Potential for Bias and Errors in Legal AI In-house

Note: This article is just one of 60+ sections from our full report titled: The 2024 Legal AI Retrospective - Key Lessons from the Past Year. Please download the full report to check any citations.

Challenge: Potential for Bias and Errors

AI systems are not infallible and can exhibit biases or make errors that have serious implications in legal contexts:

• AI-powered legal research tools may show bias in case law recommendations, potentially skewing legal analysis.[97]

• There have been instances of AI misinterpreting complex legal language in contract analysis, which could lead to errors in contract execution.[98]

• Facial recognition algorithms used in legal investigations have shown higher error rates for people of color, raising concerns about fairness and accuracy.[99]

To mitigate these risks, in-house legal teams must implement human oversight and quality control measures when using AI tools.

"When evaluating human and machine errors, the focus needs to shift from error rates to the nature and severity of the errors. While some errors are inconsequential, others can be catastrophic. Effective collaboration between humans and machines requires leveraging their comparative strengths to reduce the impact of errors, not just the frequency of errors."

Colin Doyle, Associate Professor of Law, Loyola Law School, Los Angeles, USA