14–17 Oct 2019

Executive Briefing: Designing and building responsible AI

13:4514:25 Wednesday, 16 October 2019
Location: Windsor Suite
Average rating: *****
(5.00, 3 ratings)

Level

Beginner

In the rapidly changing world of tech and AI, adopting the right design principles is key. IBM Watson applies ethical AI principles and user-centered design principles from the beginning and leverages them throughout the product development cycle.

Ariadna Font Llitjós details the five areas of ethical focus to build responsible AI systems and products, which include user-design best practices that are becoming part to IBM Watson’s designers’ and developers’ toolkit. These sets of principles, along with a strong background in design thinking and user-centered design, enable product teams to design and build systems that best serve key client persons. From data scientists to business users and client end users, IBM Watson puts ML and NLP to the service of the people, always seeking to augment their capabilities, instead of adding to the cognitive load and overwhelming them.

Photo of Ariadna Font Llitjós

Ariadna Font Llitjós

Twitter

Ariadna Font Llitjós is the director of engineering of the Cortex Machine Learning (ML) Platform at Twitter, focusing on enabling teams and increasing their productivity, with the end goal to help facilitate a healthy public conversation and reduce miss information, and she’s the engineering site executive for the New York City office, responsible for growing the engineering and design teams, as well as making Twitter NYC the best place to work by instilling a culture of inclusiveness and growth. Ari is passionate about bringing innovation to market and making intelligent systems easy to use, always putting people at the center. Previously, Ari was director of product development and design principal at IBM Watson (data and AI), where she led a large global team of engineers focused on the Watson discovery portfolio. With her teams, she worked to infuse Watson solutions and applications with knowledge and natural language understanding, leveraging machine learning (ML), neural networks (NN), and natural language processing (NLP) techniques, turning unstructured data into knowledge in a way that improves both the offerings and the user experience. Leveraging Agile, Lean, design thinking, and Lean UX best practices, Ari has been leading teams of developers, designers, and researchers for the last eight years. She earned her PhD in language and information technologies in the School of Computer Science at Carnegie Mellon University. Her PhD research focused on improving machine translation quality and accuracy by developing a largely automated approach that used online postediting feedback to refine translation rules.

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