Human-Centered Transparency & Intelligibility in AI

出版物

表示方法:

- “I’m Not Sure, But…”: Examining the Impact of Large Language Models’ Uncertainty Expression on User Reliance and Trust
  
  Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
  
  7th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024) | June 2024
  
  Publication
- AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
  
  Q. Vera Liao, Jennifer Wortman Vaughan
  
  Harvard Data Science Review | February 2024
  
  Publication
- Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
  
  Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
  
  Proc. ACM Hum.-Comput. Interact. | October 2023, 巻7
  
  Article 370
  
  PDF
- Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
  
  Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
  
  2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | April 2023
  
  PDF
- GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse
  
  Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
  
  2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | February 2023
  
  PDF
- Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
  
  Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, Jennifer Wortman Vaughan
  
  2022 Conference on Computer Supported Cooperative Work | November 2022
  
  Publication プロジェクト
- Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
  
  Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
  
  AAAI Conference on Human Computation and Crowdsourcing | October 2022
  
  Best Paper Award, Honorable Mention
  
  Publication
- Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
  
  Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
  
  KDD 2022 | July 2022
  
  Publication
- REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
  
  Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, Jennifer Wortman Vaughan
  
  FAccT 2022 | May 2022
  
  DOI
- A Human-Centered Agenda for Intelligible Machine Learning
  
  Jennifer Wortman Vaughan, Hanna Wallach
  
  In Machines We Trust: Perspectives on Dependable AI | MIT Press による投稿 | 2021
  
  Author's Version
- Datasheets for Datasets
  
  Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
  
  Communications of the ACM | December 2021, 巻64(12): pp. 86-92
  
  Publication プロジェクト
- Summarize with Caution: Comparing Global Feature Attributions
  
  Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna Wallach, Jennifer Wortman Vaughan
  
  Bulletin of the IEEE Computer Society Technical Committee on Data Engineering | December 2021
  
  Publication
- From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
  
  David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, Jennifer Wortman Vaughan
  
  HCOMP 2021 | September 2021
  
  Publication
- Manipulating and Measuring Model Interpretability
  
  Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
  
  CHI 2021 | May 2021
  
  Publication
- The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
  
  Solon Barocas, Andrew Selbst, Manish Raghavan
  
  FAT* 2020 | August 2020
  
  Publication
- Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
  
  Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
  
  CHI 2020 | April 2020
  
  CHI 2020 Honorable Mention Award
  
  Publication Github
- Understanding the Effect of Accuracy on Trust in Machine Learning Models
  
  Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
  
  2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
  
  Honorable Mention Award at CHI 2019
  
  Publication

- “I’m Not Sure, But…”: Examining the Impact of Large Language Models’ Uncertainty Expression on User Reliance and Trust
  
  Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
  
  7th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024) | June 2024
  
  Publication
- AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
  
  Q. Vera Liao, Jennifer Wortman Vaughan
  
  Harvard Data Science Review | February 2024
  
  Publication
- Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
  
  Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
  
  Proc. ACM Hum.-Comput. Interact. | October 2023, 巻7
  
  Article 370
  
  PDF
- Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
  
  Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
  
  2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | April 2023
  
  PDF
- GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse
  
  Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
  
  2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | February 2023
  
  PDF
- Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
  
  Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, Jennifer Wortman Vaughan
  
  2022 Conference on Computer Supported Cooperative Work | November 2022
  
  Publication プロジェクト
- Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
  
  Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
  
  AAAI Conference on Human Computation and Crowdsourcing | October 2022
  
  Best Paper Award, Honorable Mention
  
  Publication
- Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
  
  Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
  
  KDD 2022 | July 2022
  
  Publication
- REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
  
  Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, Jennifer Wortman Vaughan
  
  FAccT 2022 | May 2022
  
  DOI
- A Human-Centered Agenda for Intelligible Machine Learning
  
  Jennifer Wortman Vaughan, Hanna Wallach
  
  In Machines We Trust: Perspectives on Dependable AI | MIT Press による投稿 | 2021
  
  Author's Version
- Summarize with Caution: Comparing Global Feature Attributions
  
  Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna Wallach, Jennifer Wortman Vaughan
  
  Bulletin of the IEEE Computer Society Technical Committee on Data Engineering | December 2021
  
  Publication
- Datasheets for Datasets
  
  Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
  
  Communications of the ACM | December 2021, 巻64(12): pp. 86-92
  
  Publication プロジェクト
- From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
  
  David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, Jennifer Wortman Vaughan
  
