Using model cards for ethical reflection: a qualitative exploration
Resumo
Various representations have been proposed to document machine learning models. In this paper, we analyze how developers make use of one such tool, the Model Card, in ethical reflection. The work is part of a broader research project about epistemic tools for the design of artificial intelligence systems. We conducted a qualitative study based on speculative design sessions. Participants were asked to imagine that they were responsible for the development of an artificial intelligence model in two distinct scenarios: loan applications and university admissions. Regarding Model Cards, the focus of this paper, a thematic analysis of the data suggests that participating developers were selective about which of the ethical issues they reflected upon were actually recorded in their cards. However, participants were hesitant to grant full autonomy to the model they were developing, a contrast with previous studies. These findings may contribute to our current understanding of how developers can leverage epistemic and documentation tools to engage in a more ethically informed design process of artificial intelligence systems.
Palavras-chave:
Model Cards, ethical reasoning, responsible design
Referências
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Simone Diniz Junqueira Barbosa, Gabriel Diniz Junqueira Barbosa, Clarisse Sieckenius de Souza, and Carla Faria Leitão. 2021. A Semiotics-Based Epistemic Tool to Reason about Ethical Issues in Digital Technology Design and Development. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 363--374.
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Emily M. Bender and Batya Friedman. 2018. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics 6 (Dec. 2018), 587--604.
Rafael Brandão, Joel Carbonera, Clarisse de Souza, Juliana Ferreira, Bernardo Gonçalves, and Carla Leitão. 2019. Mediation Challenges and Socio-Technical Gaps for Explainable Deep Learning Applications. http://arxiv.org/abs/1907.07178 arXiv: 1907.07178.
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Antony Bryant. 2021. Continual Permutations of Misunderstanding: The Curious Incidents of the Grounded Theory Method. Qualitative Inquiry 27, 3--4 (2021), 397--411. arXiv: https://doi.org/10.1177/1077800420920663
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John L. Campbell, Charles Quincy, Jordan Osserman, and Ove K. Pedersen. 2013. Coding In-Depth Semistructured Interviews: Problems of Unitization and Inter-coder Reliability and Agreement. Sociological Methods & Research 42, 3 (Aug. 2013), 294--320.
Clarisse Sieckenius de Souza, Renato Fontoura de Gusmão Cerqueira, Luiz Marques Afonso, and Juliana Soares Jansen Ferreira. 2016. Software Developers as Users. Semiotic Investigations on Human-Centered Software Development. Springer International, Cham, Switzreland.
Virginia Eubanks. 2018. Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press, Inc., USA.
Luciano Floridi and Josh Cowls. 2019. A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review 1, 1 (July 2019), 1--15.
Yiannis Gabriel. 2018. Interpretation, Reflexivity and Imagination in Qualitative Research. In Qualitative Methodologies in Organization Studies: Volume I: Theories and New Approaches, Malgorzata Ciesielska and Dariusz Jemielniak (Eds.). Springer International Publishing, Cham, 137--157.
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João Queiroz and Floyd Merrell. 2005. Abduction: Between subjectivity and objectivity (Special Issue edited by Queiroz & Merrell). Semiotica 153, 1-4 (2005), 1--466.
Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 33--44.
Jo Reichertz. 2009. Abduction: The Logic of Discovery of Grounded Theory. FQS 11, 1 (Nov. 2009), 1--16.
Kerry Earl Rinehart. 2021. Abductive Analysis in Qualitative Inquiry. Qualitative Inquiry 27, 2 (2021), 303--311. arXiv: https://doi.org/10.1177/1077800420935912
Hong Shen, Wesley H. Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, and Haiyi Zhu. 2021. Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 850--861.
José Antonio Siqueira De Cerqueira, Anayran Pinheiro De Azevedo, Heloise Acco Tives, and Edna Dias Canedo. 2022. Guide for Artificial Intelligence Ethical Requirements Elicitation - RE4AI Ethical Guide. In Hawaii International Conference on System Sciences.
