Our Responsible AI Assessment

The Responsible AI Assessment (RAIA) helps assess the blueprint’s requirements and design to use AI safely & responsibly in your organization.
Implementing AI responsibly brings a lot of challenges. Truly want to be sure that your AI Models are ready for the next step and are built to last, our Responsible AI Assessment supports your implementation.

 

In a nutshell, the challenges of implementing AI responsibly:

  • Struggle to deploy and implement AI/ML into business processes
  • Limited tools to ensure human oversight and establish links between business experts and data science teams
  • Lack of holistic, tailored approach to achieve Responsible AI
  • Regulatory uncertainty due to a variety of legislations for different AI-supported use cases (e.g., risk, fraud, KYC, …)
  • An expected increase in regulatory pressure
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In four steps to your AI readiness:

Setting the scene:

During the first phase of the assessment, the focus will be on current ML capabilities, alignment between key stakeholders and a deep-dive analysis of the requirements needed.

  • Key stakeholder interviews
  • Deepdive analysis
  • Use Case development

Providing the framework to implement Responsible AI:

During the next phase, the framework to implement Responsible will be our focus area, all based on the (technical and operational) requirements of the prioritized use cases. The framework contains documentation on data- and model infrastructure, documentation on AI governance & processes, and lastly advice in implementation, containing:

  • Transparency and Explainability
  • Robustness & Fairness
  • Human Oversight and feedback
  • Control of your ML model

Setting the base for deployment:

The Data/MLOps infrastructure blueprint is the prioritized and detailed overview of the activities needed to deploy the selected ML use case. The blueprint contains of current- and future situations but also….

  • Designed a Data/MLOps infrastructure blueprint
  • Required MLOps processes:
    • Manageability: monitoring, alerts, drifts
    • Accountabality: ownership
    • Explainability: explainers, feedback loop and explanation design

The Result: everything is set in stone, ready for responsible usage of AI.

After the Responsible AI Assessment, a roadmap and implementation into Deeploy will be delivered with a detailed overview of the activities needed.

  • Have an applied use case and Proof of Concept
  • Implementation into Deeploy based on key deliverables 1, 2 and 3
  • Summary report: Advice & Guidance