To extend the fairness auditing framework for complex AI models like large language models (LLMs) or generative AI systems, several strategies can be employed:
Dynamic Fairness Metrics: Traditional fairness metrics may not capture the nuances of LLMs, which generate context-dependent outputs. Developing dynamic fairness metrics that consider the context and the specific use case of the model can provide a more accurate assessment of fairness. For instance, metrics could be designed to evaluate the fairness of generated text based on the demographic context of the users or the intended audience.
Multi-Modal Auditing: LLMs often interact with various data types (text, images, etc.). The auditing framework should incorporate multi-modal fairness assessments that evaluate how different inputs affect the outputs across various demographic groups. This could involve analyzing how biases manifest in different modalities and ensuring that fairness is maintained across all forms of data.
Explainability and Interpretability: Given the complexity of LLMs, integrating explainability tools into the auditing framework is crucial. This would allow auditors to understand how decisions are made and identify potential biases in the model's reasoning process. Techniques such as attention visualization or layer-wise relevance propagation can help elucidate the model's behavior.
User-Centric Auditing: Engaging with diverse user groups during the auditing process can provide insights into how different demographics experience the AI's outputs. User feedback can be incorporated into the auditing framework to ensure that the model's performance aligns with the expectations and needs of various communities.
Iterative Auditing: The framework should support iterative auditing processes, where models are continuously evaluated and improved based on feedback and new data. This is particularly important for generative models that evolve over time and may exhibit different biases as they are exposed to new training data.
By implementing these strategies, the fairness auditing framework can be adapted to effectively assess the complexities and nuances of large language models and generative AI systems, ensuring that they operate fairly and equitably across diverse user groups.