End-to-end debiasing

Our product helps you during the three main steps of AI deployments. First, via Bias Audit, we assist you to achieve a thorough understanding of your data, such as identifying the latent features, distribution of data samples, and identifying areas of your data to improve. Second, comes our Debiased Training which integrates into your training pipeline and trains a fair model even if your data is biased or imbalanced. Finally, the Model Certification certifies the fairness of your trained model on all the protected classes and features. Our technology is industry agnostic, unsupervised, and works on any data type.

Request Demo

1. Bias Audit

Our technology extracts latent features, calculates their distribution, and identifies the under and over-represented groups. Our Bias Audit outputs an interactive visualization that helps users gain an in-depth understanding of their dataset. Additionally, it suggests different strategies to improve your dataset before passing it into the training pipeline.

  • Identify noisy data samples.
  • Extract and identify all the informative underlying features in your dataset.
  • Perfect tool for engineers and analyst to understand useful metrics about their data and learn how to improve it.
  • Designed both for ML experts and Bussiness experts.
    • Bias Audit

    2. Debiased Training

    Debiased Training Themis AI

    It's impossible to gather a perfect dataset. Thus we designed our Debiased Training to come to the rescue. Debiased Training is designed to train your model fairly even if your training data is heavily biased or imbalanced. All you need to do is: install our product, import debiased training into your code, and voila!

    Train your model fairly even when utilizing:

  • Historical data that carries social biases.
  • A dataset that doesn't include enough samples of all the different demographics.

    • Achieve the highest accuracy even when:

  • Your dataset is heavily imbalanced.
  • You cannot improve your dataset on the under-represeented groups.

    • 3. Model Certification

    Once your model is trained, the Model Certification certifies the fairness of your model on all the protected classes and features in your dataset. This step is critical before deployment to keep companies safe from lawsuits and the unethical use of AI.

  • Ensure fairness of your AI model not only on explicit labels but also on latent features.
  • Avoid and win lawsuits by providing scientific proofs of your models fairness.
  • Gain positive publicity and attract more customers by utulizing fair AI in your automated decision making.
    • Model Certification Themis AI