Adopting Data Science Observability Practices

This chapter explores the concept of data science observability in AI systems, transitioning from operational checks to evaluating performance and decision-making. It introduces TACA (Transparency, Accuracy, Calibration, Alignment) as a framework for building trust in AI systems, focusing on both external user experience and internal system performance. The chapter argues that disaggregating trust into these components enhances communication and decision-making, allowing teams to balance trade-offs and improve system trustworthiness. Key findings highlight the importance of continuous evaluation and the role of TACA in creating a concrete playbook for measuring and improving AI system trust. Not all TACA elements matter equally for every application. Value, purpose, and context determine which elements are prioritized. For instance, an AI counselling companion may succeed or fail based on alignment with user values, while a bird species classifier may depend almost entirely on accuracy, with little need for transparency or calibration. Setting clear standards in the product or project requirements documents (PRD) and revisiting them at each iteration keeps teams focused on what matters, avoiding wasted work on elements that do not.