The field of AI and tech is moving towards a more nuanced understanding of bias and diversity, with a focus on mitigating stereotypes and promoting inclusive representation. Recent research has highlighted the importance of explicit supervision in controlling bias in language models, as well as the need for a multifaceted approach to addressing discrimination in software development careers. Additionally, there is a growing recognition of the potential for women to upskill or reskill into tech careers, and the importance of identifying and addressing the drivers and barriers that impact their decisions. Noteworthy papers include: Race and Gender in LLM-Generated Personas, which found systematic shifts and stereotype exaggeration in occupational personas generated by large language models. Compositional Bias Control in Large Language Models, which demonstrated the effectiveness of supervised fine-tuning in mitigating compositional biases. A Multifaceted View on Discrimination in Software Development Careers, which highlighted the prevalence of discrimination based on age, political perspective, and cognitive differences in the tech industry. Women upskilling or reskilling to an ICT career, which identified key drivers and barriers for women considering a career change into tech. From Perceived Effectiveness to Measured Impact, which investigated the effect of automated counter-stereotypes on gender bias and found that actual effectiveness can diverge from perceived effectiveness across demographic groups.