Tag: AI transparency
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Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features
Discover how domain-filtered knowledge graphs enhance interpretability of sparse autoencoder features for better AI insights and applications.
Transparent Screening of LLM Training and Inference Impact
Discover a transparent framework to estimate and compare LLM training and inference impacts, enhancing AI model transparency and sustainability.
Human-Centered XAI: Learning Theories & Future Challenges
Explore how learning theories enhance human-centered XAI, boosting transparency, trust, and user engagement while addressing future challenges.
Conformal Interpretability of Temporal Concepts in LLM Agents
Explore how conformal interpretability reveals temporal concept evolution in LLM agents, improving transparency and performance in decision-making tasks.
Co-Creating Fairness and Transparency at FAccT Conference
Explore how participatory design shaped the vision for fairness, accountability, and transparency in AI at the ACM FAccT conference.