  The rapid expansion of Artificial Intelligence (AI) technologies has
introduced both significant advancements and challenges, with diversity and
inclusion (D&I) emerging as a critical concern. Addressing D&I in AI is
essential to reduce biases and discrimination, enhance fairness, and prevent
adverse societal impacts. Despite its importance, D&I considerations are often
overlooked, resulting in incidents marked by built-in biases and ethical
dilemmas. Analyzing AI incidents through a D&I lens is crucial for identifying
causes of biases and developing strategies to mitigate them, ensuring fairer
and more equitable AI technologies. However, systematic investigations of
D&I-related AI incidents are scarce. This study addresses these challenges by
identifying and understanding D&I issues within AI systems through a manual
analysis of AI incident databases (AIID and AIAAIC). The research develops a
decision tree to investigate D&I issues tied to AI incidents and populate a
public repository of D&I-related AI incidents. The decision tree was validated
through a card sorting exercise and focus group discussions. The research
demonstrates that almost half of the analyzed AI incidents are related to D&I,
with a notable predominance of racial, gender, and age discrimination. The
decision tree and resulting public repository aim to foster further research
and responsible AI practices, promoting the development of inclusive and
equitable AI systems.
