AI clogs courts and can be risky.

We worry a bit about self- represented litigants who may use AI to abusively flood the courts with meritless lawsuits, clogging the system and overwhelming court personnel and judges. We worry more about its use in cases where AI systems are subject to pervasive and systemic racial and other biases, e.g., predictive policing, facial recognition, and criminal risk/recidivism assessment. We lose the most sleep over a court relying on AI applications or evidence as the primary or sole evidence supporting a consequential outcome (such as the imposition of a criminal sentence) without sufficient assurances that the AI system has been trained on data and/or programmed so its validity and reliability can be demonstrated empirically (i.e., it accurately does what it was designed to do and it consistently produces accurate results when applied to substantially similar facts, respectively); and that it is equitable, unbiased, and/or fair (assuming that we can reach consensus on what it means for an algorithm to be “equitable,” “unbiased,” and/or “fair”). When one or more of those conditions cannot be met, we believe that AI evidence should be excluded.