Across the United States, algorithmic tools are proliferating throughout the criminal legal system, with more than 3,000 jurisdictions in the United States using some sort of predictive technology to determine where police should be deployed, who should be arrested, how long defendants should be detained, whether someone is eligible for parole, and more. While these new technologies promise a more just and equitable society, scholars have shown that they often only reinforce bias and perpetuate systemic harms. This Essay explores how legal advocates find creative ways to use the systems that they oppose to the benefit of their clients. Using N. K. Jemisin’s fictional musings, I evaluate the case of pretrial detention hearings in New Jersey criminal courts wherein defendants are assigned a risk score by a pretrial risk assessment tool. Despite the scholarship and experiential knowledge of practitioners that suggest how harmful these risk assessment tools can be for defendants, legal advocates continue to develop legal strategies that subvert these harms. Using N. K. Jemisin’s work, I document new strategies that lawyers in the New Jersey context use which prioritize long-term gains even if immediate wins are less available.

Continue reading Dismantling the Master’s Algorithm: Understanding Defense Attorney Use of Algorithmic Recommendation Tools Using N. K. Jemisin’s Red Dirt Witch.

Robson_Dismantling