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Fairness, Bias, and Research Governance / Algorithmic Decision Accountability, Contestability, and User Auditing

AI technology may exacerbate social inequality and neglect the rights of marginalized groups.

lightbulbPractical problemAlgorithmic Decision Accountability, Contestability, and User Auditing

AI technology may exacerbate social inequality and neglect the rights of marginalized groups.

CUI '23Risk and Harm: Unpacking Ideologies in the AI Discourse

Similar questions

lightbulbPractical problemAlgorithmic Decision Accountability, Contestability, and User Auditing

When selecting cities through LLMs, marginalized groups are overlooked, exacerbating inequality.

IUI '25Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models

lightbulbPractical problemAlgorithmic Decision Accountability, Contestability, and User Auditing

Designers and researchers lack systematic frameworks to evaluate LLM applications and impacts.

CHI '25Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review

lightbulbPractical problemAlgorithmic Decision Accountability, Contestability, and User Auditing

Risk reports in AI model files lack diversity and practicality, making risk assessment difficult for users.

CHI '25RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting

lightbulbPractical problemAlgorithmic Decision Accountability, Contestability, and User Auditing

The public perceives algorithms as unfair, especially in high-stakes decisions involving race and gender.

CHI '25Lay Perceptions of Algorithmic Discrimination in the Context of Systemic Injustice

lightbulbPractical problemAlgorithmic Decision Accountability, Contestability, and User Auditing

AI systems often fail to meet real-world needs, causing errors or wasted resources.

CHI '25AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development

lightbulbPractical problemAlgorithmic Decision Accountability, Contestability, and User Auditing

Scientific organizations struggle to balance the potential of generative AI with its risks.

CHI '25Generative AI Uses and Risks for Knowledge Workers in a Science Organization

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Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models

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Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review

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RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting

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Lay Perceptions of Algorithmic Discrimination in the Context of Systemic Injustice

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