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AI Risk Management

AI Risk Management

Holistic AI’s  software platform is a scalable and structured solution that empowers your enterprise to minimise AI risks, adopt and scale AI with confidence,  and enhance business performance.

AI inventory and risk management across all systems.

The Holistic AI platforms operate based on 5 key AI risk verticals: Efficacy, Robustness, Privacy, Bias, and Explainability


The risk that a system underperforms relative to its use-case. Efficient and robust models performs well not only on the data used to train the model, but also on unseen data. This step includes:

  • Estimating generalisation
  • Model validation for hyperparameters tuning
  • Performing algorithmic selection


The risk that the system fails in response to changes or attacks. The performance of a model tends to decrease over time due to changes in dynamic environments. There is also the risk posed by adversarial threats by malicious actors. This step covers:

  • Detecting, and handling dataset shifts
  • Defending against adversarial threats


The risk that the system is sensitive to personal or critical data leakage. Machine learning is intrinsically reliant on data, which can often be personal or sensitive. As machine learning increases in popularity, it is critical to learn how to protect privacy. This step covers:

  • Data minimisation
  • Privacy-preserving techniques


The risk that the system treats individuals or groups unfairly. Machine learning can be used in critical applications, like recruitment or the judicial system. In these cases, it is especially important to ensure that algorithms do not discriminate and treat everyone equally. This step covers:

  • Measuring bias in systems
  • Mitigation of bias


The risk that an AI system may not be understandable to users and developers. Explainability is essential for building and maintaining trust across the whole ecosystem of stakeholders. This step covers:

  • Datasheets for datasets
  • Model cards for model reporting

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