arXiv:2602.04402v3 Announce Type: replace-cross Abstract: Performative predictions influence the very outcomes they aim to forecast. We study performative predictions that affect a sample (e.g., only existing users of an app) and/or the whole population (e.g., all potential app users). This raises the question of how well models generalize under performativity. For example, how well can we draw insights about new app users based on existing users when both of them react to the app's predictions? We address this question by embedding performative predictions into statistical learning theory. We
The increasing deployment of AI systems in real-world applications highlights the urgent need to understand and mitigate the reactive nature of their predictions and their recursive impact.
This research provides a theoretical framework to understand how AI model predictions can influence the very data they are trained on, impacting generalization and real-world outcomes.
The focus shifts from static model generalization to understanding and designing for performative generalization, where predictions alter user behavior and population dynamics.
- · AI ethicists and researchers
- · Platforms and applications using performative predictions
- · Regulatory bodies developing AI guidelines
- · AI developers ignoring performative effects
- · Traditional statistical learning theory applications
- · Systems susceptible to negative feedback loops
Improved understanding and tools for developing more robust and fair AI systems for dynamic environments.
Development of new AI evaluation metrics and regulatory frameworks that account for self-fulfilling or self-defeating prophecies.
Potentially, a re-evaluation of 'ground truth' in fields where AI predictions become part of the data-generating process, leading to new philosophical and scientific inquiries.
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Read at arXiv cs.LG