7 papers
Anytime Contracts for AI Services
Boris Velasevic, Eden Saig, Eric Mazumdar
AI services are prone to moral hazard, as model providers can silently route requests to cheaper, lower-quality models, and the inherent stochasticity of model outputs makes this d…
Welfare-Optimal Classification with Accuracy Auctions
Bana Sadi, Eden Saig, Nir Rosenfeld
Prediction algorithms are increasingly used to inform decisions about humans, but maximizing accuracy-the standard learning objective-is not necessarily optimal for this purpose. I…
Adaptive Contracts for Cost-Effective AI Delegation
Eden Saig, Tamar Garbuz, Ariel D. Procaccia +2
When organizations delegate text generation tasks to AI providers via pay-for-performance contracts, expected payments rise when evaluation is noisy. As evaluation methods become m…
Evolutionary Prediction Games
Eden Saig, Nir Rosenfeld
When a prediction algorithm serves a collection of users, disparities in prediction quality are likely to emerge. If users respond to accurate predictions by increasing engagement,…
Incentivizing Quality Text Generation via Statistical Contracts
Eden Saig, Ohad Einav, Inbal Talgam-Cohen
While the success of large language models (LLMs) increases demand for machine-generated text, current pay-per-token pricing schemes create a misalignment of incentives known in ec…
Delegated Classification
Eden Saig, Inbal Talgam-Cohen, Nir Rosenfeld
When machine learning is outsourced to a rational agent, conflicts of interest might arise and severely impact predictive performance. In this work, we propose a theoretical framew…
Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
Eden Saig, Nir Rosenfeld
Optimizing user engagement is a key goal for modern recommendation systems, but blindly pushing users towards increased consumption risks burn-out, churn, or even addictive habits.…