Nowadays, Artificial Intelligence (AI) is increasingly applied in many engineering systems to enhance automation and efficiency. AI-based systems enable monitoring of the overall system and automate basic tasks. However, adopting AI may create conflicts between the system and the human operator. A few studies are focusing on the interaction between AI and Human Intelligence (HI), but they don’t analyze machine and crew failures separately in detail from a safety perspective. This work combines risk assessment of system failure due to human errors utilizing cognitive-based third-generation human reliability analysis (HRA) methods. We use the Information, Decision, and Action (IDA) phases of the crew context for qualitative human reliability analysis (HRA) to evaluate human failure mechanisms and performance influencing factors (PIFs) that lead to human failure events (HFEs). We evaluate the human error probability (HEP) for each crew failure mode (CFM) and calculate a total probability using Bayesian networks. The framework is demonstrated through an investigation of the Ethiopian Airlines Flight 302 Boeing MAX 737 crash as a case study, considering a research gap in exploring the development of HRA methods in the aviation field.