research-article

Open access

The Forgotten Margins of AI Ethics

Authors:

Thomas Laurent,

Matthew S. Brown,

Johnathan Flowers,

Anthony Ventresque, and

Christopher L. DancyAuthors Info & Claims

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

June 2022

Pages 948 - 958

https://doi.org/10.1145/3531146.3533157

Published: 20 June 2022 Publication History

All formats PDF

Abstract

How has recent AI Ethics literature addressed topics such as fairness and justice in the context of continued social and structural power asymmetries? We trace both the historical roots and current landmark work that have been shaping the field and categorize these works under three broad umbrellas: (i) those grounded in Western canonical philosophy, (ii) mathematical and statistical methods, and (iii) those emerging from critical data/algorithm/information studies. We also survey the field and explore emerging trends by examining the rapidly growing body of literature that falls under the broad umbrella of AI Ethics. To that end, we read and annotated peer-reviewed papers published over the past four years in two premier conferences: FAccT and AIES. We organize the literature based on an annotation scheme we developed according to three main dimensions: whether the paper deals with concrete applications, use-cases, and/or people’s lived experience; to what extent it addresses harmed, threatened, or otherwise marginalized groups; and if so, whether it explicitly names such groups. We note that although the goals of the majority of FAccT and AIES papers were often commendable, their consideration of the negative impacts of AI on traditionally marginalized groups remained shallow. Taken together, our conceptual analysis and the data from annotated papers indicate that the field would benefit from an increased focus on ethical analysis grounded in concrete use-cases, people’s experiences, and applications as well as from approaches that are sensitive to structural and historical power asymmetries.

References

[1]

Philip E Agre. 1994. Surveillance and capture: Two models of privacy. The Information Society 10, 2 (1994), 101–127.

[2]

Sara Ahmed. 2007. A phenomenology of whiteness. Feminist theory 8, 2 (2007), 149–168.

[3]

M. Alexander. 2010. The new Jim Crow: Mass incarceration in the age of colorblindness. New Press, New York, NY, USA. https://search.library.wisc.edu/catalog/9910095136402121 Includes bibliographical references (pages 249-279) and index.

[4]

Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019. Discrimination through optimization: How Facebook’s Ad delivery can lead to biased outcomes. Proceedings of the ACM on Human-Computer Interaction 3, CSCW(2019), 1–30.

Digital Library

[5]

Roger T. Ames and David L. Hall. 2003. Dao De Jing—Making This Life Significant—A Philosophical Translation. Ballantine Books.

[6]

Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica, May 23(2016), 2016.

[7]

Ruha Benjamin. 2019. Race after technology: Abolitionist tools for the new jim code. John Wiley & Sons.

[8]

Abeba Birhane. 2021. Algorithmic injustice: a relational ethics approach. Patterns 2, 2 (2021), 100205.

[9]

Abeba Birhane and Vinay Uday Prabhu. 2021. Large image datasets: A pyrrhic win for computer vision?. In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1536–1546.

[10]

E. Bonilla-Silva. 2015. The Structure of Racism in Color-Blind, “Post-Racial” America. American Behavioral Scientist 59, 11 (2015), 1358–1376. https://doi.org/10.1177/0002764215586826

[11]

Simone Browne. 2015. Dark matters: On the surveillance of blackness. Duke University Press.

[12]

Matthew Browning and Bruce Arrigo. 2020. Stop and Risk: Policing, Data, and the Digital Age of Discrimination. American Journal of Criminal Justice(2020), 1–19.

[13]

J. Buolamwini and T. Gebru. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, Vol. 81. PMLR, New York, NY, USA, 77–91. http://proceedings.mlr.press

[14]

Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan. 2018. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, Vol. 81. PMLR, New York, NY, USA, 134–148. http://proceedings.mlr.press

[15]

Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement 20, 1 (1960), 37–46.

[16]

Patricia Hill Collins. 2002. Black feminist thought: Knowledge, consciousness, and the politics of empowerment. routledge.

[17]

Sasha Costanza-Chock. 2020. Design justice: Community-led practices to build the worlds we need.

[18]

Kate Crawford. 2021. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.

