for Building a Responsible and Ethical AI Solution:
Solution is Designed and Built to Be Explainable and Transparent
All code and workflows can be interpreted understood without having advanced technical knowledge. This often requires additional steps in the design and build of the solution to ensure explainability is achieved and maintained.
Use MLOps Principles to Ensure the Solution is Sustainable and Secure
MLOps brings the principals of DevOps to support the deployment, monitoring and governance of AI solutions through a combination of tools, technologies and practices. We aim to make the AI model implementation fast, reliable, repeatable through the automation of tests and deployment pipelines, using standardized infrastructure across pre-prod and production environments, ensuring solid security is built in throughout and automating monitoring to validate the model continues to work as expected.
Solution Addresses Risks of Potential Bias
We include processes to assess data quality, confirm that training data appropriately represents all target audiences, testing, tracking and alerts for deliberate attempts to expose model bias, bias detection and remediation plans and monitoring for model bias over time to mitigate the risk of potential bias in AI solutions.
Privacy and Consent is Confirmed for All Data Used in the Solution
We recommend active notification to users when they are interacting with an AI system to ensure they are aware. We also advocate providing all users with details when their personal data a solution is collected and for what purpose.
Build in ‘Human in the Loop’ Safeguards for Appropriate Oversight
We design and build AI solutions that maintain an appropriate level of human oversight at all times, including a human override capability should unexpected results or actions occur.