aegis-ai-eval

Your task is to create a mini-application that leverages the 'aegis-ai-eval' package to assess and mitigate risks associated with deploying AI models in various business contexts. This application will serve as a risk management tool for companies looking to implement AI solutions but are wary of potential ethical, legal, and operational risks.

**Application Overview:**
This application, named 'AI Risk Sentinel', will provide a user-friendly interface where users can input details about their AI model and its intended use case. Based on this information, the application will perform a comprehensive risk assessment using the 'aegis-ai-eval' framework and suggest mitigation strategies to address identified risks.

**Key Features:*
1. **Model Input Form:** A form where users can specify details such as the type of AI model (e.g., machine learning, deep learning), the data it processes, and the industry sector it will be deployed in.
2. **Risk Assessment Engine:** Utilize the 'aegis-ai-eval' package to analyze the inputted information and identify potential risks. These could include biases in the dataset, privacy concerns, and compliance issues.
3. **Mitigation Strategy Generator:** Based on the risk assessment results, generate actionable steps to mitigate these risks. For example, if bias is detected, suggest techniques like re-balancing the dataset or implementing fairness-aware algorithms.
4. **Report Generation:** Create a detailed report summarizing the findings of the risk assessment and the proposed mitigation strategies. This report should be easy to understand and share with stakeholders.
5. **User Interface:** Develop a simple, intuitive web-based UI using Flask or Django to interact with the backend logic.

**How to Use 'aegis-ai-eval':*
- Import the necessary modules from the 'aegis-ai-eval' package at the beginning of your script.
- After collecting user inputs through the form, pass these details to the risk assessment function provided by the package.
- Interpret the output from the package to generate meaningful insights and suggestions.
- Ensure that the application can handle different types of inputs gracefully and provides informative feedback to the user.

**Development Steps:**
1. Set up a virtual environment and install the required packages including 'aegis-ai-eval'.
2. Design the user interface for collecting model details.
3. Implement the backend logic for processing inputs and generating outputs using 'aegis-ai-eval'.
4. Test the application thoroughly with different scenarios to ensure robustness.
5. Deploy the application on a local server for demonstration purposes.

By completing this project, you will gain valuable experience in integrating specialized AI risk assessment tools into practical applications, enhancing your understanding of both technical and ethical considerations in AI deployment.