Beyond Prediction: Algorithmic Proof of Guilt or Innocence in Criminal Trials

Explore the controversial use of algorithms in criminal trials for determining guilt or innocence. Delve into the challenges and ethical concerns surrounding machine-generated evidence, including DNA analysis, authorship attribution, and location data. Examine the limitations of current legal frameworks in regulating algorithmic proof and the potential impact on justice outcomes. Gain insights into the complexities of using proprietary algorithms in the criminal justice system, the lack of transparency, and the need for independent audits. Consider potential solutions and the role of researchers in improving the fairness and accuracy of algorithmic evidence in criminal proceedings. This 46-minute lecture by Andrea Roth from the University of California, Berkeley, presented at the Santa Fe Institute, offers a comprehensive overview of this critical issue at the intersection of technology, law, and justice.

Beyond Prediction: Algorithmic Proof of Guilt or Innocence in Criminal Trials