The field of recruitment and professional development is moving towards a greater emphasis on fairness and equity, with a focus on addressing biases in AI-supported decision-making and promoting diversity and inclusion in STEM education. Researchers are developing new methods and frameworks for evaluating and improving fairness in recruitment processes, including the use of information flow modeling and causal synthetic data generation. These approaches aim to provide transparency and accountability in complex socio-technical systems and to support the development of more equitable and inclusive practices. Notable papers in this area include:

• The External Fairness Evaluation of LinkedIn Talent Search, which highlights demographic disparities in temporal stability and exposure in recruitment platforms.
• Modeling Fairness in Recruitment AI via Information Flow, which applies an information flow-based modeling framework to a real-world recruitment process to identify and analyze fairness risks.
• Advancing Equity in STEM, which critically analyzes the National Science Foundation's Division of Equity for Excellence in STEM and advocates for transformative reforms to fundamentally restructure STEM education environments.