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| # Interview AI Detector | |
| ## Overview | |
| Interview AI Detector is a machine learning model designed to distinguish between human and AI-generated responses during interviews. The system is composed of two models: | |
| 1. **ALBERT Model**: Processes text features extracted from responses. | |
| 2. **Logistic Regression Model (LogReg)**: Utilizes the output from the ALBERT model along with additional behavioral features to make the final prediction. | |
| The model is deployed on Google Vertex AI, with integration managed by a Kafka consumer deployed on Google Compute Engine. Both the model and Kafka consumer utilize FastAPI for API management. | |
| ## Architecture | |
| ### ALBERT Model | |
| - **Source**: HuggingFace | |
| - **Input**: 25 numerical features extracted from the text, including: | |
| - Part-of-Speech (POS) tags | |
| - Readability scores | |
| - Sentiment analysis | |
| - Perplexity numbers | |
| - **Output**: Features used as input for the Logistic Regression model | |
| ### Logistic Regression Model | |
| - **Input**: | |
| - Output from the ALBERT model | |
| - 4 additional features, including typing behavior metrics such as backspace count and key presses per letter | |
| - **Output**: Final prediction indicating whether the response is human or AI-generated | |
| ## Deployment | |
| - **Model Deployment**: Vertex AI | |
| - **Kafka Consumer Deployment**: Compute Engine | |
| - **API Framework**: FastAPI | |
| - **Training**: | |
| - **Epochs**: 8 | |
| - **Dataset**: 2000 data points (1000 human responses, 1000 AI-generated responses) | |
| - **Framework**: PyTorch | |
| ## Usage | |
| ### API Endpoints | |
| - **POST /predict**: | |
| - **Description**: Receives a pair of question and answer, along with typing behavior metrics. Runs the prediction pipeline and returns the result. | |
| - **Input**: | |
| ```json | |
| { | |
| "question": "Your question text", | |
| "answer": "The given answer", | |
| "backspace_count": 5, | |
| "letter_click_counts": {"a": 27, "b": 4, "c": 9, "d": 17, "e": 54, "f": 12, "g": 4, "h": 15, "i": 25, "j": 2, "k": 2, "l": 14, "m": 10, "n": 23, "o": 23, "p": 9, "q": 1, "r": 24, "s": 19, "t": 36, "u": 9, "v": 6, "w": 8, "x": 1, "y": 7, "z": 0} | |
| } | |
| ``` | |
| - **Output**: | |
| ```json | |
| { | |
| "predicted_class": "HUMAN" or "AI", | |
| "main_model_probability": "0.85", | |
| "secondary_model_probability": "0.75", | |
| "confidence": "High Confidence" or "Partially Confident" or "Low Confidence" | |
| } | |
| ``` | |
| ## Limitations | |
| - The model is not designed for retraining. The current implementation focuses solely on deployment and prediction. | |
| - The repository is meant for deployment purposes only and does not support local installation for development. | |
| ## Author | |
| Yakobus Iryanto Prasethio |