 lead to unintended consequences, especially in high-stakes environments like healthcare or finance. For instance, biases in training data may result in discriminatory outcomes, raising questions about fairness and accountability (V7 Labs, 2025). Additionally, the lack of transparency in how AI agents arrive at decisions—often referred to as the 'black box' problem—complicates efforts to audit or rectify errors, underscoring the need for explainable AI frameworks (TU, 2025).

Security risks also pose a 