Matthew Talbert. 2019. Moral Responsibility. In The Stanford Encyclopedia of Philosophy (winter 2019 ed.), Edward N. Zalta (Ed.). Metaphysics Research Lab, Stanford University, Online. [link]
Iddo Tavory and Stefan Timmermans. 2014. Abductive Analysis: Theorizing Qualitative Research (illustrated edition ed.). University of Chicago Press, Chicago.
Ville Vakkuri, Kai-Kristian Kemell, Marianna Jantunen, Erika Halme, and Pekka Abrahamsson. 2021. ECCOLA --- A Method for Implementing Ethically Aligned AI Systems. Journal of Systems and Software 182 (Dec. 2021), 111067.
Luis Vila-Henninger, Claire Dupuy, Virginie Van Ingelgom, Mauro Caprioli, Ferdinand Teuber, Damien Pennetreau, Margherita Bussi, and Cal Le Gall. 2022. Abductive Coding: Theory Building and Qualitative (Re)Analysis. Sociological Methods & Research sn (Feb. 2022), 00491241211067508. arXiv: https://doi.org/10.1177/00491241211067508
Maranke Wieringa. 2020. What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, Barcelona, Spain, 1--18.
Simone Diniz Junqueira Barbosa, Gabriel Diniz Junqueira Barbosa, Clarisse Sieckenius de Souza, and Carla Faria Leitão. 2021. A Semiotics-Based Epistemic Tool to Reason about Ethical Issues in Digital Technology Design and Development. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 363--374.
Tom L. Beauchamp and James F. Childress. 2019. Principles of Biomedical Ethics (8th edition ed.). Oxford University Press, New York.
Emily M. Bender and Batya Friedman. 2018. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics 6 (Dec. 2018), 587--604.
Rafael Brandão, Joel Carbonera, Clarisse de Souza, Juliana Ferreira, Bernardo Gonçalves, and Carla Leitão. 2019. Mediation Challenges and Socio-Technical Gaps for Explainable Deep Learning Applications. http://arxiv.org/abs/1907.07178 arXiv: 1907.07178.
Virginia Braun and Victoria Clarke. 2012. Thematic analysis. In APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological., Harris Cooper, Paul M. Camic, Debra L. Long, A. T. Panter, David Rindskopf, and Kenneth J. Sher (Eds.). American Psychological Association, Washington, 57--71.
Antony Bryant. 2002. Re-grounding Grounded Theory. The Journal of Information Technology Theory and Application (JITTA) 4, 1 (2002), 25--42. https://aisel.aisnet.org/jitta/vol4/iss1/7 The last issue of JITTA appeared by the end of 2018.
Antony Bryant. 2017. Grounded Theory and Grounded Theorizing: Pragmatism in Research Practice. Oxford University Press, New York. 432 pages.
Antony Bryant. 2021. Continual Permutations of Misunderstanding: The Curious Incidents of the Grounded Theory Method. Qualitative Inquiry 27, 3--4 (2021), 397--411. arXiv: https://doi.org/10.1177/1077800420920663
Charmaz Kathy Bryant, Antony. 2019. The SAGE handbook of current developments in grounded theory. Sage, Thousand Oaks, California. [link].
John L. Campbell, Charles Quincy, Jordan Osserman, and Ove K. Pedersen. 2013. Coding In-Depth Semistructured Interviews: Problems of Unitization and Inter-coder Reliability and Agreement. Sociological Methods & Research 42, 3 (Aug. 2013), 294--320.
Clarisse Sieckenius de Souza, Renato Fontoura de Gusmão Cerqueira, Luiz Marques Afonso, and Juliana Soares Jansen Ferreira. 2016. Software Developers as Users. Semiotic Investigations on Human-Centered Software Development. Springer International, Cham, Switzreland.
