Motion To Compel Discovery Pursuant to Maryland Rule 4-262 Concerning Operation and Reliability of Flock Data

This motion argues that Flock’s ALPR and Vehicle Fingerprint® systems are secretive, unregulated, and error-prone artificial intelligence tools whose reliability cannot be assessed without robust discovery into how they function, how they were developed and trained, and what their error rates are (pp. 4-7). It explains that machine-learning “hallucinations,” environmental conditions (weather, lighting, camera noise), and reliance on flawed databases and private HotLists undermine the accuracy of Flock outputs (pp. 4–7). Drawing on Maryland Rule 4-262, Brady/Giglio, and Crane v. Kentucky, the motion contends that Flock operates like an uncorroborated confidential informant or other similar technologies like ShotSpotter and Facial Recognition and that the accused is therefore constitutionally entitled to underlying data, logs, policies, audits, and technical documentation so they can confront and impeach Flock-derived evidence and present a complete defense (pp. 8–14).