- Put Responsible AI into Practice—
attend the digital event on December 7 - Nov 30, 2021.
Learn best practice guidelines for building AI solutions responsibly. Join AI experts from Microsoft and BCG at Put Responsible AI into Practice—a free Azure digital event on December 7.
- Stop Blaming Humans for Bias in AI - Nov 19, 2021.
Can artificial intelligence be rid of bias? This is an important question, and it’s equally important that we look in the right place for the answer.
- Machine Learning Safety: Unsolved Problems - Nov 5, 2021.
There remain critical challenges in machine learning that, if left resolved, could lead to unintended consequences and unsafe use of AI in the future. As an important and active area of research, roadmaps are being developed to help guide continued ML research and use toward meaningful and robust applications.
- The Case for a Global Responsible AI Framework - Oct 30, 2021.
Public and private organizations have come out with their own set of AI principles, focusing on AI-related risks from their perspective. However, it’s imperative d=to have a global consensus on Responsible AI – based on data governance, transparency and accountability – on how to utilize and benefit from AI in a way that is both consistent and ethical.
- Coding Ethics for AI & AIOps: Designing Responsible AI Systems - Aug 26, 2021.
AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.
- Towards a Responsible and Ethical AI - Jul 30, 2021.
It is not the technology at fault, but the intention.
- Navigate the road to Responsible AI - Dec 18, 2020.
Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.
- Machine Fairness: How to assess AI system’s fairness and mitigate any observed unfairness issues - May 26, 2020.
Microsoft is bringing the latest research in responsible AI to Azure (both Azure Machine Learning and their open source toolkits), to empower data scientists and developers to understand machine learning models, protect people and their data, and control the end-to-end machine learning process.
- Machine Ethics and Artificial Moral Agents - Nov 2, 2017.
This article is simply a stream of consciousness on questions and problems I have been thinking and asking myself, and hopefully, it will stimulate some discussion.
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