HCI.TOPHCI, made easy

arrow_backBack to questions View categoryarrow_forward

AI Explainability, Trust, and Calibration / Transparency, Auditability, and Trust Calibration Mechanisms

Users lack trust in AI tools, especially in high-risk HR domains.

lightbulbPractical problemTransparency, Auditability, and Trust Calibration Mechanisms

Users lack trust in AI tools, especially in high-risk HR domains.

CSCW '24The Role of Algorithmic Audits and Other Soft Law Approaches in Informing Users' Calibrated Trust in Artificial Intelligence Tools

Similar questions

lightbulbPractical problemTransparency, Auditability, and Trust Calibration Mechanisms

Urban residents have low trust in 311 systems, with fragmented reports and inefficient service.

CHI '25Design for Civic Quality of Life Things

lightbulbPractical problemTransparency, Auditability, and Trust Calibration Mechanisms

Users easily mistake language models for having human traits, leading to over-trust or misunderstanding.

CHI '25A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies

lightbulbPractical problemTransparency, Auditability, and Trust Calibration Mechanisms

Users lack clarity on how social media processes their data in real time, causing privacy and trust issues.

CHI '25"Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications

lightbulbPractical problemTransparency, Auditability, and Trust Calibration Mechanisms

Designers struggle to quickly and efficiently optimize the effectiveness of generative task prompts.

CHI '24EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria

lightbulbPractical problemTransparency, Auditability, and Trust Calibration Mechanisms

Scholars struggle to assess literature credibility and reproducibility, especially novices or interdisciplinary researchers.

CHI '24Integrating measures of replicability into scholarly search: Challenges and opportunities

lightbulbPractical problemTransparency, Auditability, and Trust Calibration Mechanisms

Users have complex ethical reactions to highly anthropomorphic AI, affecting trust and interaction.

CHI '24Which Artificial Intelligences Do People Care About Most? A Conjoint Experiment on Moral Consideration

Related papers

Design for Civic Quality of Life Things

Graham Dove, Eric Corbett

A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies

Alicia DeVrio, Myra Cheng, Lisa Egede

"Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications

Jack West, Bengisu Cagiltay, Shirley Zhang

EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria

Taesu Kim, Yoonjoo Lee, Jamin Shin

Integrating measures of replicability into scholarly search: Challenges and opportunities

Chuhao Wu, Tatiana Chakravorti, John M Carroll

Which Artificial Intelligences Do People Care About Most? A Conjoint Experiment on Moral Consideration

Ali Ladak, Jamie Harris, Jacy Reese Anthis

AQX: Explaining Air Quality Forecast for Verifying Domain Knowledge using Feature Importance Visualization

Reshika Palaniyappan Velumani, Meng Xia, Jun Han

Templates and Trust-o-meters: Towards a widely deployable indicator of trust in Wikipedia

Andrew Kuznetsov, Margeigh Novotny, Jessica Klein

Manipulating and Measuring Model Interpretability

Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman

Cody: An AI-Based System to Semi-Automate Coding for Qualitative Research

Tim Rietz, Alexander Maedche

Expanding Explainability: Towards Social Transparency in AI systems

Upol Ehsan, Q. Vera Liao, Michael Muller

User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics

Anamaria Crisan, Michael Correll