[19]

C. L. Dancy and P. K. Saucier. 2022. AI and Blackness: Towards moving beyond bias and representation. IEEE Transactions on Technology and Society 3, 1 (2022), 31–40. https://doi.org/10.1109/TTS.2021.3125998

[20]

Lorraine Daston. 2018. Calculation and the division of labor, 1750-1950. Bulletin of the German Historical Institute 62 (2018), 9–30.

[21]

Hanne De Jaegher. 2019. Loving and knowing: Reflections for an engaged epistemology. Phenomenology and the Cognitive Sciences(2019), 1–24.

[22]

Catherine D’ignazio and Lauren F Klein. 2020. Data feminism. MIT press.

[23]

Hubert Dreyfus. 1972. What computers can’t do: The limits of artificial intelligence. (1972).

[24]

Severin Engelmann, Mo Chen, Felix Fischer, Ching-yu Kao, and Jens Grossklags. 2019. Clear Sanctions, Vague Rewards: How China’s Social Credit System Currently Defines” Good” and” Bad” Behavior. In Proceedings of the conference on fairness, accountability, and transparency. 69–78.

Digital Library

[25]

Virginia Eubanks. 2018. Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.

Digital Library

[26]

Casey Fiesler, Natalie Garrett, and Nathan Beard. 2020. What do We teach when We teach tech ethics? A syllabi analysis. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education. 289–295.

Digital Library

[27]

R. Fogliato, A. Xiang, Z. Lipton, D. Nagin, and A. Chouldechova. 2021. On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes. In 2021 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery, 100–111. https://doi.org/10.1145/3461702.3462538

Digital Library

[28]

Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016. On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236(2016).

[29]

Oscar H Gandy Jr. 2021. The panoptic sort: A political economy of personal information. Oxford University Press.

[30]

Michael Gardiner. 1996. Alterity and ethics: A dialogical perspective. Theory, Culture & Society 13, 2 (1996), 121–143.

[31]

Thomas Krendl Gilbert and Yonatan Mintz. 2019. Epistemic Therapy for Bias in Automated Decision-Making. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19). Association for Computing Machinery, New York, NY, USA, 61–67. https://doi.org/10.1145/3306618.3314294

Digital Library

[32]

R. W. Gilmore. 2017. Abolition geography and the problem of innocence. In Futures of Black Radicalism, G. T. Johnson and A. Lubin (Eds.). Verso, Brooklyn, NY, 225–240.

[33]

Naman Goel, Mohammad Yaghini, and Boi Faltings. 2018. Non-Discriminatory Machine Learning through Convex Fairness Criteria. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (New Orleans, LA, USA) (AIES ’18). Association for Computing Machinery, New York, NY, USA, 116. https://doi.org/10.1145/3278721.3278722

Digital Library

[34]

Hila Gonen and Yoav Goldberg. 2019. Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 609–614.

[35]

L. M. Hampton. [n.d.]. Black Feminist Musings on Algorithmic Oppression. In 2021 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, 1. https://doi.org/10.1145/3442188.3445929

Digital Library

[36]

bell hooks. 1984. Feminist Theory: From Margin to Center. Routledge.

[37]

B. Jefferson. 2020. Digitize and Punish: Racial criminalization in the digital age. University of Minnesota Press, Minneapolis, MN, USA, 171.

[38]

Anna Jobin, Marcello Ienca, and Effy Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 9 (2019), 389–399.

[39]

Alicia Juarrero. 2000. Dynamics in action: Intentional behavior as a complex system. Emergence 2, 2 (2000), 24–57.

[40]

Pratyusha Kalluri. 2020. Don’t ask if artificial intelligence is good or fair, ask how it shifts power.Nature 583, 7815 (2020), 169–169.

