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

TL;DR

This paper surveys AI/ML practitioners to understand how diversity and inclusion principles are perceived and implemented in real-world AI development, revealing organizational challenges and the importance of diverse teams for ethical AI.

Contribution

It provides empirical insights into current D&I practices in AI, identifying barriers and highlighting the gap between principles and implementation in industry settings.

Findings

Most practitioners see D&I as essential for fairness and bias mitigation.

Implementation of D&I remains inconsistent across organizations.

Major barriers include under-representation and lack of transparency.

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.