Esthappan's research highlights that while algorithmic risk assessments aim to aid judges by reducing bias in bail decisions, the reality is far more complex.
Judges often use risk assessment scores selectively rather than consistently, influenced by their personal biases and the specifics of each case, indicating a nuanced relationship with technology.
Algorithmic tools should theoretically help streamline bail process and reduce human error, yet Esthappan’s findings reveal they may unintentionally mask systemic issues in the bail system.
Understanding how judges interact with these algorithms shows that technology alone cannot resolve deep-rooted biases and inefficiencies in the justice system; human factors play a significant role.
#algorithmic-risk-assessment #bail-system #judicial-decision-making #criminal-justice #bias-in-technology
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