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Our Commitment To Ethical AI

Every organization should benefit from AI’s potential to transform their business processes. But beyond its technological capabilities – we have the responsibility of ensuring that AI is human centered and socially beneficial, fair, safe, and inclusive.

two people crossing a road from above

Our Ethical AI Core Principles

Human Centered and Socially Beneficial
AI solutions should be designed to support human goals and objectives, with human and societal needs in mind.

Fair and Accountable
Fairness should prevail in every interaction, eliminating any type of discrimination and undesirable bias.

Explainable and Transparent
We make sure our technology is transparent and explainable; our customers control their own data & data models.

Secure and Safe
AI solutions should be implemented in a way that the data used is secure, protecting the privacy of the people.

Human Centered Automation & Ethical AI

Our goal is to develop technology grounded in how work flows through an organization, that makes humans an integral part of the long-term solution to growing data processing needs—needs that are currently beyond human capacity.

Using a human centered approach to automation, the Hyperscience Platform helps enterprise organizations and government agencies transform how they approach and prioritize work, resulting in fewer mistakes and fairer decisions and positively impacting the lives of millions of their customers.

Meet Our AI Ethics Committee

Erin Millender

VP Legal

Erin serves as VP of Legal and is responsible for advising on day-to-day legal and business matters including venture financing, corporate governance, risk management, technology transactions, data privacy, employment law matters, IP portfolio management, entity formation and other strategic initiatives. She is also Co-Chair of Northwestern Pritzker School of Law Alumni Club of New York.

Tony Lee

Chief Technology Officer

Tony serves as Chief Technology Officer at Hyperscience, leading the Product, Design and Engineering teams. He has held senior leadership roles at Yahoo, Box, Zendesk, and Dropbox. Tony began his 25-year engineering career at NASA, where he worked on automation software for air traffic control, and later continued his research into computer network optimization. Tony worked on distributed software systems while at Vitria Technology. He later built world-class teams at Box and Zendesk, and led the development of a next generation collaboration product at Dropbox. Tony holds a PhD in Engineering, and a combined degree in Engineering and Political Science from Brown University.

Paz Macdonald

Chief Marketing Officer

Paz serves as Chief Marketing Officer at Hyperscience. She holds nearly 25 years marketing leadership and management experience from Enterprise and SaaS software companies including MongoDB, Cisco, Samsung, IBM and HP. She joined from Software AG, where as CMO, she spearheaded the company’s marketing transformation. Paz was one of the early hires at MongoDB, building and growing marketing and sales expansion for international markets. Paz holds a Professional Post-Graduate Diploma in Marketing from the Chartered Institute of Marketing, and a BSc (Hons) degree in Economics from Loughborough University.

Ching-Fong Su

VP Machine Learning

Ching Fong (CF) serves as VP Machine Learning at Hyperscience. He has more than 15 years of R&D experience in the tech sector. His expertise includes areas of search ranking, content classification, online advertisement, and data analytics. Prior to joining Hyperscience, CF was the Head of Machine Learning at Quora, where his teams developed ML applications of recommendation systems, content understanding, and text classification models. Before Quora, he held technical leadership positions at Polyvore (acquired by Yahoo), Shanda Innovations America, and Yahoo Search. He was also a senior researcher at the Fujitsu Lab of America.CF received a Master’s and a Ph.D. degree in Electrical and Computer Engineering from the University of Texas at Austin.

In the push for development, is the U.S. prepared to regulate AI?

CF Su, VP of Machine Learning

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