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25 Jan 2022 / 17:30 - 18:15
AI or Not AI: Is there a choice?
Innovation comes in waves that propagate throughout the ecosystem and transform businesses, reshape practices and rattle regulatory frameworks.
Initially a new technology appears to be a differentiator but in time, with a broader adoption, it turns into a requirement. Managing an innovation trend, like incorporation of AI into products, is a challenge with a daunting realization: “The trend is unstoppable. If we don’t take it up, our competitors will.”
While one may not have a choice of whether or not to play, one can still choose how to play. In this session we reflect on the nature of innovation and pre-requisites for taking it from R&D to business development. With AI, the process is complex, without established standards, and requires in-depth expertise to assess quality at specific stages. We reflect on the dilemma between early adoption to guarantee market share and risks of moving into uncharted territories that may lead to suboptimal performance, moral responsibility and possibly a liability.
26 Jan 2022 / 13:00 - 13:45
Working with a black box: Reproducibility and quality assurance in AI
As with any technology innovation, dependability of AI based systems is key to adoption and impact. In highly regulated sectors there are legal requirements for organizations to validate systems before deployment and to reproduce system outputs decades later.
Many AI systems are non-transparent or too complex to reason about and, from the operational perspective, de-facto black-boxes. Therefore, the producers and users of AI must work jointly to define quality standards for such systems and to develop best practices for their quality assessment and quality assurance.
In this session we discuss the importance of AI reproducibility in order to support comprehension, reasoning and reliable application of AI for specific purposes. We reflect on the past technology trends and effects of fast technology obsolescence to motivate actions towards reliable and long-term reproducibility of AI results. With increased use of SaaS models to incorporate AI technologies into solutions, it is particularly important to support verification and validation of AI beyond the market-lifetime of the SaaS platforms.
Natasa Milic-Frayling,
Intact Digital
Dr Natasa Milic-Frayling is a Founder and CEO of Intact Digital Ltd, a company that provides a platform and services for hosting legacy software installations to enable long-term readability and use digital data.
Intact Digital works with highly regulated sectors such as Pharma and Life Sciences to support compliance with the data integrity regulations, reconstruction of research studies and reproducibiity of data analyses.
Natasa has 25 years of experience in computer science research and innovation, including 17 years at Microsoft Research. She authored over 100 research publications and has a dozen of approved patents to her name. She is Professor Emerita at the University of Nottingham where she spent 5 years serving as Chair of Data Science and contributing to the University research strategy on Data Science and AI.
Natasa is actively engaged with a broader professional community on critical issues that arise from wide use of digtial technologies ranging from professional ethic, privacy and design transparency to digital obsolescence and responsible innovation. She served as a member of the Association for Computing Europe Council and as Chair of ACM Women Europe. She is an active member of the Preservation Sub-Committee within the UNESCO Memory of the World Programme and serves as Chair of the Research and Technology Working group for the UNESCO PERSIST project.
