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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:
      {
        "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:
      {
        "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