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Papers

Browse and search HCI research papers from All

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Author: 2502

Results: 17

Toward More Ethical and Transparent Systems and Environments

AdRecommended

Learn AI Coding at CodeNow

Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work.

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Session 3a: Unpacking User Interpretation and System Design

Session 1g: Contextualizing Fairness in AI

XAI 2

Data, Bias and Fairness

Data, Bias and Fairness; Data, Bias and Fairness

Paper Title	Authors	Research Topics
	SN
emoji_events	WD	Toward More Ethical and Transparent Systems and Environments
	KF
emoji_events	SK
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
	NG	Session 3a: Unpacking User Interpretation and System Design
	MM	Session 1g: Contextualizing Fairness in AI
	VC	XAI 2
	ZW
	QL
	AH	Data, Bias and Fairness
	AH	Data, Bias and Fairness
	MM	Data, Bias and Fairness; Data, Bias and Fairness
	FP
emoji_events	MM
emoji_events	HK
emoji_events	MY
	KH

All

Papers

Browse and search HCI research papers from All

Active Filters

Author: 2502

Results: 17

Toward More Ethical and Transparent Systems and Environments

AdRecommended

Learn AI Coding at CodeNow

Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work.

Explore Now

Session 3a: Unpacking User Interpretation and System Design

Session 1g: Contextualizing Fairness in AI

XAI 2

Data, Bias and Fairness

Data, Bias and Fairness; Data, Bias and Fairness

Paper Title	Authors	Research Topics
	SN
emoji_events	WD	Toward More Ethical and Transparent Systems and Environments
	KF
emoji_events	SK
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
	NG	Session 3a: Unpacking User Interpretation and System Design
	MM	Session 1g: Contextualizing Fairness in AI
	VC	XAI 2
	ZW
	QL
	AH	Data, Bias and Fairness
	AH	Data, Bias and Fairness
	MM	Data, Bias and Fairness; Data, Bias and Fairness
	FP
emoji_events	MM
emoji_events	HK
emoji_events	MY
	KH

Papers

“Helping Me Versus Doing It for Me”: Designing for Agency in LLM-Infused Writing Tools for Science Journalism

Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Work

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies

Learn AI Coding at CodeNow

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

Tinker, Tailor, Configure, Customize: The Articulation Work of Contextualizing an AI Fairness Checklist

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support

Manipulating and Measuring Model Interpretability

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Understanding the Effect of Accuracy on Trust in Machine Learning Models

Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?

“Helping Me Versus Doing It for Me”: Designing for Agency in LLM-Infused Writing Tools for Science Journalism

Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Work

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies

Learn AI Coding at CodeNow

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

Tinker, Tailor, Configure, Customize: The Articulation Work of Contextualizing an AI Fairness Checklist

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support

Manipulating and Measuring Model Interpretability

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Understanding the Effect of Accuracy on Trust in Machine Learning Models

Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?

Papers

“Helping Me Versus Doing It for Me”: Designing for Agency in LLM-Infused Writing Tools for Science Journalism

Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Work

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies

Learn AI Coding at CodeNow

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

Tinker, Tailor, Configure, Customize: The Articulation Work of Contextualizing an AI Fairness Checklist

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support

Manipulating and Measuring Model Interpretability

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Understanding the Effect of Accuracy on Trust in Machine Learning Models

Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?

“Helping Me Versus Doing It for Me”: Designing for Agency in LLM-Infused Writing Tools for Science Journalism

Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Work

Canvil: Designerly Adaptation for LLM-Powered User Experiences

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies

Learn AI Coding at CodeNow

(De)Noise: Moderating the Inconsistency Between Human Decision-Makers

Tinker, Tailor, Configure, Customize: The Articulation Work of Contextualizing an AI Fairness Checklist

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support

Manipulating and Measuring Model Interpretability

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Understanding the Effect of Accuracy on Trust in Machine Learning Models

Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?