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From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation.

Juan M Durán1, David van der Vloed2, Arnout Ruifrok2

  • 1Delft University of Technology, Jaffalaan 5, 2628 BX, Delft, Netherlands.

Forensic Science International. Synergy
|September 17, 2024
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Summary

Artificial intelligence (AI) in forensic science can be trusted if its reliability is proven. Computational reliabilism (CR) uses technical, scientific, and societal indicators to justify belief in AI outputs for voice and facial comparisons.

Keywords:
Evidence evaluationExplainabilityForensic AIReliabilism

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Area of Science:

  • Philosophy of Science
  • Forensic Science
  • Artificial Intelligence

Background:

  • AI algorithms are increasingly used in forensic biometrics but often function as 'black boxes'.
  • The inclusion of these AI systems in criminal law raises significant ethical, legal, and philosophical questions.
  • A multidisciplinary debate is needed to address the suitability of AI in forensic evidence evaluation.

Purpose of the Study:

  • To present a philosophy of science perspective on the use of AI in forensic evidence evaluation.
  • To introduce computational reliabilism (CR) as a framework for assessing AI reliability in forensics.
  • To argue for the justification of AI output in voice and facial comparisons within a legal context.

Main Methods:

  • Applying the framework of computational reliabilism (CR) to AI in forensic science.
  • Classifying grounds for believing AI reliability into three types of indicators: technical, scientific, and societal.
  • Analyzing the suitability of AI for forensic voice and facial comparison based on these indicators.

Main Results:

  • AI outputs in forensic voice and facial comparison can be considered reliable under CR.
  • Technical indicators involve algorithm validation, forensic application validation, and case-based validation.
  • Scientific indicators include inherent identifying information in faces/voices and established forensic metrics.
  • Societal indicators encompass scientific consensus and practitioner expertise.

Conclusions:

  • Computational reliabilism provides a robust framework for evaluating AI in forensic evidence beyond mere explainability.
  • Expert witnesses may prioritize technical indicators, while triers-of-fact may rely on societal indicators for AI-supported evidence.
  • The study supports the justified belief in AI-driven forensic comparisons of voices and faces.