[41]

Shivaram Kalyanakrishnan, Rahul Alex Panicker, Sarayu Natarajan, and Shreya Rao. 2018. Opportunities and Challenges for Artificial Intelligence in India. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (New Orleans, LA, USA) (AIES ’18). Association for Computing Machinery, New York, NY, USA, 164–170. https://doi.org/10.1145/3278721.3278738

Digital Library

[42]

Sampath Kannan, Aaron Roth, and Juba Ziani. 2019. Downstream Effects of Affirmative Action. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19). Association for Computing Machinery, New York, NY, USA, 240–248. https://doi.org/10.1145/3287560.3287578

Digital Library

[43]

Michael P. Kim, Amirata Ghorbani, and James Zou. 2019. Multiaccuracy: Black-Box Post-Processing for Fairness in Classification. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19). Association for Computing Machinery, New York, NY, USA, 247–254. https://doi.org/10.1145/3306618.3314287

Digital Library

[44]

M. L. King Jr.1968. Where do we go from her: Chaos or community?Beacon Press, Boston, MA, USA.

[45]

P. M. Krafft, Meg Young, Michael Katell, Jennifer E. Lee, Shankar Narayan, Micah Epstein, Dharma Dailey, Bernease Herman, Aaron Tam, Vivian Guetler, Corinne Bintz, Daniella Raz, Pa Ousman Jobe, Franziska Putz, Brian Robick, and Bissan Barghouti. 2021. An Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, 772–781. https://doi.org/10.1145/3442188.3445938

Digital Library

[46]

Peter Kuhn and Kailing Shen. 2013. Gender discrimination in job ads: Evidence from china. The Quarterly Journal of Economics 128, 1 (2013), 287–336.

[47]

Chenyang Li. 1994. The Confucian Concept of Jen and the Feminist Ethic of Care: A Comparative Study. Hypatia: A Journal of Feminist Philosophy 9, 1 (1994), 70–89.

[48]

Kristian Lum and William Isaac. 2016. To predict and serve?Significance 13, 5 (2016), 14–19.

[49]

Ivana Marková. 2016. The dialogical mind: Common sense and ethics. Cambridge University Press.

[50]

Jeanna Matthews, Marzieh Babaeianjelodar, Stephen Lorenz, Abigail Matthews, Mariama Njie, Nathaniel Adams, Dan Krane, Jessica Goldthwaite, and Clinton Hughes. 2019. The Right To Confront Your Accusers: Opening the Black Box of Forensic DNA Software. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19). Association for Computing Machinery, New York, NY, USA, 321–327. https://doi.org/10.1145/3306618.3314279

Digital Library

[51]

John S Mbiti. 1969. African Religions and Philosophy. New York: Frederick A.

[52]

Charlton D McIlwain. 2019. Black software: The internet and racial justice, from the AfroNet to Black Lives Matter. Oxford University Press, USA.

[53]

Daniel McNamara. 2019. Equalized Odds Implies Partially Equalized Outcomes Under Realistic Assumptions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19). Association for Computing Machinery, New York, NY, USA, 313–320. https://doi.org/10.1145/3306618.3314290

Digital Library

[54]

Sabelo Mhlambi. 2020. From Rationality to Relationality: Ubuntu as an Ethical and Human Rights Framework for Artificial Intelligence Governance. Carr Center for Human Rights Policy Discussion Paper Series (2020).

[55]

Alan Mishler. 2019. Modeling Risk and Achieving Algorithmic Fairness Using Potential Outcomes. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (Honolulu, HI, USA) (AIES ’19). Association for Computing Machinery, New York, NY, USA, 555–556. https://doi.org/10.1145/3306618.3314323

Digital Library

[56]

Shakir Mohamed, Marie-Therese Png, and William Isaac. 2020. Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. Philosophy & Technology 33, 4 (2020), 659–684.

[57]

K. G. Muhammad. 2010. The Condemnation of Blackness: Race, Crime, and the Making of Modern Urban America. Harvard University Press, Cambridge, MA.

[58]

Safiya Umoja Noble. 2018. Algorithms of oppression: How search engines reinforce racism. NYU Press.

[59]

N Noddings. 2013. Caring: A Relational Approach to Ethics and Moral Education. University of California Press.

[60]

Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447–453.

[61]

Cathy O’Neil. 2016. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.

Digital Library

[62]

Frank Pasquale. 2015. The black box society. Harvard University Press.

[63]

Neil R Powe. 2020. Black kidney function matters: use or misuse of race?Jama 324, 8 (2020), 737–738.

