by Sujit Raman and Thomas Armstrong
From left to right: Sujit Raman and Thomas Armstrong. (Photos courtesy of authors).
Everyone these days is talking about artificial intelligence and how to use it responsibly. Among law enforcement and compliance professionals, discussions around the responsible use of AI are nothing new. Even so, recent advances in machine learning have turbocharged AI’s transformative potential in detecting, preventing, and—in a particular sense—even predicting illicit activity. These advances are especially notable in the field of blockchain analytics: the process of associating digital asset wallets to real-world entities.
In a recent, pathbreaking opinion and order, U.S. District Judge Randolph Moss rejected a criminal defendant’s challenge to the government’s evidentiary use of blockchain analytics to link him to illicit financial activity.[1] Many courts—including, just a few days ago, a U.S. district court in Massachusetts[2]—have relied on the validity of blockchain analytics when taking pre-trial actions like issuing seizure orders and authorizing arrest warrants; Judge Moss’s opinion is the first trial court examination of this powerful analytic capability. Taken together, this growing body of legal authority forcefully affirms the reliability—and therefore admissibility in court—of evidence derived from such analytics.