## 4 Discussion  
Collaboration was used as the starting point for the collection of empirical data from Kiva that consists of over 2 M observations with over 30 attributes spanning a time frame of 2006 through 2021, and subjects evolved based on practitioners' real-world experiences. The present literature in this field acknowledges the existence of bias in client-related decision-making, but there is a lack of specificity in the application of bias theory. Cognitive and motivational biases in decisions involving clients were acknowledged, and this gap was filled. While we selected sample data in several cases to demonstrate the importance of model retraining in this research, a leading practice is to use all available data. There are several techniques and algorithms as highlighted in this research that, when considered together, can contribute to a governance framework for continuously monitoring the decisions and outcomes of P2P lending and addressing socially responsible questions, including:  
1. Are more loans being funded to men in any given month, and what attributes are highly correlated with men being funded a loan versus a woman being funded a loan? 2. Are more loans being funded quicker for a given sector, and what factors are causing those quick decisions (e.g., market trends, etc.)? What key attributes are highly correlated with a loan request not being funded? 3. In any given set of loan requests, how many are likely to be funded in 7, 10, or 20 days? 4. With the introduction of new data and/or policies, does re-training the model and model output highlight any risks or inconsistencies in achieving P2P lending expectations (e.g., does the number of funded loans suddenly decline)? 5. How is a company managing behavior biases in P2P lending, for example, familiarity bias, where a lender is likely to fund a borrower who shares a similar background, experiences, or ethnicity? How is feature importance contributing to potential bias? With respect to bias, there are many tools available today to promote bias detection. In this section, we describe three of these available tools below:  
1. AWS Clarify is an Amazon Web Services (AWS) machine learning (ML) service that streamlines the process of developing, training, and deploying correct computer vision models. It provides a collection of tools for data labeling, data management, and model training, as well as a suite of pre-built models, to let developers quickly design and deploy unique image and video analysis applications [30, 31]. Models that have already been built: Pre-built models for popular computer vision tasks such as object detection, semantic segmentation, and picture classification are available through AWS Clarify. These models are trained on large-scale datasets and can be tailored to specific use cases with custom data. Model deployment: AWS Clarify offers a simple deployment mechanism for models created using the service. Models can be deployed either on AWS or on premises [30, 31]. Developers can use AWS Clarify through following these steps:  
1. Prepare the information: Collect and label the data used to train the model. 2. Train the model: On the labeled data, use AWS Clarify to train the model. 3. Examine the model: To determine the model's accuracy, use the evaluation metrics offered by AWS Clarify. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Algorithm** & **Time to Train (ms)** & **Inference Time** & **F1-Score** & **AUC** & **Recall** & **Precision** & **Accuracy** \\ \hline Linear Regression & 1020 & 98 & 0.974 & 0.5 & 1 & 0.948 & 0.948 \\ \hline Logistic Regression & 4750 & 87 & 0.797 & 0.752 & 0.668 & 0.987 & 0.677 \\ \hline K-Nearest Neighborhood & 151 & 16,300 & 0.854 & 0.618 & 0.766 & 0.964 & 0.751 \\ \hline Multinomial Naive Bayes & 120 & 86 & 0.792 & 0.655 & 0.669 & 0.972 & 0.667 \\ \hline Gradient Boosting & 1030 & 101 & 0.893 & 0.74 & 0.822 & 0.978 & 0.814 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of model performance across select algorithms. 4.