no code implementations • 26 Jul 2024 • Mohammmad Tahaei, Daricia Wilkinson, Alisa Frik, Michael Muller, Ruba Abu-Salma, Lauren Wilcox
Calls for engagement with the public in Artificial Intelligence (AI) research, development, and governance are increasing, leading to the use of surveys to capture people's values, perceptions, and experiences related to AI.
no code implementations • 25 Jan 2024 • Justin D. Weisz, Jessica He, Michael Muller, Gabriela Hoefer, Rachel Miles, Werner Geyer
Generative AI applications present unique design challenges.
no code implementations • 16 Feb 2023 • Mohammad Tahaei, Marios Constantinides, Daniele Quercia, Sean Kennedy, Michael Muller, Simone Stumpf, Q. Vera Liao, Ricardo Baeza-Yates, Lora Aroyo, Jess Holbrook, Ewa Luger, Michael Madaio, Ilana Golbin Blumenfeld, Maria De-Arteaga, Jessica Vitak, Alexandra Olteanu
In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence.
no code implementations • 10 Feb 2023 • Mohammad Tahaei, Marios Constantinides, Daniele Quercia, Michael Muller
In this paper, we present a bottom-up mapping of the current state of research at the intersection of Human-Centered AI, Ethical, and Responsible AI (HCER-AI) by thematically reviewing and analyzing 164 research papers from leading conferences in ethical, social, and human factors of AI: AIES, CHI, CSCW, and FAccT.
no code implementations • 13 Jan 2023 • Justin D. Weisz, Michael Muller, Jessica He, Stephanie Houde
We anticipate these principles to usefully inform design decisions made in the creation of novel human-AI applications, and we invite the community to apply, revise, and extend these principles to their own work.
no code implementations • 13 Jan 2023 • Steven I. Ross, Michael Muller, Fernando Martinez, Stephanie Houde, Justin D. Weisz
The Programmer's Assistant is an experimental prototype software development environment that integrates a chatbot with a code editor.
no code implementations • 10 Feb 2022 • Jiao Sun, Q. Vera Liao, Michael Muller, Mayank Agarwal, Stephanie Houde, Kartik Talamadupula, Justin D. Weisz
Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion.
no code implementations • 11 Oct 2021 • Mayank Agarwal, Kartik Talamadupula, Fernando Martinez, Stephanie Houde, Michael Muller, John Richards, Steven I Ross, Justin D. Weisz
However, due to the paucity of parallel data in this domain, supervised techniques have only been applied to a limited set of popular programming languages.
no code implementations • 28 Jul 2021 • Upol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, Mark O. Riedl
Explainability of AI systems is critical for users to take informed actions.
no code implementations • 13 Jan 2021 • David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, Felix Portnoy
The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team.
no code implementations • 12 Jan 2021 • Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, Justin D. Weisz
We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST's effect and implications at the technical, decision-making, and organizational level.
no code implementations • 7 Jan 2021 • Dakuo Wang, Q. Vera Liao, Yunfeng Zhang, Udayan Khurana, Horst Samulowitz, Soya Park, Michael Muller, Lisa Amini
There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.
no code implementations • 18 Jan 2020 • Amy X. Zhang, Michael Muller, Dakuo Wang
We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use.
no code implementations • 17 Jan 2020 • Jaimie Drozdal, Justin Weisz, Dakuo Wang, Gaurav Dass, Bingsheng Yao, Changruo Zhao, Michael Muller, Lin Ju, Hui Su
We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML).
no code implementations • 13 Dec 2019 • Daniel Karl I. Weidele, Justin D. Weisz, Eno Oduor, Michael Muller, Josh Andres, Alexander Gray, Dakuo Wang
Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow.
no code implementations • 13 Dec 2019 • Q. Vera Liao, Michael Muller
Two general routes have been followed to develop artificial agents that are sensitive to human values---a top-down approach to encode values into the agents, and a bottom-up approach to learn from human actions, whether from real-world interactions or stories.
no code implementations • 8 Sep 2019 • Yaoli Mao, Dakuo Wang, Michael Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, AleksandraMojsilović
Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process.
no code implementations • 5 Sep 2019 • Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, Alexander Gray
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways.