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Diversity and Inclusion in AI: Insights from a Survey of AI/ML Practitioners

Sidra Malik, Muneera Bano, Didar Zowghi

TL;DR

The paper argues that although AI practitioners recognise D&I as essential for fairness, there is a persistent gap between perceived value and real-world implementation across the AI lifecycle. Using a mixed-methods practitioner survey structured around five AI ecosystem pillars, it reveals that organisations show progress in recruitment and governance on paper but uneven execution, especially in post-development monitoring and data-bias auditing. The findings identify key barriers—underrepresentation, demographic data gaps, and weak integration of D&I into workflows—while highlighting that diverse teams are still viewed as contributors to ethical and trustworthy AI. The study underscores the need for actionable, ongoing D&I practices, robust governance, and targeted training to bridge theory and practice, with implications for policy, industry, and future research.

Abstract

Growing awareness of social biases and inequalities embedded in Artificial Intelligence (AI) systems has brought increased attention to the integration of Diversity and Inclusion (D&I) principles throughout the AI lifecycle. Despite the rise of ethical AI guidelines, there is limited empirical evidence on how D&I is applied in real-world settings. This study explores how AI and Machine Learning(ML) practitioners perceive and implement D&I principles and identifies organisational challenges that hinder their effective adoption. Using a mixed-methods approach, we surveyed industry professionals, collecting both quantitative and qualitative data on current practices, perceived impacts, and challenges related to D&I in AI. While most respondents recognise D&I as essential for mitigating bias and enhancing fairness, practical implementation remains inconsistent. Our analysis revealed a disconnect between perceived benefits and current practices, with major barriers including the under-representation of marginalised groups, lack of organisational transparency, and limited awareness among early-career professionals. Despite these barriers, respondents widely agree that diverse teams contribute to ethical, trustworthy, and innovative AI systems. By underpinning the key pain points and areas requiring improvement, this study highlights the need to bridge the gap between D&I principles and real-world AI development practices.

Diversity and Inclusion in AI: Insights from a Survey of AI/ML Practitioners

TL;DR

The paper argues that although AI practitioners recognise D&I as essential for fairness, there is a persistent gap between perceived value and real-world implementation across the AI lifecycle. Using a mixed-methods practitioner survey structured around five AI ecosystem pillars, it reveals that organisations show progress in recruitment and governance on paper but uneven execution, especially in post-development monitoring and data-bias auditing. The findings identify key barriers—underrepresentation, demographic data gaps, and weak integration of D&I into workflows—while highlighting that diverse teams are still viewed as contributors to ethical and trustworthy AI. The study underscores the need for actionable, ongoing D&I practices, robust governance, and targeted training to bridge theory and practice, with implications for policy, industry, and future research.

Abstract

Growing awareness of social biases and inequalities embedded in Artificial Intelligence (AI) systems has brought increased attention to the integration of Diversity and Inclusion (D&I) principles throughout the AI lifecycle. Despite the rise of ethical AI guidelines, there is limited empirical evidence on how D&I is applied in real-world settings. This study explores how AI and Machine Learning(ML) practitioners perceive and implement D&I principles and identifies organisational challenges that hinder their effective adoption. Using a mixed-methods approach, we surveyed industry professionals, collecting both quantitative and qualitative data on current practices, perceived impacts, and challenges related to D&I in AI. While most respondents recognise D&I as essential for mitigating bias and enhancing fairness, practical implementation remains inconsistent. Our analysis revealed a disconnect between perceived benefits and current practices, with major barriers including the under-representation of marginalised groups, lack of organisational transparency, and limited awareness among early-career professionals. Despite these barriers, respondents widely agree that diverse teams contribute to ethical, trustworthy, and innovative AI systems. By underpinning the key pain points and areas requiring improvement, this study highlights the need to bridge the gap between D&I principles and real-world AI development practices.

Paper Structure

This paper contains 33 sections, 9 figures.

Table of Contents

  1. Introduction
  2. Motivation and Related work
  3. Humans: Recruitment and Team Structures
  4. Data and Bias Mitigation
  5. AI lifecycle: Process and Systems
  6. Governance
  7. Research Questions
  8. Methodology
  9. Survey Design, Structure and Development
  10. Pilot Study
  11. Participant Recruitment and Demographics
  12. Data Analysis Approach
  13. Survey Results
  14. Demographic Analysis
  15. Perceived Impact of Diversity and Inclusion in AI
  16. ...and 18 more sections

Figures (9)

  • Figure 1: Gender vs. Experience
  • Figure 2: Perceived Impact of Diverse and Inclusive Teams om D&I in AI
  • Figure 3: Perceived Impact: Effect of D&I on processes and systems
  • Figure 4: Perceived Impact:Effect of government policies on success of D&I in AI initiatives
  • Figure 5: Current Practices: Ensuring diverse and inclusive datasets
  • ...and 4 more figures