Gender Bias in Machine Learning - Challenges and Solutions

Explore the critical issue of gender bias in machine learning through a 23-minute talk by Shalvi Mahajan from SAP SE at the Data Science Festival. Delve into how AI algorithms often reflect and amplify existing societal gender biases, impacting product design and service provision. Examine examples of gender stereotyping in machine learning, including biases in language translation and pronoun assignment in large language models. Understand the role of biased training data in perpetuating stereotypes and learn about multi-faceted approaches to address this problem, such as improving dataset diversity and implementing fairness-aware techniques. Gain insights into the need for ethical guidelines and regulations in ML system deployment to ensure accountability and transparency. Suitable for those seeking a high-level overview of gender bias challenges in AI and potential solutions.