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06/02/2023
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
The rise of powerful large language models (LLMs) brings about tremendou...
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06/01/2023
Rethinking Model Evaluation as Narrowing the Socio-Technical Gap
The recent development of generative and large language models (LLMs) po...
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05/24/2023
Evaluating NLG Evaluation Metrics: A Measurement Theory Perspective
We address the fundamental challenge in Natural Language Generation (NLG...
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04/17/2023
Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling
Data storytelling plays an important role in data workers' daily jobs si...
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04/17/2023
Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding
Qualitative analysis of textual contents unpacks rich and valuable infor...
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03/13/2023
Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders
With the heightened digitization of the workplace, alongside the rise of...
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02/22/2023
fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks
To design with AI models, user experience (UX) designers must assess the...
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02/21/2023
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
Despite the widespread use of artificial intelligence (AI), designing us...
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02/16/2023
Human-Centered Responsible Artificial Intelligence: Current Future Trends
In recent years, the CHI community has seen significant growth in resear...
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02/14/2023
Generation Probabilities Are Not Enough: Exploring the Effectiveness of Uncertainty Highlighting in AI-Powered Code Completions
Large-scale generative models enabled the development of AI-powered code...
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01/25/2023
Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access
During a public health crisis like the COVID-19 pandemic, a credible and...
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01/23/2023
Selective Explanations: Leveraging Human Input to Align Explainable AI
While a vast collection of explainable AI (XAI) algorithms have been dev...
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01/18/2023
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
AI explanations are often mentioned as a way to improve human-AI decisio...
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11/12/2022
Seamful XAI: Operationalizing Seamful Design in Explainable AI
Mistakes in AI systems are inevitable, arising from both technical limit...
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06/22/2022
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
Recent years have seen a surge of interest in the field of explainable A...
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04/29/2022
Designing for Responsible Trust in AI Systems: A Communication Perspective
Current literature and public discourse on "trust in AI" are often focus...
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04/25/2022
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Despite impressive performance in many benchmark datasets, AI models can...
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02/10/2022
Investigating Explainability of Generative AI for Code through Scenario-based Design
What does it mean for a generative AI model to be explainable? The emerg...
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12/21/2021
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
As AI systems demonstrate increasingly strong predictive performance, th...
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10/20/2021
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
As a technical sub-field of artificial intelligence (AI), explainable AI...
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09/24/2021
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increa...
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07/28/2021
The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations
Explainability of AI systems is critical for users to take informed acti...
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06/02/2021
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI
In this paper, we describe an open source Python toolkit named Uncertain...
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04/09/2021
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Automated Machine Learning (AutoML) is a rapidly growing set of technolo...
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04/08/2021
Question-Driven Design Process for Explainable AI User Experiences
A pervasive design issue of AI systems is their explainability–how to pr...
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03/27/2021
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
Given that there are a variety of stakeholders involved in, and affected...
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01/29/2021
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Data scientists face a steep learning curve in understanding a new domai...
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01/12/2021
Expanding Explainability: Towards Social Transparency in AI systems
As AI-powered systems increasingly mediate consequential decision-making...
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01/07/2021
How Much Automation Does a Data Scientist Want?
Data science and machine learning (DS/ML) are at the heart of the recent...
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11/15/2020
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Transparency of algorithmic systems entails exposing system properties t...
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09/06/2020
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation
We propose a new active learning (AL) framework, Active Learning++, whic...
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04/04/2020
Measuring Social Biases of Crowd Workers using Counterfactual Queries
Social biases based on gender, race, etc. have been shown to pollute mac...
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01/24/2020
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
Active Learning (AL) is a human-in-the-loop Machine Learning paradigm fa...
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01/08/2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
A surge of interest in explainable AI (XAI) has led to a vast collection...
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01/07/2020
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
Today, AI is being increasingly used to help human experts make decision...
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12/13/2019
Enabling Value Sensitive AI Systems through Participatory Design Fictions
Two general routes have been followed to develop artificial agents that ...
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09/06/2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
As artificial intelligence and machine learning algorithms make further ...
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05/25/2019
Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys
The rise of increasingly more powerful chatbots offers a new way to coll...
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01/23/2019
Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
Ensuring fairness of machine learning systems is a human-in-the-loop pro...
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12/14/2018
Bootstrapping Conversational Agents With Weak Supervision
Many conversational agents in the market today follow a standard bot dev...
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08/04/2017