Azure Responsible AI

Azure Responsible AI Cheat Sheet

Microsoft outlines six key principles for responsible AI: accountability, inclusiveness, reliability and safety, fairness, transparency, and privacy and security.

Accountability

• People designing and deploying AI systems need to be accountable for their actions and decisions.
• Internal review bodies can provide oversight and guidance in AI systems.
• Considers the systems’ impact and integrating ethical considerations into their development

Inclusiveness

• AI should consider all human races and experiences. Inclusive design practices can address potential barriers.
• Assistive technologies should be used to empower people with impairments.
• Development of systems that respect and include all users

Reliability and safety

• AI systems need to be reliable, safe, and resistant to manipulation.
• Rigorous testing, validation, monitoring, and model tracking processes are important.
• Utilizing a heatmap is a tool for ensuring reliability and safety in AI systems

Explainability

• AI systems should be able to justify their decisions and comply with policies, standards, and regulations.
• Tools like InterpretML help achieve model explainability.

Fairness

• AI systems should not discriminate based on gender, race, sexual orientation, or religion.
• Microsoft provides an AI fairness checklist and tools like Fairlearn for fairness assessment and improvement.
• Ensuring that AI systems treat all individuals equally and do not result in unjust or prejudicial treatment.

Transparency

• Transparency helps understand the data, algorithms, and transformation logic used in AI models.
• Azure Machine Learning provides snapshots for transparency and reproducibility.
• Aims to make AI systems more understandable and explainable to users.

Privacy and security

• Personal data in AI systems should be protected and accessed without compromising privacy.
• Azure differential privacy helps preserve privacy by randomizing data and adding noise.
• Safeguards AI systems against cybersecurity threats.
• Concerned with the protection of user data, ensuring the confidentiality, integrity, and availability of that data
  • • Require transparency about the collection, use, and storage of data.

    • Mandate that consumers have appropriate controls to choose how their data is used

References:

• Learn Azure, Coud Adoption Framework: Responsible and trusted AI
  Responsible and trusted AI – Cloud Adoption Framework

• Learn Azure, Azure Machine Learning: Responsible AI
  What is Responsible AI – Azure Machine Learning

• Learn Azure, Azure Machine Learning: Workspace
  What is a workspace? – Azure Machine Learning

• Microsoft Learn : Trusted AI
  https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai

• Microsoft Learn : Privacy and Security
  https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2#privacy-and-security

• Microsoft Learn : Concept Fairness https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2

• Privacy Preserving Machine Learning Innovation
  https://www.microsoft.com/en-us/research/group/privacy-preserving-machine-learning-innovation/

• Concept Responsible AI https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2#privacy-and-security