, M. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Brief history of artificial intelligence
Muthukrishnan, N.; Maleki, F.; Ovens, K.; Reinhold, C.; Forghani, B.; Forghani, R.; et al. 2020 · 2020
Cited alongside, same era.
Bias in data-driven artificial intelligence systems—An introductory survey
Ntoutsi, E.; Fafalios, P.; Gadiraju, U.; Iosifidis, V.; Nejdl, W.; Vidal, M.-E.; Ruggieri, S.; Turini, F.; Papadopoulos, S.; Krasanakis, E.; et al. 2020 · 2020
Cited alongside, same era.
The Role of Civil Society
Stjernfelt, F.; Lauritzen, A. M.; Stjernfelt, F.; and Lauritzen, A. M. 2020 · 2020
Cited alongside, same era.
Regulation of algorithmic tools in the United States
Yoo, C. S.; and Lai, A. 2020 · 2020
Cited alongside, same era.
Datasheets for datasets
Gebru, T.; Morgenstern, J.; Vecchione, B.; Vaughan, J. W.; Wallach, H.; Iii, H. D.; and Crawford, K. 2021 · 2021
Cited alongside, same era.
A survey on bias and fairness in machine learning
Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. 2021 · 2021
Cited alongside, same era.
Algorithmic impact assessments and accountability: The co-construction of impacts
Metcalf, J.; Moss, E.; Watkins, E. A.; Singh, R.; and Elish, M. C. 2021 · 2021
Cited alongside, same era.
Co-creating a Transdisciplinary Map of Technology-mediated Harms, Risks and Vulnerabilities: Challenges, ambivalences and opportunities
Domínguez Hernández, A.; Ramokapane, K. M.; Das Chowdhury, P.; Michalec, O.; Johnstone, E.; Godwin, E.; Cork, A. G.; and Rashid, A. 2023 · 2023
Later among the works it cites.
Who is going to regulate AI?
Levin, B.; and Downes, L. 2023 · 2023
Later among the works it cites.
From bias to repair: Error as a site of collaboration and negotiation in applied data science work
Lin, C. K.; and Jackson, S. J. 2023 · 2023
Later among the works it cites.
Artificial intelligence act
Madiega, T. 2023 · 2023
Later among the works it cites.
Taking AI risks seriously: a new assessment model for the AI Act
Novelli, C.; Casolari, F.; Rotolo, A.; Taddeo, M.; and Floridi, L. 2023 · 2023
Later among the works it cites.
Stronger together: on the articulation of ethical charters, legal tools, and technical documentation in ML
Pistilli, G.; Muñoz Ferrandis, C.; Jernite, Y.; and Mitchell, M. 2023 · 2023
Later among the works it cites.
Algorithms as Social-Ecological-Technological Systems: an Environmental Justice Lens on Algorithmic Audits
Rakova, B.; and Dobbe, R. 2023 · 2023
Later among the works it cites.
Seeing like a toolkit: How toolkits envision the work of AI ethics
Wong, R. Y.; Madaio, M. A.; and Merrill, N. 2023 · 2023
Later among the works it cites.
Berman, G.; Goyal, N.; and Madaio, M. 2024 · 2024
Closest in time.
Use case cards: A use case reporting framework inspired by the european AI act
Hupont, I.; Fernández-Llorca, D.; Baldassarri, S.; and Gómez, E. 2024 · 2024
Closest in time.
” You can’t build what you don’t understand”: Practitioner Perspectives on Explainable AI in the Global South
Okolo, C. T.; and Lin, H. 2024 · 2024
Closest in time.
Guidelines for Integrating Value Sensitive Design in Responsible AI Toolkits
Sadek, M.; Constantinides, M.; Quercia, D.; and Mougenot, C. 2024 · 2024
Closest in time.