ABSTRACT
In this chapter, we evaluate research methods aimed at bringing transparency and accountability to opaque, potentially biased algorithmic systems. We critically review methods that promote transparency through awareness, correctness, interpretability, and accountability, probing into users’ perceptions and interactions with these systems. These methods, while valuable, can fall short when improperly designed, overwhelming users rather than providing actionable information. This situation underscores the importance of algorithmic literacy and public education, which can empower users in their interactions with algorithmic systems. We conclude with a discussion on strategies to foster such literacy in schools and public spaces, as well as empowering everyday users in understanding and detecting potentially harmful algorithmic behaviours, thereby facilitating informed and transparent interactions with these systems.
