Search Results for author:
Found 27 papers, 2 papers with code
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
We argue that one way to close the gap is to develop evaluation methods that account for different user requirements in these usage contexts.
Designing for Responsible Trust in AI Systems: A Communication Perspective
Current literature and public discourse on "trust in AI" are often focused on the principles underlying trustworthy AI, with insufficient attention paid to how people develop trust.
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples.
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.
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Besides developing AI technologies for this purpose, the emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI to make decisions.
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
In this chapter, we begin with a high-level overview of the technical landscape of XAI algorithms, then selectively survey our own and other recent HCI works that take human-centered approaches to design, evaluate, and provide conceptual and methodological tools for XAI.
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations
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.
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 Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models.
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models.
Question-Driven Design Process for Explainable AI User Experiences
A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI.
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models.
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 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.
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders.
Agent Assist through Conversation Analysis
Customer support agents play a crucial role as an interface between an organization and its end-users.
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation
The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model.
Measuring Social Biases of Crowd Workers using Counterfactual Queries
Social biases based on gender, race, etc.
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
We conducted an empirical study comparing the model learning outcomes, feedback content and experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation).
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 of algorithmic work on the topic.
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
In these scenarios, full automation is often undesirable, not only due to the significance of the outcome, but also because human experts can draw on their domain knowledge complementary to the model's to ensure task success.
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.
Doc2Dial: a Framework for Dialogue Composition Grounded in Business Documents
We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in business documents via crowdsourcing.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions
The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user's free-text responses, and probes answers whenever needed.
Bootstrapping Conversational Agents With Weak Supervision
We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling.
A Measure for Dialog Complexity and its Application in Streamlining Service Operations
Dialog is a natural modality for interaction between customers and businesses in the service industry.
