NCA-GENL Question 202
Select 3You are developing a generative AI application using a large language model (LLM) that integrates data from various sources, including text, images, and structured databases. To ensure the application remains transparent, fair, and verifiable, which of the following practices should you adopt?
- A
Document the provenance and preprocessing steps of each data source used for training the model.
- B
Use only publicly available datasets, as they inherently ensure fairness and transparency.
- C
Implement bias detection mechanisms to evaluate and mitigate potential unfairness in the model's outputs.
- D
Provide users with an explanation of how the model generated its responses, including the influence of input data.
- E
Ensure that sensitive or proprietary data sources are excluded to completely avoid transparency concerns.
Show answer and explanation
Correct answers: A, C, D
Explanation
To ensure that a generative AI model integrating various data types is transparent, fair, and verifiable, it is essential to document data origins and preprocessing steps, implement mechanisms to detect and mitigate biases, and provide users with clear explanations of the model's outputs. These practices collectively build trust and accountability in AI systems, while simplistic solutions like relying solely on public datasets or excluding certain data types do not fully address the challenges of fairness and transparency.
- A. Correct.
Documenting the provenance and preprocessing steps of data sources ensures transparency and allows stakeholders to verify the integrity and relevance of the data used. This is a crucial step for building trustworthy AI systems.
- B. Incorrect.
While publicly available datasets are accessible, they are not inherently fair or transparent. Many public datasets may still contain biases or lack proper documentation, so additional measures are required to ensure fairness and transparency.
- C. Correct.
Bias detection mechanisms are crucial for identifying and mitigating potential issues of unfairness in model outputs. This ensures that the LLM does not propagate or amplify biases present in the training data.
- D. Correct.
Providing users with explanations about the model's decision-making process enhances transparency and trust. This also allows users to understand how input data influences the model's output.
- E. Incorrect.
Excluding sensitive or proprietary data does not necessarily address transparency concerns. Transparency requires proper documentation and fairness checks, not just the exclusion of certain data.