The discussion revolves around the inadequacies of conventional rating systems, particularly how they often rely on simplistic averages which fail to account for review context and user behavior. The critique highlights examples from platforms like Yelp, Google, and Amazon, demonstrating how one-off sensational reviews can skew the overall ratings negatively. Several users advocate for more robust algorithms that consider various parameters, such as user retention and duration of engagement, which some systems are already implementing (e.g., Foursquare). There’s also commentary on the subjective nature of ratings across different platforms, and the inherent biases that can arise depending on users’ expectations and experiences. Suggestions for improvement include simplifying the rating scales to avoid confusion and bias, as well as potentially outsourcing the interpretation of qualitative feedback to AI for better insights.