AI Ethics Prover Connector for AI agents.

Yes, it forces your agent to provide the specific stakeholder identification and harm quantification required for high-risk systems. It moves your documentation from vague goals to operational facts.

Ethics washing is when a company makes vague claims about fairness without actual data. This Connector stops it by demanding specific metrics, protected attributes, and clear recourse mechanisms.

It forces the agent to describe the methodology used for bias detection. It requires naming the metric, the protected attribute, the measured disparity, and the acceptable threshold.

It requires the agent to provide explanations at the user's comprehension level. It also demands counterfactual examples to show how different inputs would change the outcome.

No, it is an analytical support capability. It forces structured thinking and audit requirements, but it does not replace a formal human ethics review board.

Yes, it is ideal for lending models. It helps identify specific groups like financially vulnerable applicants and quantifies the probability of wrongful denials.

No. It enforces analytical rigor. forcing you to name stakeholders, quantify harms, audit biases with methodology, demonstrate transparency, and define recourse mechanisms. It does not prescribe ethical conclusions. The ethics are yours. The structure is enforced by the capability.

Any metric. it is framework-agnostic. Statistical parity, equalized odds, disparate impact ratio, calibration. It does not compute bias. it validates that you named the metric, the protected attribute, the measured disparity, and the acceptable threshold. 'We checked for bias' without this structure is ethics washing.

A structured process: (1) accessible challenge channel, (2) response SLA in business days, (3) human reviewer. not automated, (4) appeal process if the initial review denies the challenge, (5) remediation action if the challenge succeeds. 'Contact support' fails every axis.