Multimodal deep learning for evidence assessment with algorithmic bias analysis in criminal law

Key Points

• The analysis reveals significant algorithmic bias in criminal law processes.
• Evidence assessment is enhanced through deep learning techniques across various data sources.
• This observational analysis incorporates multimodal data to improve fairness in legal assessments.
• Implications suggest a need for better frameworks to address inherent biases in algorithms.

Abstract