SOURCE-LINKED INTELLIGENCE

Algorithmic Contract Design for AI Markets: Learning, Information Design, and Robustness to Strategic Manipulation

CORDIS · observation · Publication date unknown

Algorithmic Contract Design for AI Markets: Learning, Information Design, and Robustness to Strategic Manipulation Artificial Intelligence (AI) markets, such as outsourced data labelling and model training, are expanding rapidly but face incentive problems. Service providers may reduce effort to cut costs, while the complexity and randomness of machine-learning (ML) outputs make it difficult to verify quality or effort. While contract theory offers tools to align incentives, a gap remains between theory and practice. Classical models assume that game parameters are commonly known, whereas in reality, much of this information is private. The learning efficiency and computational tractability of finding good contracts under such incomplete information remain largely unexplored. The irregular structures often lead to hardness results, while positive findings are scarce. Furthermore, when learning algorithms are deployed, agents with information advantages may manipulate the process, undermining the rel

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

226420.56

unit

EUR