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Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications.
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Gender as a Variable in Natural-Language Processing: Ethical Considerations. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing . Association for Computational Linguistics, Valencia, Spain, 1–11
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Nothing Protects Black Women From Dying in Pregnancy and Childbirth
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Clinical information extraction applications: A literature review
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Machine Learning and Natural Language Processing in Mental Health: Systematic Review
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Extraction of Lactation Frames from Drug Labels and LactMed. In Proceedings of the 18th BioNLP Workshop and Shared Task , Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, and Junichi Tsujii (Eds.). Association for Computational Linguistics, Florence, Italy, 191–200
How Different Groups Prioritize Ethical Values for Responsible AI. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 310–323
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Literature-Augmented Clinical Outcome Prediction. In Findings of the Association for Computational Linguistics: NAACL 2022 , Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz (Eds.). Association for Computational Linguistics, Seattle, United States, 438–453
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Participation Is not a Design Fix for Machine Learning. In Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (<conf-loc>, <city>Arlington</city>, <state>VA</state>, <country>USA</country>, </conf-loc>) (EAAMO ’22) . Association for Computing Machinery, New York, NY, USA, Article 1, 6 pages
Mona Sloane, Emanuel Moss, Olaitan Awomolo, and Laura Forlano. 2022 · 2022
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Heath Goodrum, Meghana Gudala, Ankita Misra, and Kirk Roberts. 2019 · 2019
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Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 89
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Enhancing Clinical Concept Extraction with Contextual Embedding
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The Giving Voice to Mothers study: Inequity and mistreatment during pregnancy and childbirth in the United States
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Do no harm: a roadmap for responsible machine learning for health care
Jenna Wiens, Suchi Saria, Mark P. Sendak, Marzyeh Ghassemi, Vincent X. Liu, Finale Doshi-Velez, Kenneth Jung, Katherine A. Heller, David C. Kale, Mohammed Saeed, Pilar N. Ossorio, Sonoo Thadaney-Israni, and Anna Goldenberg. 2019 · 2019
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Challenges and opportunities for public health made possible by advances in natural language processing
Oliver Baclic, Matthew C. Tunis, Kelsey Young, Coraline Doan, Howard Swerdfeger, and Justin Schonfeld. 2020 · 2020
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COVID-19 and Racial/Ethnic Disparities
Monica Webb Hooper, Anna María Nápoles, and Eliseo J Perez-stable. 2020 · 2020
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How Do I Matter? A Review of the Participatory Design Practice with Less Privileged Participants. In Proceedings of the 16th Participatory Design Conference 2020 - Participation(s) Otherwise - Volume 1 (Manizales, Colombia) (PDC ’20) . Association for Computing Machinery, New York, NY, USA, 137–147
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Later among the works it cites.
Upstream Mitigation Is Not
Ryan Steed, Swetasudha Panda, Ari Kobren, and Michael Wick. 2022 · 2022
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Taxonomy of Risks posed by Language Models. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 214–229
Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel. 2022 · 2022
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Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum
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Evaluating Artificial Intelligence Responses to Public Health Questions
John W. Ayers, Zechariah Zhu, Adam Poliak, Eric C. Leas, Mark Dredze, Michael Hogarth, and Davey M. Smith. 2023b · 2023
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A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 7, 16 pages
Amanda Baughan, Xuezhi Wang, Ariel Liu, Allison Mercurio, Jilin Chen, and Xiao Ma. 2023 · 2023
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Prior Hospitalization, Severe Maternal Morbidity, and Pregnancy-Associated Deaths in Massachusetts From 2002 to 2019
Eugene R Declercq, Howard J Cabral, Chia-Ling Liu, Ndidiamaka Amutah-Onukagha, Audra Meadows, Xiaohui Cui, and Hafsatou Diop. 2023 · 2023
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The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (Boston, MA, USA) (EAAMO ’23) . Association for Computing Machinery, New York, NY, USA, Article 37, 23 pages
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Data Feminism
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MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
Scott L Fleming, Alejandro Lozano, William J Haberkorn, Jenelle A Jindal, Eduardo P Reis, Rahul Thapa, Louis Blankemeier, Julian Z Genkins, Ethan Steinberg, Ashwin Nayak, et al · 2023
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Designing Participatory AI: Creative Professionals’ Worries and Expectations about Generative AI. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA ’23) . Association for Computing Machinery, New York, NY, USA, Article 82, 8 pages
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Organizational Governance of Emerging Technologies: AI Adoption in Healthcare. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency . 1396–1417
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The AI revolution in medicine: GPT-4 and beyond
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What’s fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chicago, IL, USA) (FAccT ’23) . Association for Computing Machinery, New York, NY, USA, 1505–1519
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TalkUp: Paving the Way for Understanding Empowering Language. In Findings of the Association for Computational Linguistics: EMNLP 2023 , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 9334–9354
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The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
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Ethical considerations in the early detection of Alzheimer’s disease using speech and AI. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chicago, IL, USA) (FAccT ’23) . Association for Computing Machinery, New York, NY, USA, 1062–1075
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Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chicago, IL, USA) (FAccT ’23) . Association for Computing Machinery, New York, NY, USA, 1599–1608
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Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?
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Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 754, 18 pages
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Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
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Epic, Microsoft bring GPT-4 to EHRs
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In the Name of Fairness: Assessing the Bias in Clinical Record De-identification. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chicago, IL, USA) (FAccT ’23) . Association for Computing Machinery, New York, NY, USA, 123–137
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Towards Conversational Diagnostic AI
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The Media Frames Corpus: Annotations of Frames Across Issues. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) , Chengqing Zong and Michael Strube (Eds.). Association for Computational Linguistics, Beijing, China, 438–444
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