Trust and Transparency in Data Science

This chapter reflects on the tumultuous events of 2020 while exploring the challenges of ensuring trustworthiness in data analysis. The speakers discuss the importance of accountability, transparency, and systematic checks in data science, drawing parallels to rigorous standards in fields like aviation and automotive safety. They emphasize the complexities of reproducibility and the necessity of building credibility through thoughtful presentation and openness in analytical processes.