Virginia Eubanks. 2018. Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press, Inc., USA.
Luciano Floridi and Josh Cowls. 2019. A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review 1, 1 (July 2019), 1--15.
Yiannis Gabriel. 2018. Interpretation, Reflexivity and Imagination in Qualitative Research. In Qualitative Methodologies in Organization Studies: Volume I: Theories and New Approaches, Malgorzata Ciesielska and Dariusz Jemielniak (Eds.). Springer International Publishing, Cham, 137--157.
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2018. Datasheets for Datasets. https://arxiv.org/abs/1803.09010v8
Marilys Guillemin and Lynn Gillam. 2004. Ethics, Reflexivity, and "Ethically Important Moments" in Research. Qualitative Inquiry 10, 2 (2004), 261--280.
Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski. 2018. The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards. http://arxiv.org/abs/1805.03677 arXiv: 1805.03677.
Ben Hutchinson, Andrew Smart, Alex Hanna, Emily Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, and Margaret Mitchell. 2021. Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 560--575.
Professor of Linguistics Robin Tolmach Lakoff. 2001. The Language War (first edição ed.). University of California Press, Berkeley.
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). ACM, New York, NY, USA, 220--229. event-place: Atlanta, GA, USA.
Cathy O'Neil. 2016. Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group, USA.
João Queiroz and Floyd Merrell. 2005. Abduction: Between subjectivity and objectivity (Special Issue edited by Queiroz & Merrell). Semiotica 153, 1-4 (2005), 1--466.
Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 33--44.
Jo Reichertz. 2009. Abduction: The Logic of Discovery of Grounded Theory. FQS 11, 1 (Nov. 2009), 1--16.
Kerry Earl Rinehart. 2021. Abductive Analysis in Qualitative Inquiry. Qualitative Inquiry 27, 2 (2021), 303--311. arXiv: https://doi.org/10.1177/1077800420935912
Hong Shen, Wesley H. Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, and Haiyi Zhu. 2021. Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). Association for Computing Machinery, New York, NY, USA, 850--861.
José Antonio Siqueira De Cerqueira, Anayran Pinheiro De Azevedo, Heloise Acco Tives, and Edna Dias Canedo. 2022. Guide for Artificial Intelligence Ethical Requirements Elicitation - RE4AI Ethical Guide. In Hawaii International Conference on System Sciences.
Matthew Talbert. 2019. Moral Responsibility. In The Stanford Encyclopedia of Philosophy (winter 2019 ed.), Edward N. Zalta (Ed.). Metaphysics Research Lab, Stanford University, Online. [link]
Iddo Tavory and Stefan Timmermans. 2014. Abductive Analysis: Theorizing Qualitative Research (illustrated edition ed.). University of Chicago Press, Chicago.
Ville Vakkuri, Kai-Kristian Kemell, Marianna Jantunen, Erika Halme, and Pekka Abrahamsson. 2021. ECCOLA --- A Method for Implementing Ethically Aligned AI Systems. Journal of Systems and Software 182 (Dec. 2021), 111067.
Luis Vila-Henninger, Claire Dupuy, Virginie Van Ingelgom, Mauro Caprioli, Ferdinand Teuber, Damien Pennetreau, Margherita Bussi, and Cal Le Gall. 2022. Abductive Coding: Theory Building and Qualitative (Re)Analysis. Sociological Methods & Research sn (Feb. 2022), 00491241211067508. arXiv: https://doi.org/10.1177/00491241211067508
Maranke Wieringa. 2020. What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, Barcelona, Spain, 1--18.
Publicado
17/10/2022
Como Citar
NUNES, José Luiz; D. J. BARBOSA, Gabriel; SIECKENIUS DE SOUZA, Clarisse; LOPES, Hélio; D. J. BARBOSA, Simone.
Using model cards for ethical reflection: a qualitative exploration. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 21. , 2022, Diamantina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
