Search Results for author: Michael Muller

Found 11 papers, 0 papers with code

Using Document Similarity Methods to create Parallel Datasets for Code Translation

no code implementations11 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.

Code Translation Machine Translation +1

The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations

no code implementations28 Jul 2021 Upol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, Mark O. Riedl

In this paper, we conduct a mixed-methods study of how two different groups of whos--people with and without a background in AI--perceive different types of AI explanations.

How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

no code implementations13 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.

Expanding Explainability: Towards Social Transparency in AI systems

no code implementations12 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.

Decision Making

How Much Automation Does a Data Scientist Want?

no code implementations7 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.

AutoML

How do Data Science Workers Collaborate? Roles, Workflows, and Tools

no code implementations18 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.

Enabling Value Sensitive AI Systems through Participatory Design Fictions

no code implementations13 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.

AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates

no code implementations13 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.

AutoML Feature Engineering

How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?

no code implementations8 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.

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