Exploring Causal Analysis and Explainability in Multi-Layered LLM Auditing

Abstract: Large language models (LLMs) have emerged as powerful tools with vast potential across various fields. However, concerns regarding bias, safety, and fairness necessitate the development of robust auditing procedures to ensure their responsible use. This paper builds upon the established three-layered LLM auditing approach (Governance, Model, Application) by highlighting the importance of causal analysis and explainability in each layer. We discuss recent research advancements in causal AI and explainable AI (XAI) and explore their potential applications within LLM auditing.

1. Introduction

Large language models (LLMs) are a type of artificial intelligence (AI) capable of processing and generating human-like text. Trained on massive datasets, LLMs can perform various tasks, including translation, writing different kinds of creative content, and answering your questions in an informative way (Vaswani et al., 2022). Despite their potential benefits, the "black-box" nature of LLMs raises concerns about potential biases, safety issues, and fairness in their outputs (Brundage et al., 2020). To mitigate these risks and ensure the responsible use of LLMs, robust auditing procedures are crucial.

The current dominant approach to LLM auditing is a three-layered framework:

1. Governance Audits: These audits assess the development process of LLMs, focusing on the practices of the companies that create them.
2. Model Audits: These audits evaluate the LLM itself before it's released, looking for potential biases or safety issues.
3. Application Audits: These audits examine how LLMs are used in different applications to ensure they are being used responsibly (Mikolov et al., 2013).

This paper argues that incorporating causal analysis and explainability techniques into each layer of the auditing framework can significantly enhance its effectiveness.

2. Causal Analysis and Explainability in LLM Auditing

2.1. Governance Layer

Causal analysis plays a vital role in understanding the root causes of potential biases within LLMs. By analyzing causal relationships between data selection practices, training algorithms, and model outputs, auditors can identify how development choices contribute to bias. For instance, causal analysis can reveal if a dataset skewed towards a particular demographic group leads to biased outputs in the LL

Here, X1 represents data selection practices, X2 represents training algorithms, and X3 represents model outputs. The arrows indicate causal relationships. By analyzing this DAG (Directed Acyclic Graph), auditors can identify how X1 and X2 causally affect X3, helping to pinpoint potential sources of bias (Pearl, 2009).

Explainability techniques, on the other hand, can shed light on governance decisions and risk mitigation strategies. By making these processes more transparent, explainability fosters trust and accountability within the development lifecycle. For example, explainability tools can provide detailed reports on bias detection methods employed during data cleaning or highlight the rationale behind specific training algorithm choices (Samek et al., 2019).

2.2. Model Layer

Causal AI offers a powerful set of tools to identify causal relationships within the complex inner workings of LLMs. By analyzing causal links between inputs, internal model representations, and outputs, auditors can pinpoint the root causes of biases or safety issues. For instance, causal AI can help identify if specific biases in the training data causally affect the LLM's decision-making process for a particular typ

2.3. Application Layer

Application audits focus on ensuring that LLMs are integrated and utilized responsibly within different applications. Here's how causal analysis and explainability can be applied in this layer:

• Causal Analysis for Downstream Impacts: Causal analysis can help identify how design choices within an LLM application can lead to downstream impacts on users or society. For example, an application that relies on an LLM for loan approvals might inadvertently amplify the LLM's bias against a particular demographic group. Causal analysis can help pinpoint this causal relationship between the application design (relying solely on LLM output) and the potential discriminatory outcome.
• Explainability for Informed User Interactions: Integrating explainability techniques into LLM applications can improve user trust and enable informed interactions. By providing users with explanations for the LLM's outputs within the application, users can assess the credibility and potential biases inherent in the LLM's responses. For instance, an application using an LLM for news summarization might explain which keywords or sources the LLM prioritized when generating a summary. This allows users to make a more informed judgment about the information presented.

Examples of Explainability in LLM Applications:

• Highlighting influential factors: XAI techniques can highlight the most critical factors considered by the LLM when generating a response. This can help users understand the reasoning behind the output and identify potential biases based on the highlighted factors.
• Counterfactual explanations: These explanations explore how the LLM's output would change if specific input features were modified. This can help users understand the model's sensitivity to different aspects of the input and identify potential biases.

By incorporating causal analysis and explainability into application audits, we can ensure that LLM applications are designed and used ethically, mitigating potential risks and fostering responsible innovation.

3. Emerging Challenges and Future Directions

The field of LLM auditing is constantly evolving. Here, we discuss two emerging challenges:

• Auditing Generative LLMs: A new challenge is auditing generative LLMs, which can create entirely new content. Detecting biases or safety issues in generated text can be particularly difficult. Future research should explore methods specifically designed to audit generative models.
• Auditing for Robustness: It's important to ensure LLMs are robust to adversarial attacks, where malicious actors try to manipulate the model's outputs. Future research needs to address auditing for LLM robustness.