Document Category: Discovery

This motion seeks discovery concerning the reliability of colorimetric presumptive field drug tests, arguing that these tests are unreliable and prone to high false-positive rates. Drawing on empirical research and audits from jurisdictions nationwide, the motion explains that common legal substances frequently trigger positive results and that accuracy varies widely based on officer training, storage, and administration. It argues that information regarding test methodology, error rates, officer training, and agency policies constitutes Brady and Giglio material and is essential for effective assistance of counsel at the plea-bargaining stage and knowing pleas. Defenders can use this motion to compel early discovery.

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).

Because each stage of Facial Recognition (FR) carries a risk of error, due process and Brady require disclosure concerning every step (pp. 6–7). The five steps are: (1) the probe photo used, (2) the database selected, (3) any photo editing performed, (4) the algorithmic search, and (5) human review (pp. 6, 9–13). The brief explains that FR has particularly high error rates when applied to people of color, women, elders, and children (p. 8). Low-quality or edited probe photos increase error (pp. 10–11), and many FR databases are skewed by overrepresentation of minorities (pp. 12–13). Algorithms operate as “black boxes” with differing reliability (p. 13), while human review is subject to the same biases as eyewitness identification (p. 13). Because the risk of error varies at each stage and may be exculpatory, defense counsel is entitled to full discovery of the FR process, including the algorithm and analyst, both of whom function as impeachable “witnesses” (pp. 26–27). Defenders can use this brief to argue for comprehensive discovery of FR methods and to frame challenges to the admissibility or reliability of FR-based identifications.