Rohan Massey, Marc Groman, Sajai Singh, Stephen Burns, Patricia Martín-Marrero
There is great commercial advantage to be gained from the use of large learning data sets, but what commercial and regulatory issues must companies be addressing, and how should the benefits of sharing data sets be assessed.
Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical/financial or other applications, need data. The more data the better. However, due to commercial, regulatory, ethical, and national security reasons, including data privacy, not all data can or should be made publicly available or shared with other parties. In the private sector, sharing of valuable data in an AI model available to a broad pool of companies (including competitors) further reduces the incentive to share data, especially where the resulting AI models would be available for all partners irrespective of their individual contribution. The panel will discuss approaches to data sharing and the commercial and regulatory issues that should be considered in assessing how and what data to share.
Rohan Massey, Partner, Ropes & Gray
Marc Groman, Principle, Groman Consulting Group
Sajai Singh, Partner, J. Sagar Associates
Stephen Burns, Partner, Bennett Jones
Patricia Martín-Marrero, Lead Counsel, R&D Data & Technology, Takeda Pharmaceuticals
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