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Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways.
How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?
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.
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates
Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow.
Enabling Value Sensitive AI Systems through Participatory Design Fictions
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.
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems
We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML).
How do Data Science Workers Collaborate? Roles, Workflows, and Tools
We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use.
How Much Automation Does a Data Scientist Want?
There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.
Expanding Explainability: Towards Social Transparency in AI systems
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.
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
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.
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Explainability of AI systems is critical for users to take informed actions.
Using Document Similarity Methods to create Parallel Datasets for Code Translation
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.
Investigating Explainability of Generative AI for Code through Scenario-based Design
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.
Toward General Design Principles for Generative AI Applications
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
The Programmer's Assistant is an experimental prototype software development environment that integrates a chatbot with a code editor.
A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI
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.
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence.
Design Principles for Generative AI Applications
Generative AI applications present unique design challenges.
