Summary
This module is an exploration of the principles that should help guide the ethical development and deployment of AI systems. We began with fairness, understanding the need for AI to perform equitably across diverse groups. We then examined reliability and safety, ensuring AI operates with integrity and resilience. Privacy and security followed, highlighting the importance of protecting personal data. Inclusiveness reminded us that AI must serve the full spectrum of human diversity. We then transitioned to transparency, emphasizing clear communication about AI's capabilities and limitations. Finally, we arrived at accountability, where developers and operators are responsible for their AI systems' performance and impact.
Each principle isn't just a standalone concept but part of an interconnected framework that, when woven together, forms the fabric of ethical AI. These principles were designed to ensure that as AI becomes more integrated into our lives, it does so in a way that is fair, safe, private, inclusive, transparent, and accountable—ultimately leading to a future where AI empowers everyone and while upholding to the highest ethical standards.
Other resources
To continue your learning journey for this topic, go to:
- Design a system for AI governance - Training | Microsoft Learn
- Applying systems for AI governance - Training | Microsoft Learn
- Empower every student with an inclusive classroom - Training | Microsoft Learn
- Immersive Reader - Training | Microsoft Learn
- HAX Playbook - Microsoft HAX Toolkit
- Detect and mitigate unfairness in models with Azure Machine Learning - Training | Microsoft Learn
- Prepare for the implications of responsible AI - Training | Microsoft Learn
- Fairlearn_WhitePaper-2020-09-22.pdf (microsoft.com)
- Transparency Note and use cases for AI Content Safety - Azure AI services | Microsoft Learn
Bibliography
Model performance and fairness. Microsoft. Published February 27, 2023.
Unit 3. Examine Microsoft AI principles – Fairness. in: Examine how Microsoft is committed to responsible AI. Microsoft. Publication date unavailable.
Unit 3. Identify guiding principles for responsible AI. in: Embrace responsible AI principles and practices. Microsoft. Publication date unavailable.
Unit 8. Understand Responsible AI. in: Get started with AI on Azure. Microsoft. Publication date unavailable.
Unit 5. Examine Microsoft AI principles – Privacy and Security. Microsoft. in: Examine how Microsoft is committed to responsible AI. Microsoft. Publication date unavailable.
Unit 6. Examine Microsoft AI principles – Inclusiveness. in: Examine how Microsoft is committed to responsible AI. Microsoft. Publication date unavailable.
Unit 7. AI tools for educators and accessibility. in: Empower educators to explore the potential of artificial intelligence. Microsoft. Publication date unavailable.
Using Microsoft Teams and Microsoft Translator to host a multilingual parent-teacher conference. Microsoft. Publication date unavailable.
Beatman A. Six ways to improve accessibility with Azure AI. Microsoft. Publication date unavailable.
Unit 7. Examine Microsoft AI principles – Transparency. in: Examine how Microsoft is committed to responsible AI. Microsoft. Publication date unavailable.
Unit 8. Examine Microsoft AI principles – Accountability. in: Examine how Microsoft is committed to responsible AI. Microsoft. Publication date unavailable.
Lee P. Harnessing big data in pediatric research to reimagine healthcare. Microsoft. Published May 21, 2019.
Collaborating to bring AI innovation to education. Microsoft. Published June 26, 2023.