3 articles tagged with #algorithmic-governance. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers extend the "Selection as Power" framework to dynamic settings, introducing constrained reinforcement learning that maintains bounded decision authority in AI systems. The study demonstrates that governance constraints can prevent AI systems from collapsing into deterministic dominance while still allowing adaptive improvement through controlled parameter updates.
Researchers introduce HAMU, a machine unlearning algorithm that removes the influence of specific training data while preserving model performance by quantifying the difficulty of balancing forget quality and retain utility through data similarity metrics. The approach offers theoretical guarantees and practical deployability for non-convex models, addressing a critical privacy and bias concern in machine learning.
Researchers propose the first statistical framework for Algorithmic Collective Action (ACA) involving multiple independent collectives attempting to coordinate changes in shared data to influence AI classifier behavior. The framework provides computable bounds on collective success while accounting for varying sizes, strategies, and goal alignment across groups, with applications to climate adaptation in smart cities.
