Rockfall presents a significant risk to the safety and economy of communities and infrastructure in mountainous regions. To assess the hazards associated with rockfall, Dunham et al. [1] developed the Rockfall Activity Index (RAI), which utilizes high-resolution terrestrial lidar-derived digital elevation models (DEMs) of rock slopes to categorize a slope face into seven distinct morphological units, or "RAI classes." This paper focuses on a comprehensive study conducted at four sites in Alaska, USA, where a robust lidar-based 5-year inventory of 4,381 rockfall events was analyzed. The primary objective was to investigate variations in failure characteristics, such as cumulative magnitude-frequency distributions, non-cumulative power-law parameters, average annual failure rates, and average failure depths, among the different RAI classes. The findings demonstrate that the seven RAI classes effectively differentiate the rock slope based on unique mass-wasting char-acteristics. Furthermore, the research explores spatial variations and temporal variations in these failure characteristics. Based on the study's outcomes, recommendations are provided for modifying the RAI parameters of each RAI class, specifically the annual failure rate and average failure depth. These modifications aim to enhance the accuracy and effectiveness of rockfall hazard assessments.