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Risks of ignoring uncertainty propagation in AI-augmented security pipelines.

Emanuele Mezzi1, Aurora Papotti1, Fabio Massacci1,2

  • 1Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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Summary
This summary is machine-generated.

This study addresses the uncertainty in AI-augmented systems by quantifying error propagation in automated pipelines. It provides a framework and simulator to assess risks in safety-critical AI applications.

Keywords:
artificial intelligenceautomatic program repairuncertainty quantification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • AI is increasingly integrated into software development, creating automated pipelines with AI subsystems of uncertain performance.
  • This integration poses significant risks to safety-critical domains due to potential error propagation.
  • Existing risk analysis methods do not adequately address the uncertainty in AI-augmented systems.

Purpose of the Study:

  • To develop a formal framework for capturing and quantifying uncertainty propagation in AI-augmented software systems.
  • To create a simulator for evaluating the impact of propagating errors.
  • To provide recommendations for AI system evaluation policies.

Main Methods:

  • Formalizing the underpinnings of uncertainty propagation in AI pipelines.
  • Developing a simulator to quantify the uncertainty arising from error propagation.
  • Conducting a case study to evaluate the simulation of propagating errors.

Main Results:

  • The study provides a method to formally capture uncertainty propagation in AI pipelines.
  • A simulator was developed and evaluated, demonstrating the quantification of uncertainty.
  • The approach's generalizability and limitations were discussed, with recommendations for evaluation policies.

Conclusions:

  • The developed framework and simulator offer a novel approach to assessing uncertainty in AI-augmented systems.
  • The findings are crucial for enhancing the safety and reliability of AI in critical applications.
  • Further research is needed to extend the approach to real-world systems and relax existing assumptions.