The conversation delves into the complexities of data bias in machine learning, highlighting the need for models that reflect our values rather than just historical data. Parinaz emphasizes the importance of understanding that data does not equate to truth, advocating for a more interdisciplinary approach to address these challenges. The discussion also touches on the significance of improving processes and the limitations of relying solely on explainability in AI.
