Game Theory, AI, Machine Learning, and LLMs

My starting point is the response an automated action invites. A provider can lower the cost of preparing an appeal; a payer can lower the cost of reviewing or contesting it. Either party may improve its own productivity while the combined process generates more submissions, more disputes and more work. This is a strategic-interaction hypothesis, not a claim about the performance of a particular product. It explains why a local time-saving estimate is insufficient for an enterprise investment decision.

The relevant actors need not share an objective. A patient values timely, appropriate care and understandable obligations. A provider needs clinically sound decisions and collectible reimbursement. A payer must administer coverage and manage expenditure. A vendor may earn fees from activity, completed transactions or measured outcomes. The contract determines which behaviors are rewarded; the interface makes some of those behaviors easier to execute. Both belong in the design review.

Consider a hypothetical payment-integrity service compensated on gross findings. Expanding the volume of challenged claims can increase the vendor’s apparent output even if a material share is later overturned. The buyer’s relevant result is sustainable net recovery after appeals, administrative cost, contract interpretation and the consequences for provider relationships. Payment on a mature, reconciled result changes the incentive, but also delays vendor cash and requires agreement on attribution. There is a real contract trade-off, not a universally superior pricing label.

Information asymmetry complicates the choice. The party selling a model may know more about its failure modes; the deploying organization may know more about the workflow; the person affected may be unable to observe either. An explanation screen cannot resolve that asymmetry by itself. I would require access to the evidence relevant to a consequential action, a route to contest it and a party with the authority to correct the record. Those controls make mistakes discoverable and change the cost of leaving them unresolved.

Before deployment, specify the initial objective, the likely response from each counterparty and the conditions under which the response defeats the investment thesis. Test whether higher throughput increases duplicate work, whether a new ranking changes which cases get attention, and whether users learn to alter inputs to receive a preferred result. A pilot that holds everyone else’s behavior fixed may miss the principal risk of scaled deployment.

I would authorize automation in stages. Begin with a bounded action whose evidence can be checked and whose errors can be reversed. Evaluate the combined workflow, including the burden transferred to counterparties. Expand only when the organization can explain both the direct effect and the response it induces. Where the parties disagree about the objective, renegotiate the process or contract before increasing automated volume.