Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory

10 May 2022

Papers citing "Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory"

29 / 29 papers shown

Title
What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions Somayeh Molaei Lionel P. Robert Nikola Banovic 26 0 0 09 May 2025
AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression Dong Whi Yoo Jiayue Melissa Shi Violeta J. Rodriguez Koustuv Saha AI4MH 54 0 0 26 Apr 2025
Knowledge-Augmented Explainable and Interpretable Learning for Anomaly Detection and Diagnosis Martin Atzmueller Tim Bohne Patricia Windler 85 0 0 28 Nov 2024
Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers Jose A. Guridi Cristobal Cheyre Qian Yang 34 1 0 30 Oct 2024
ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs Hua Shen Tiffany Knearem Reshmi Ghosh Yu-Ju Yang Tanushree Mitra Yun Huang Yun Huang 61 0 0 15 Sep 2024
HCC Is All You Need: Alignment-The Sensible Kind Anyway-Is Just Human-Centered Computing Eric Gilbert 13 2 0 30 Apr 2024
Incremental XAI: Memorable Understanding of AI with Incremental Explanations Jessica Y. Bo Pan Hao Brian Y Lim CLL 39 6 0 10 Apr 2024
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks Kacper Sokol Julia E. Vogt 37 11 0 19 Mar 2024
Farsight: Fostering Responsible AI Awareness During AI Application Prototyping Zijie J. Wang Chinmay Kulkarni Lauren Wilcox Michael Terry Michael A. Madaio 40 43 0 23 Feb 2024
A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations G. Berman Nitesh Goyal Michael A. Madaio ELM 45 20 0 30 Jan 2024
The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice Fernando Delgado Stephen Yang Michael A. Madaio Qian Yang 76 100 0 02 Oct 2023
Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making Timothée Schmude Laura M. Koesten Torsten Moller Sebastian Tschiatschek 38 1 0 26 May 2023
Explaining the ghosts: Feminist intersectional XAI and cartography as methods to account for invisible labour Goda Klumbytė Hannah Piehl Claude Draude 15 1 0 05 May 2023
A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty A. Houston Georgina Cosma 23 1 0 20 Apr 2023
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML Hilde J. P. Weerts Florian Pfisterer Matthias Feurer Katharina Eggensperger Eddie Bergman Noor H. Awad Joaquin Vanschoren Mykola Pechenizkiy B. Bischl Frank Hutter FaML 38 18 0 15 Mar 2023
Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook Teresa Datta John P. Dickerson 34 10 0 10 Mar 2023
Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support Tim Miller 27 120 0 24 Feb 2023
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience Q. V. Liao Hariharan Subramonyam Jennifer Wang Jennifer Wortman Vaughan HAI 33 58 0 21 Feb 2023
On the Impact of Explanations on Understanding of Algorithmic Decision-Making Timothée Schmude Laura M. Koesten Torsten Moller Sebastian Tschiatschek 24 15 0 16 Feb 2023
A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI Mohammad Tahaei Marios Constantinides Daniele Quercia Michael J. Muller AI4TS 54 8 0 10 Feb 2023
Seamful XAI: Operationalizing Seamful Design in Explainable AI Upol Ehsan Q. V. Liao Samir Passi Mark O. Riedl Hal Daumé 30 20 0 12 Nov 2022
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods Kasun Amarasinghe Kit T. Rodolfa Sérgio Jesus Valerie Chen Vladimir Balayan Pedro Saleiro P. Bizarro Ameet Talwalkar Rayid Ghani 27 0 0 24 Jun 2022
Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance Andrew Bell O. Nov Julia Stoyanovich 27 26 0 10 Jun 2022
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence Kacper Sokol Peter A. Flach 39 20 0 29 Dec 2021
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence David Alvarez-Melis Harmanpreet Kaur Hal Daumé Hanna M. Wallach Jennifer Wortman Vaughan FAtt 51 27 0 27 Apr 2021
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects Samir Passi S. Jackson 171 108 0 09 Feb 2020
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 192 742 0 13 Dec 2018
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 232 201 0 06 Jul 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,684 0 28 Feb 2017