# FairRAG: A Privacy-Preserving Framework for Fair Financial Decision-Making
## 6 Results
### Critical Analysis: Benefits and Constraints of FairRAG
#### 6.5.1 Benefits of FairRAG  
* Architectural Simplicity and Computational Efficiency: A primary advantage of FairRAG is its design, which avoids the need for expensive private fine-tuning of large models. By leveraging a pre-trained smaller LLM (OPT-125M) and applying differential privacy at the data generation stage, the architecture remains lightweight. This contrasts sharply with methods like DP-SGD, which are applied to multi-billion  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline GPT-2 & 0.71 & 0.48 & 0.81 & 0.60 \\ Phi-2 & 0.76 & 0.57 & 0.65 & 0.61 \\ All-MiniLM & 0.76 & 0.54 & 0.67 & 0.60 \\ DistilBERT & 0.75 & 0.53 & 0.59 & 0.56 \\ TinyLlama & 0.73 & 0.49 & 0.82 & 0.61 \\ OPT-125M & 0.80 & 0.57 & 0.61 & 0.58 \\ FairRAG & 0.84 & 0.63 & 0.60 & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of language models on the Telco Churn dataset, with and without FairRAG integration.  
parameter models, requiring significant computational resources for training and complex privacy budget accounting. Our approach, being approximately 64 times smaller in parameter count than models like Google's PaLM-8B used in comparable DP synthetic data, makes privacy-preserving LLM applications more accessible.
* Decoupled Privacy Preservation: FairRAG integrates differential privacy natively by treating the private synthetic dataset as a static safe-to-use asset within its knowledge base. This \"decoupling\" of the privacy mechanism from the core predictive model is a key architectural benefit. It consumes information from a pre-sanitized source, allowing the use of powerful off-the-shelf LLMs while still benefiting from formal privacy guarantees \((\varepsilon,\delta)\) on the underlying data.
* Inherent Interpretability through Retrieval: Unlike opaque black-box models, FairRAG provides a clear and intuitive form of local instance-based explainability. When making a prediction for a new customer, the RAG mechanism retrieves the most similar existing profiles from its knowledge base. These retrieved examples serve as the rationale for the final prediction. This allows a human analyst to directly inspect the context the LLM used, building trust and making it possible to audit and understand individual outcomes, a critical requirement for high-stakes decisions such as churn management.
* Integrated Dual-Source Uncertainty Quantification: The framework provides a holistic measure of uncertainty than traditional models. It uniquely combines two distinct signals:
* Retrieval Uncertainty which quantifies the quality of the retrieved context by measuring the dissimilarity of neighboring profiles.
* Generative Uncertainty, which captures the LLM's confidence in its own output. By fusing these, FairRAG can distinguish between predictions that are uncertain due to ambiguous input (poor context) and those that are uncertain because the model itself is struggling to reason, providing a richer signal for trustworthy decision-making.