[64]

H. L. T. Quan. 2017. ”It’s hard to stop rebels that time travel”: Democratic living and the radical reimagining of old worlds. In Futures of Black Radicalism, G. T. Johnson and A. Lubin (Eds.). Verso, Brooklyn, NY, 173–193.

[65]

Inioluwa Deborah Raji. 2020. The discomfort of death counts: Mourning through the distorted lens of reported COVID-19 death data. Patterns 1, 4 (2020), 100066.

[66]

K. T. Rodolfa, E. Salomon, L. Haynes, I. H. Mendieta, J. Larson, and R. Ghani. [n.d.]. Case study: predictive fairness to reduce misdemeanor recidivism through social service interventions. In 2020 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, 142–153. https://doi.org/10.1145/3351095.3372863

Digital Library

[67]

Richard Rothstein. 2017. The color of law: A forgotten history of how our government segregated America. Liveright Publishing.

[68]

R Joshua Scannell. 2019. This is not Minority Report: Predictive policing and population racism. Captivating technology: Race, carceral technoscience, and liberatory imagination in everyday life(2019), 107–129.

[69]

Andrew D Selbst, Danah Boyd, Sorelle A Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. Fairness and abstraction in sociotechnical systems. In Proceedings of the Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency. Atlanta, GA, USA, 59–68.

Digital Library

[70]

John Shotter. 2006. Vygotsky, Bakhtin, Goethe: Consciousness and the dynamics of voice. (2006).

[71]

Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Nunes Ribeiro, George Arvanitakis, Fabrício Benevenuto, Krishna P. Gummadi, Patrick Loiseau, and Alan Mislove. 2018. Potential for Discrimination in Online Targeted Advertising. In Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, Vol. 81. PMLR, New York, NY, USA, 5–19. http://proceedings.mlr.press/v81

[72]

Biplav Srivastava and Francesca Rossi. 2018. Towards Composable Bias Rating of AI Services. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (New Orleans, LA, USA) (AIES ’18). Association for Computing Machinery, New York, NY, USA, 284–289. https://doi.org/10.1145/3278721.3278744

Digital Library

[73]

Sylvia Tamale. 2020. Decolonization and Afro-feminism. Daraja Press.

[74]

B. Taskesen, J. Blanchet, D. Kuhn, and V. A. Nguyen. 2021. A Statistical Test for Probabilistic Fairness. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, 648–665. https://doi.org/10.1145/3442188.3445927

Digital Library

[75]

Keeanga-Yamahtta Taylor. 2019. Race for profit: How banks and the real estate industry undermined black homeownership. UNC Press Books.

[76]

Shannon Vallor. 2016. Technology and the Virtues: A Philosophical Guide to a World Worth Wanting. Oxford University Press.

[77]

B Van Norden. 2017. Taking Back Philosophy: A Multicultural Manifesto. Columbia University Press.

[78]

Marisa Vasconcelos, Carlos Cardonha, and Bernardo Gonçalves. 2018. Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (New Orleans, LA, USA) (AIES ’18). Association for Computing Machinery, New York, NY, USA, 323–329. https://doi.org/10.1145/3278721.3278751

Digital Library

[79]

Heinz Von Foerster and Bernhard Pörksen. 2002. Understanding systems: Conversations on epistemology and ethics. Kluwer Academic/Plenum Publishers New York.

[80]

Darshali A Vyas, Leo G Eisenstein, and David S Jones. 2020. Hidden in plain sight—reconsidering the use of race correction in clinical algorithms.

[81]

Alexander G Weheliye. 2014. Law: Property. In Habeas viscus: Racializing assemblages, biopolitics, and black feminist theories of the human. Duke University Press, Durham, NC, 74–88.

[82]

Joseph Weizenbaum. 1976. Computer power and human reason: From judgment to calculation.(1976).

[83]

Ida B. Wells. 1999 (1893). Lynch Law. University of Illinois Press, Urbana, Book section 4.

[84]

Terry Winograd and Fernando Flores. 1986. Understanding computers and cognition: A new foundation for design. Intellect Books.

