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People-centered AI: ​the principles that guide us

When coupled with automation, AI can dramatically speed processes, improve decisions, and free people from an ever-wider range of repetitive tasks. But as with any powerful, transformative technology, AI must be thoughtfully managed to maximize its positive impact. We are committed to enabling the responsible and ethical application of AI + automation for UiPath, our customers, and our partners.​

Our core principles

We believe all AI + automation systems should be developed and implemented based on the following standards:​

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Explainability​

The factors that underlie  AI systems’ predictions or decisions should be visible, understandable, and reasonable.​

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Privacy & security​

AI systems should respect privacy to the utmost and ensure that all data are held in secure environments with multiple layers of permissions.​

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Social benefit​

AI systems should be designed to enable a fairer, more equitable society, empowering people and their organizations to identify and weed out bias, make more thoughtful decisions, and expand diversity and inclusivity.​

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Accountability​

Users should be made aware of where and where AI systems are being employed. They should also be allowed and encouraged to report faults and provide feedback.​

Our goal is to help foster research

Our goal is to help foster research that leads to the next wave of transformational technologies at the intersections of automation and AI and ensure human-centered automations are deployed to establish ethical, inclusive, and accountable AI systems.”

UiPath AI Advisory Board

Watch Dr. Etzioni’s keynote at the 2022 UiPath AI Summit

Our AI Advisory Board

Leading experts help guide our efforts to develop and support human-centered AI + automation​

Dr. Oren Etzioni, Chairperson

Dr. Oren Etzioni, Chairperson

Since its founding in 2014, Dr. Etzioni has served as CEO of the not-for-profit Allen Institute for AI (AI2), which focuses on AI research and engineering for the common good. An accomplished AI theorist and practitioner, he's published 100+ papers on AI and founded several companies.

Daniel S. Weld, PhD

Daniel S. Weld, PhD.

Dr. Weld is Professor Emeritus at the Paul G. Allen School of Computer Science & Engineering, and the General Manager and Chief Scientist of Semantic Scholar at the Allen Institute of AI. Dr. Weld runs the Lab for Human-AI Interaction, which helps people better understand and control AI tools, assistants and systems.

Sarah E. Chasins, PhD.

Sarah E. Chasins, PhD.​

As Assistant Professor of Computer Science at UC Berkeley EECS, Dr. Chasins’ work focuses on democratizing programming, especially for social scientists, data scientists, and other non-traditional programs. Her goal is to make programming tools so usable that automating a task is easier than teaching the same task to a human.​

More to explore

Learn more about AI + RPA