A significant risk associated with AI systems, including large language models that power tools like GitHub Copilot, is their lack of interpretability, often referred to as the "black box" problem. The complex, multi-layered neural networks make it difficult to trace the exact reasoning behind a specific output or decision. This opacity can obscure underlying biases, potential errors, or security vulnerabilities in the model's logic, making it challenging to trust, debug, and hold the system accountable. This is a core challenge addressed by the principle of Transparency in responsible AI development.

A. AI algorithms can learn and amplify existing societal biases present in their training data, which is a major ethical risk.

C. The use of AI often involves vast datasets, which increases the importance and complexity of data privacy regulations, not eliminates them.

D. AI tools augment developer capabilities and change workflows but do not replace the fundamental need for developers' problem-solving and architectural skills.

1. Microsoft Responsible AI Standard, v2. (June 2022). The principle of Transparency is detailed on page 11: "Transparency is about helping people understand AI systems and their outputs... An important aspect of transparency is explainability, which is the ability to explain an AI system’s behavior in a way that is understandable to people." This directly supports the risk identified in option B.

2. GitHub Copilot Trust Center. In the section "Responsible Deployment," GitHub discusses the limitations and responsible practices for Copilot, acknowledging that the model's suggestions are generated probabilistically and require human oversight. This implicitly points to the non-deterministic and not fully interpretable nature of the system. The document states, "GitHub Copilot is a tool, like a compiler or a debugger. It is the developer’s responsibility to check the security and quality of their code."

3. Stanford University, Human-Centered Artificial Intelligence (HAI). (2023). Artificial Intelligence Index Report 2023. Chapter 5: "Public Opinion," page 158. The report discusses global public perception of AI, where a key finding is that "people are nervous about AI products and services... [and] a lack of understanding of AI." This nervousness is partly rooted in the difficulty of interpreting AI decisions.