Technology concerns
Challenges related to technology refer to the limitations or constraints associated with generative AI. For example, the quality of training data is a major challenge for the development of generative AI models. Hallucination, explainability, and authenticity of the output are also challenges resulting from the limitations of the algorithms. Table 2 presents the technology challenges and issues associated with generative AI. These challenges include hallucinations, training data quality, explainability, authenticity, and prompt engineering
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
3 - Other
Risk ID
mit540
Domain lineage
7. AI System Safety, Failures, & Limitations
7.3 > Lack of capability or robustness
Mitigation strategy
1. Implement a comprehensive Data Governance and Quality Assurance framework to ensure training datasets are diverse, well-structured, and verifiably accurate, thereby directly mitigating the root causes of model bias and fundamental inaccuracies (hallucinations). 2. Architecturally embed Retrieval-Augmented Generation (RAG) techniques to ground all generative outputs in external, factual knowledge bases, which is critical for minimizing model hallucination and enhancing the authenticity and reliability of the generated content. 3. Integrate Explainable Artificial Intelligence (XAI) methodologies across the model lifecycle to provide necessary verifiability and lineage into the model's complex decision processes, supporting effective debugging, continuous monitoring, and regulatory compliance regarding model limitations.