  HCOMP 2021 | September 2021
  
  Publication
- Manipulating and Measuring Model Interpretability
  
  Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
  
  CHI 2021 | May 2021
  
  Publication
- The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
  
  Solon Barocas, Andrew Selbst, Manish Raghavan
  
  FAT* 2020 | August 2020
  
  Publication
- Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
  
  Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
  
  CHI 2020 | April 2020
  
  CHI 2020 Honorable Mention Award
  
  Publication Github
- Understanding the Effect of Accuracy on Trust in Machine Learning Models
  
  Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
  
  2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
  
  Honorable Mention Award at CHI 2019
  
  Publication
- AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
  
  Q. Vera Liao, Jennifer Wortman Vaughan
  
  Harvard Data Science Review | February 2024
  
  Publication
- Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
  
  Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
  
  Proc. ACM Hum.-Comput. Interact. | October 2023, 巻7
  
  Article 370
  
  PDF
- Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
  
  Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
  
  2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | April 2023
  
  PDF
- GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse
  
  Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
  
  2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | February 2023
  
  PDF
- Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
  
  Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, Jennifer Wortman Vaughan
  
  2022 Conference on Computer Supported Cooperative Work | November 2022
  
  Publication プロジェクト
- Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
  
  Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
  
  AAAI Conference on Human Computation and Crowdsourcing | October 2022
  
  Best Paper Award, Honorable Mention
  
  Publication
- Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
  
  Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
  
  CHI 2020 | April 2020
  
  CHI 2020 Honorable Mention Award
  
  Publication Github
- Understanding the Effect of Accuracy on Trust in Machine Learning Models
  
  Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
  
  2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
  
  Honorable Mention Award at CHI 2019
  
  Publication
- Manipulating and Measuring Model Interpretability
  
  Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
  
  CHI 2021 | May 2021
  
  Publication

- “I’m Not Sure, But…”: Examining the Impact of Large Language Models’ Uncertainty Expression on User Reliance and Trust
  
  Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
  
  7th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024) | June 2024
  
  Publication
- Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
  
  Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
  
  2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | April 2023
  
  PDF
- GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse
  
  Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
  
  2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | February 2023
  
  PDF
- Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
  
  Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, Jennifer Wortman Vaughan
  
  2022 Conference on Computer Supported Cooperative Work | November 2022
  
  Publication プロジェクト
- Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
  
  Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar
  
  AAAI Conference on Human Computation and Crowdsourcing | October 2022
  
  Best Paper Award, Honorable Mention
  
  Publication
- Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
  
  Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
  
  KDD 2022 | July 2022
  
  Publication
- REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
  
  Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, Jennifer Wortman Vaughan
  
  FAccT 2022 | May 2022
  
  DOI
- From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
  
  David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, Jennifer Wortman Vaughan
  
  HCOMP 2021 | September 2021
  
  Publication
- Manipulating and Measuring Model Interpretability
  
  Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
  
  CHI 2021 | May 2021
  
  Publication
- The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
  
  Solon Barocas, Andrew Selbst, Manish Raghavan
  
  FAT* 2020 | August 2020
  
  Publication
- Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
  
  Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
  
  CHI 2020 | April 2020
  
  CHI 2020 Honorable Mention Award
  
  Publication Github
- Understanding the Effect of Accuracy on Trust in Machine Learning Models
  
  Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
  
  2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
  
  Honorable Mention Award at CHI 2019
  
  Publication
- AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
  
  Q. Vera Liao, Jennifer Wortman Vaughan
  
  Harvard Data Science Review | February 2024
  
  Publication
- Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
  
  Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
  
  Proc. ACM Hum.-Comput. Interact. | October 2023, 巻7
  
  Article 370
  
  PDF
- Summarize with Caution: Comparing Global Feature Attributions
  
  Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna Wallach, Jennifer Wortman Vaughan
  
  Bulletin of the IEEE Computer Society Technical Committee on Data Engineering | December 2021
  
  Publication
- Datasheets for Datasets
  
  Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
  
  Communications of the ACM | December 2021, 巻64(12): pp. 86-92
  
  Publication プロジェクト
- A Human-Centered Agenda for Intelligible Machine Learning
  
  Jennifer Wortman Vaughan, Hanna Wallach
  
  In Machines We Trust: Perspectives on Dependable AI | MIT Press による投稿 | 2021
  
  Author's Version