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Trustworthy AI in practice: an analysis of practitioners' needs and challenges

Maria Teresa Baldassarre, Domenico Gigante, Marcos Kalinowski, Azzurra Ragone, Sara Tibidò

TL;DR

Trustworthy AI in practice analyzes how AI practitioners perceive and implement TAI across the SDLC. The authors combine a survey and think-aloud interviews with 34 practitioners to reveal priorities (privacy and transparency), common mitigation strategies (data quality and explainable models), and significant impediments (time, cost, performance trade-offs). They identify a gap between high-level principles and concrete, deployable practices and propose practical guidelines, knowledge resources, and monitoring tools to support TAI throughout development and in production. The findings have implications for industry standards and regulatory compliance, including the AI Act, and highlight areas where future research should focus on end-to-end tooling and knowledge bases.

Abstract

Recently, there has been growing attention on behalf of both academic and practice communities towards the ability of Artificial Intelligence (AI) systems to operate responsibly and ethically. As a result, a plethora of frameworks and guidelines have appeared to support practitioners in implementing Trustworthy AI applications (TAI). However, little research has been done to investigate whether such frameworks are being used and how. In this work, we study the vision AI practitioners have on TAI principles, how they address them, and what they would like to have - in terms of tools, knowledge, or guidelines - when they attempt to incorporate such principles into the systems they develop. Through a survey and semi-structured interviews, we systematically investigated practitioners' challenges and needs in developing TAI systems. Based on these practical findings, we highlight recommendations to help AI practitioners develop Trustworthy AI applications.

Trustworthy AI in practice: an analysis of practitioners' needs and challenges

TL;DR

Trustworthy AI in practice analyzes how AI practitioners perceive and implement TAI across the SDLC. The authors combine a survey and think-aloud interviews with 34 practitioners to reveal priorities (privacy and transparency), common mitigation strategies (data quality and explainable models), and significant impediments (time, cost, performance trade-offs). They identify a gap between high-level principles and concrete, deployable practices and propose practical guidelines, knowledge resources, and monitoring tools to support TAI throughout development and in production. The findings have implications for industry standards and regulatory compliance, including the AI Act, and highlight areas where future research should focus on end-to-end tooling and knowledge bases.

Abstract

Recently, there has been growing attention on behalf of both academic and practice communities towards the ability of Artificial Intelligence (AI) systems to operate responsibly and ethically. As a result, a plethora of frameworks and guidelines have appeared to support practitioners in implementing Trustworthy AI applications (TAI). However, little research has been done to investigate whether such frameworks are being used and how. In this work, we study the vision AI practitioners have on TAI principles, how they address them, and what they would like to have - in terms of tools, knowledge, or guidelines - when they attempt to incorporate such principles into the systems they develop. Through a survey and semi-structured interviews, we systematically investigated practitioners' challenges and needs in developing TAI systems. Based on these practical findings, we highlight recommendations to help AI practitioners develop Trustworthy AI applications.
Paper Structure (20 sections, 5 figures, 3 tables)

This paper contains 20 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. AI Principles proliferation
  4. Trustworthy AI principles definitions
  5. Method
  6. Data collection
  7. The survey
  8. Interview Study Protocol
  9. Data Analysis
  10. Results and findings
  11. Preliminary concepts knowledge
  12. Practices in Preventing untrustworthiness in AI
  13. Practices in Discovering Untrustworthiness Issues
  14. Practices in Addressing Untrustworthiness Issues
  15. Demographics and background information
  16. ...and 5 more sections

Figures (5)

  • Figure 1: TAI principles addressed by SDLC phase.
  • Figure 2: Strategies employed to ensure trustworthiness in AI. N/A answers have been removed.
  • Figure 3: Perceived usefulness of hypothetical tools to prevent TAI issues. N/A answers have been removed.
  • Figure 4: Strategies employed to discover untrustworthiness in AI.
  • Figure 5: Perceived usefulness of hypothetical tools to address TAI issues. N/A answers have been removed.