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SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector

Kyeongryul Lee, Heehyeon Kim, Joyce Jiyoung Whang

TL;DR

The paper introduces SAIF, a four-stage Systematic Data Generation Framework for evaluating the risks of generative AI in the public sector, incorporating multimodal capabilities and evolving jailbreak methods. It situates SAIF within a public-sector risk taxonomy, detailing four risk categories: system and operational misuse, content safety, societal, and legal and rights-related risks. SAIF comprises breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types to generate robust risk data and facilitate mitigation. The framework aims to provide a scalable, adaptable pipeline for comprehensive, multimodal risk assessment to support safe and responsible deployment of generative AI in government contexts. Future work includes integrating knowledge graphs and compositional reasoning to strengthen automatic data generation and risk coverage across diverse governmental applications.

Abstract

The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, evaluations of risks associated with AI-driven systems in the public sector remain insufficiently explored. Building upon an established taxonomy of AI risks derived from diverse government policies and corporate guidelines, we investigate the critical risks posed by generative AI in the public sector while extending the scope to account for its multimodal capabilities. In addition, we propose a Systematic dAta generatIon Framework for evaluating the risks of generative AI (SAIF). SAIF involves four key stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types. It ensures the systematic and consistent generation of prompt data, facilitating a comprehensive evaluation while providing a solid foundation for mitigating the risks. Furthermore, SAIF is designed to accommodate emerging jailbreak methods and evolving prompt types, thereby enabling effective responses to unforeseen risk scenarios. We believe that this study can play a crucial role in fostering the safe and responsible integration of generative AI into the public sector.

SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector

TL;DR

The paper introduces SAIF, a four-stage Systematic Data Generation Framework for evaluating the risks of generative AI in the public sector, incorporating multimodal capabilities and evolving jailbreak methods. It situates SAIF within a public-sector risk taxonomy, detailing four risk categories: system and operational misuse, content safety, societal, and legal and rights-related risks. SAIF comprises breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types to generate robust risk data and facilitate mitigation. The framework aims to provide a scalable, adaptable pipeline for comprehensive, multimodal risk assessment to support safe and responsible deployment of generative AI in government contexts. Future work includes integrating knowledge graphs and compositional reasoning to strengthen automatic data generation and risk coverage across diverse governmental applications.

Abstract

The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, evaluations of risks associated with AI-driven systems in the public sector remain insufficiently explored. Building upon an established taxonomy of AI risks derived from diverse government policies and corporate guidelines, we investigate the critical risks posed by generative AI in the public sector while extending the scope to account for its multimodal capabilities. In addition, we propose a Systematic dAta generatIon Framework for evaluating the risks of generative AI (SAIF). SAIF involves four key stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types. It ensures the systematic and consistent generation of prompt data, facilitating a comprehensive evaluation while providing a solid foundation for mitigating the risks. Furthermore, SAIF is designed to accommodate emerging jailbreak methods and evolving prompt types, thereby enabling effective responses to unforeseen risk scenarios. We believe that this study can play a crucial role in fostering the safe and responsible integration of generative AI into the public sector.
Paper Structure (14 sections, 2 figures, 1 table)

This paper contains 14 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Risks of Generative AI in Public Sector
  4. System and Operational Misuse Risks
  5. Content Safety Risks
  6. Societal Risks
  7. Legal and Rights-Related Risks
  8. Systematic Data Generation Framework for Evaluating the Risks of Generative AI
  9. Breaking Down Risks
  10. Designing Scenarios
  11. Applying Jailbreak Methods
  12. Exploring Prompt Types
  13. Implications for Public Sector Applications
  14. Conclusion and Future Work

Figures (2)

  • Figure 1: Overview of SAIF, which provides a consistent and scalable pipeline for generating prompt data, structured into four stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types.
  • Figure 2: An illustration of the evaluation process for generative AI risks with SAIF, which shows how generated prompt data is used to assess risks in the resulting outputs and identify specific vulnerabilities for mitigating the risks.