README.md

metadata

title: Interview AI Detector
emoji: 🧐
colorFrom: red
colorTo: green
sdk: docker
pinned: false

Interview AI Detector

Playground

https://panduwana-interview-ai-detector.hf.space/docs

Dev setup

Requirements:

• Python 3.10.x (may have problems with higher versions)
• Accept the agreement for https://huggingface.co/google/gemma-2b

git clone [email protected]:rekrutmen_plus/interview-ai-detector.git
cd interview-ai-detector

With Docker

Local build/run

``` cp COPY_THEN_EDIT.env .env # then FILL IT OUT pip install python-dotenv transformers huggingface_hub python download-huggingface-model.py google/gemma-2b docker build -t interview-ai-detector --secret id=dotenv,src=.env . PORT=8080 docker run -p $PORT:7860 interview-ai-detector ``` Then open: http://localhost:8080/docs/

Using Huggingface Spaces

- define a secret: HUGGINGFACE_TOKEN in the space's settings - git push --force

Without Docker

python --version # ensure 3.10.x
pip install -r requirements.txt
python huggingface-download-model.py google/gemma-2b
python -m nltk.downloader punkt wordnet averaged_perceptron_tagger
unzip ~/nltk_data/corpora/wordnet.zip -d ~/nltk_data/corpora/
PORT=8080 uvicorn prediction:app --host 0.0.0.0 --port $PORT

Then open: http://localhost:8080/docs/

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.

Author

Yakobus Iryanto Prasethio