I am a Partner Research Area Manager at Microsoft Research. I oversee the research area on human-centered AI, where we advance the state-of-the-art in Responsible AI, human-AI collaboration, sensing, signal processing, productivity, future of work and mental well-being. The team is passionate about developing real systems, tools and prototypes to address the challenges of having AI systems in the real world.
I am an Affiliate Faculty with the University of Washington.
I serve as Technical Advisor for Microsoft’s Internal Committee on AI, Engineering and Ethics. I lead efforts at Microsoft on reliability&safety of AI systems, in particular on developing tools, best practices and guidance towards developing Responsible AI. In my role, my investigations focus on the frontier of Responsible AI, in understanding the new risks and opportunities that arise from the developments in fundamental AI technologies.
My research focuses on developing AI systems that can function reliably in the open world in collaboration with people. I am particularly interested in the impact of AI on society and developing AI systems that are reliable, unbiased and trustworthy.
You can find information about my research and recent publications on my personal website.
Episode 9, January 24, 2018- As the reality of artificial intelligence continues to capture our imagination, and critical AI systems enter our world at a rapid pace, Dr. Ece Kamar, a senior researcher in the Adaptive Systems and Interaction Group at Microsoft Research, is working to help us understand AI’s far-reaching implications, both as we use it, and as we build it. Today, Dr. Kamar talks about the complementarity between humans and machines, debunks some common misperceptions about AI, reveals how we can overcome bias and blind spots by putting humans in the AI loop, and argues convincingly that, despite everything machines can do (and they can do a lot), humans are still “the real deal.”
The webinar will present examples of how these learnings are shaping our research on developing principles and tools for bringing the AI principle of reliability and safety to reality. In particular, it will showcase an ecosystem of open-source tools that are intended to accelerate the machine learning (ML) development life cycle by identifying and mitigating failures in a faster, systematic, and rigorous way.