Exploring the landscape of AI-generated texts reveals their diverse applications in Content Generation, Personalized Marketing, Virtual Assistants, and Creative Writing. However, these advancements come with inherent risks such as the spread of misinformation, perpetuation of biases, accountability challenges, and privacy concerns. To navigate these complexities, our project focuses on developing a cutting-edge machine learning algorithm. This algorithm aims to adeptly differentiate between AI-generated and human-generated texts, providing a robust solution to elevate content authenticity and effectively address associated risks.
- Loads and preprocesses text data (replace with specific steps if applicable).
- Defines a model for AI-generated text detection (replace with specific model details if applicable).
- Evaluates the model's performance (e.g., calculates accuracy, precision, recall).
- Generates visualizations (e.g., confusion matrix, ROC curve) to analyze results (replace with specific visualizations if applicable).
This project requires the following Python libraries:
- pandas
- numpy
- seaborn
- matplotlib
- scikit-learn
Install the required libraries. You can find them listed in the requirements.txt file. Open Detect-AI-Generated-Text.pynb in a Jupyter Notebook environment or a compatible platform (e.g., Google Colab). Run the notebook cells sequentially.
The notebook generates outputs like classification reports and visualizations to help analyze the model's effectiveness in identifying AI-generated text.