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An adversarial risk analysis framework for software release decision support.

Refik Soyer1, Fabrizio Ruggeri2, David Rios Insua3

  • 1Department of Decision Sciences, George Washington University, Washington, District of Columbia, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|February 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new AI risk analysis framework to help developers decide when to release artificial intelligence (AI) products, considering reliability, costs, and competition.

Keywords:
adversarial risk analysisartificial intelligencerisk analysissoftware engineeringstrategic analysis

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Area of Science:

  • Software Engineering
  • Artificial Intelligence Risk Management
  • Decision Analysis

Background:

  • Stringent quality constraints and competitive markets necessitate careful AI system release decisions.
  • AI system reliability, safety, fault analysis, costs, and competitor actions complicate release timing.
  • Current AI risk management frameworks require robust methods for market release decisions.

Purpose of the Study:

  • To propose a novel general adversarial risk analysis framework for AI system release decisions.
  • To support AI developers in navigating the complexities of market entry.
  • To provide a structured approach for evaluating AI product release under uncertainty.

Main Methods:

  • Development of a multi-agent adversarial risk analysis framework.
  • Modeling producers and buyers as distinct agent types within the framework.
  • Illustration of the framework's implementation with a practical example.

Main Results:

  • The proposed framework offers a method for AI developers to assess release timing.
  • The framework accounts for adversarial interactions between producers and buyers.
  • Extensions for multiple producers and buyers are discussed, enhancing applicability.

Conclusions:

  • The novel adversarial risk analysis framework provides a valuable tool for AI release decision-making.
  • Addressing uncertainty and competitive dynamics is crucial for successful AI product deployment.
  • The framework can be extended to more complex market scenarios involving multiple stakeholders.