Disclaimer: This podcast is completely AI generated by ⁠⁠⁠⁠⁠⁠⁠NoteBookLM⁠⁠⁠⁠⁠⁠⁠ 🤖 Summary ⁠In this episode we discuss the following article that explores the multifaceted nature of AI bias⁠, explaining how it emerges at various stages of deep learning, from problem framing and data collection to data preparation. It highlights the challenges in mitigating this bias, including the difficulty in identifying its origins, inadequate testing methodologies, the lack of social context in algorithm design, and the conflicting definitions of fairness. The article concludes by acknowledging the ongoing efforts of researchers to address these complex issues and emphasizing that eliminating algorithmic discrimination is a continuous process.