Digital Library

[85]

S. Wynter. 2003. Unsettling the Coloniality of Being/Power/Truth/Freedom: Towards the Human, After Man, Its Overrepresentation - An Argument. CR: The New Centennial Review 3, 3 (2003), 257–337. http://www.jstor.org/stable/41949874

[86]

Shoshana Zuboff. 2019. The age of surveillance capitalism: The fight for a human future at the new frontier of power. Profile Books.

Cited By

Widder D(2024)Epistemic Power in AI Ethics Labor: Legitimizing Located ComplaintsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658973(1295-1304)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658973
Simson JFabris AKern C(2024)Lazy Data Practices Harm Fairness ResearchProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658931(642-659)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658931
Vrijenhoek SDaniil SSandel JHollink L(2024)Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658926(573-584)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658926
Show More Cited By

Index Terms

The Forgotten Margins of AI Ethics

Index terms have been assigned to the content through auto-classification.

Recommendations

Automated Kantian Ethics: A Faithful Implementation
KI 2022: Advances in Artificial Intelligence
Abstract
As we grant artificial intelligence increasing power and independence in contexts like healthcare, policing, and driving, AI faces moral dilemmas but lacks the tools to solve them. Warnings from regulators, philosophers, and computer scientists ...
Read More
Invoking politics and ethics in the design of information technology: undesigning the design

It is a truism that the design and deployment of information and communication technologies is vital to everyday life, the conduct of work and to social order. But how are individual, organisational and societal choices made? What might it mean to ...
Read More
Examining Responsibility and Deliberation in AI Impact Statements and Ethics Reviews
AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

The artificial intelligence research community is continuing to grapple with the ethics of its work by encouraging researchers to discuss potential positive and negative consequences. Neural Information Processing Systems (NeurIPS), a top-tier ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

June 2022

2351 pages

ISBN:9781450393522

DOI:10.1145/3531146

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

ACM: Association for Computing Machinery

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2022

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

National Science Foundation
Science Foundation Ireland

Conference

FAccT '22

Sponsor:

ACM

FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency

June 21 - 24, 2022

Seoul, Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

37
Total Citations
View Citations
3,963
Total Downloads

Downloads (Last 12 months)2,012
Downloads (Last 6 weeks)197

Other Metrics

View Author Metrics

Citations

Cited By

Widder D(2024)Epistemic Power in AI Ethics Labor: Legitimizing Located ComplaintsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658973(1295-1304)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658973
Simson JFabris AKern C(2024)Lazy Data Practices Harm Fairness ResearchProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658931(642-659)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658931
Vrijenhoek SDaniil SSandel JHollink L(2024)Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658926(573-584)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658926
Klein LD'Ignazio C(2024)Data Feminism for AIProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658543(100-112)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658543
Shelby RRismani SRostamzadeh N(2024)Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-ReductionProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642461(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642461
Hanschke VRees DAlanyali MHopkinson DMarshall P(2024)Data Ethics Emergency Drill: A Toolbox for Discussing Responsible AI for Industry TeamsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642402(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642402
Joyce DKormilitzin AHamer-Hunt JMcKee KTomasev N(2024)Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi studyBMJ Open10.1136/bmjopen-2023-07910514:3(e079105)Online publication date: 15-Mar-2024
https://doi.org/10.1136/bmjopen-2023-079105
Jafari E(2024)Artificial intelligence and learning environment: Human considerationsJournal of Computer Assisted Learning10.1111/jcal.13011Online publication date: 21-May-2024
https://doi.org/10.1111/jcal.13011
Birhane ASteed ROjewale VVecchione BRaji I(2024)AI auditing: The Broken Bus on the Road to AI Accountability2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)10.1109/SaTML59370.2024.00037(612-643)Online publication date: 9-Apr-2024
https://doi.org/10.1109/SaTML59370.2024.00037
Moses MGipson Rankin S(2024)Medical artificial intelligence should do no harmNature Reviews Electrical Engineering10.1038/s44287-024-00049-21:5(280-281)Online publication date: 12-Apr-2024
https://doi.org/10.1038/s44287-024-00049-2
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents