Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligence

Diversity in ML/AI design is critical for equitable outcomes: The paper emphasizes that algorithms designed without diverse perspectives have produced discriminatory results in facial recognition and recidivism systems, underscoring why retaining diverse talent in the field is essential for responsible AI development.

Student persistence in ML/AI differs by demographic group: The research found that persistence varies across gender, international student status, student loan status, and visible minority status, suggesting that different populations may face distinct barriers to remaining in the field.

Professional confidence matters more than academics for long-term commitment: While short-term persistence is linked to enrollment factors like major and level of study, long-term intentional persistence is primarily correlated with students' confidence in their professional role—indicating that career readiness and self-efficacy are key to retaining talent.

Ethical concerns and competitive pressures are unique factors in ML/AI: Unlike other STEM fields, ML/AI students are influenced by the ambiguous ethical implications of their work and the highly competitive, lucrative nature of the field, suggesting these distinctive characteristics shape whether students choose to stay.

Students want their work to have positive impact: The paper indicates that wanting work to have positive social impact is a factor in persistence (though the abstract cuts off), highlighting that responsible AI values may be important for attracting and retaining ethically-minded professionals.