Search Results for author: Michael Muller

Found 18 papers, 0 papers with code

Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance

no code implementations26 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.

Systematic Literature Review

A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI

no code implementations10 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.

Fairness Systematic Literature Review

Toward General Design Principles for Generative AI Applications

no code implementations13 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.

A Case Study in Engineering a Conversational Programming Assistant's Persona

no code implementations13 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.

Chatbot Language Modelling +1

Investigating Explainability of Generative AI for Code through Scenario-based Design

no code implementations10 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.

Code Translation Explainable Artificial Intelligence (XAI)

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

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 Explainable Artificial Intelligence (XAI)

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 Marketing +1

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.

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

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.

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.

scientific discovery

Cannot find the paper you are looking for? You can Submit a new open access paper.