First Published on 20 June 2023.

Introduction:

In recent posts we have been discussing AI bias, the systematic error in AI systems that can result in discriminatory outcomes, which is a topic that has gained significant attention. One approach that holds promise in addressing bias is the LLM (Label-Labeled Map) Process Supervision technique. In this blog post, we will explore the concept of LLM Process Supervision, its relevance to AI bias, and how it can be leveraged to mitigate bias in AI systems.

Here is some context: AI algorithms learn patterns from data and make predictions or decisions based on those patterns. However, if the training data used to develop these algorithms is biased or incomplete, the AI system can inadvertently perpetuate those biases, leading to unfair outcomes.

Takeaways / Goals:

Understanding the LLM Process Supervision Technique:

LLM Process Supervision is an innovative approach that involves incorporating human reviewers into the AI training process. These reviewers are provided with labeled examples and tasked with comparing the predictions made by the AI system to the ground truth labels. By comparing these predictions, reviewers can identify biases and discrepancies, providing valuable feedback for improving the system’s performance.

Identifying and Addressing Bias:

LLM Process Supervision helps identify bias by enabling reviewers to examine the predictions made by AI systems. Reviewers can highlight instances where bias is present, such as favoring one demographic group over another, and flag them for further investigation. This approach ensures that AI systems are subjected to ongoing scrutiny and evaluation, allowing for bias detection and subsequent mitigation.

Mitigating Bias through Iterative Improvement:

The iterative nature of LLM Process Supervision allows for continuous improvement and bias mitigation. As human reviewers identify biases, developers can analyze the underlying causes and make necessary adjustments to the training data, algorithms, or both. By iteratively refining the AI system, bias can be minimized, leading to fairer outcomes.

Advantages of LLM Process Supervision:

LLM Process Supervision offers several advantages over traditional AI development methods. It facilitates a human-in-the-loop approach, which helps uncover biases that may not be apparent solely through automated techniques. Human reviewers bring their expertise, intuition, and contextual understanding to the process, augmenting the AI system’s ability to detect and address bias effectively.