Cover Image

From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dai, Jessica, Gradu, Paula, Raji, Inioluwa Deborah, Recht, Benjamin
Format:	Preprint
Published:	2025
Subjects:	Computers and Society Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Aggregated Individual Reporting for Post-Deployment Evaluation
by: Dai, Jessica, et al.
Published: (2025)

Evaluating Prediction-based Interventions with Human Decision Makers In Mind
by: Raji, Inioluwa Deborah, et al.
Published: (2025)

Concrete Problems in AI Safety, Revisited
by: Raji, Inioluwa Deborah, et al.
Published: (2023)

Bridging Prediction and Intervention Problems in Social Systems
by: Liu, Lydia T., et al.
Published: (2025)

From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
by: Chehbouni, Khaoula, et al.
Published: (2024)

Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
by: Ojewale, Victor, et al.
Published: (2024)

AI auditing: The Broken Bus on the Road to AI Accountability
by: Birhane, Abeba, et al.
Published: (2024)

"I Searched for a Religious Song in Amharic and Got Sexual Content Instead": Investigating Online Harm in Low-Resourced Languages on YouTube
by: Nigatu, Hellina Hailu, et al.
Published: (2024)

Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets
by: Straitouri, Eleni, et al.
Published: (2024)

The Data Addition Dilemma
by: Shen, Judy Hanwen, et al.
Published: (2024)

A Deep Generative Framework for Joint Households and Individuals Population Synthesis
by: Qian, Xiao, et al.
Published: (2024)

Harm Mitigation in Recommender Systems under User Preference Dynamics
by: Chee, Jerry, et al.
Published: (2024)

IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures
by: Gringras, David
Published: (2026)

Learning With Multi-Group Guarantees For Clusterable Subpopulations
by: Dai, Jessica, et al.
Published: (2024)

Lazy Data Practices Harm Fairness Research
by: Simson, Jan, et al.
Published: (2024)

From Melting Pots to Misrepresentations: Exploring Harms in Generative AI
by: Gautam, Sanjana, et al.
Published: (2024)

Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
by: Quaye, Jessica, et al.
Published: (2024)

From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing
by: Cen, Sarah H., et al.
Published: (2024)

A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
by: Pfohl, Stephen R., et al.
Published: (2024)

Evaluation without Generation: Non-Generative Assessment of Harmful Model Specialization with Applications to CSAM
by: Suriyakumar, Vinith M., et al.
Published: (2026)

Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness
by: Xu, Ruichen, et al.
Published: (2026)

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach
by: Ahmed, Md Sabbir, et al.
Published: (2025)

Monotone Individual Fairness
by: Bechavod, Yahav
Published: (2024)

Harm Amplification in Text-to-Image Models
by: Hao, Susan, et al.
Published: (2024)

Identifying Privacy Personas
by: Hrynenko, Olena, et al.
Published: (2024)

Can Probabilistic Feedback Drive User Impacts in Online Platforms?
by: Dai, Jessica, et al.
Published: (2024)

Rethinking Individual Fairness in Deepfake Detection
by: Hou, Aryana, et al.
Published: (2025)

Identifying Cyberbullying Roles in Social Media
by: Sandoval, Manuel, et al.
Published: (2024)

A Conceptual Framework for Ethical Evaluation of Machine Learning Systems
by: Gupta, Neha R., et al.
Published: (2024)

The Value of Prediction in Identifying the Worst-Off
by: Fischer-Abaigar, Unai, et al.
Published: (2025)

Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation
by: Chien, Jennifer, et al.
Published: (2024)

A Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender System
by: Baidya, Nikita, et al.
Published: (2026)

Uncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation
by: Sun, Shuzhou, et al.
Published: (2025)

Research on Individual Trait Clustering and Development Pathway Adaptation Based on the K-means Algorithm
by: Wei, Qianru, et al.
Published: (2026)

Bias and Identifiability in the Bounded Confidence Model
by: Borile, Claudio, et al.
Published: (2025)

Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review
by: Benítez-Peña, Sandra, et al.
Published: (2026)

Identifying Dealbreakers and Robust Policies for the Energy Transition Amid Unexpected Events
by: Coppitters, Diederik, et al.
Published: (2025)

Efficient k-means with Individual Fairness via Exponential Tilting
by: Zhu, Shengkun, et al.
Published: (2024)

Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
by: Zhang, He, et al.
Published: (2023)

The Role of Learning Algorithms in Collective Action
by: Ben-Dov, Omri, et al.
Published: (2024)