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Panel discussion: 5 perspectives on how to approach Machine Learning risks

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Discover how Microsoft, Equinor, SINTEF DIGITAL, OG21 and DNV are tackling challenges related to ML quality

Machine learning (ML) is increasingly used to solve complex, resource-intensive and sometimes critical tasks. The models need to be fair and unbiased, especially where machine learning is being deployed in sensitive contexts. The impacts of wrongful results from the machine learning systems range from financial loss to a tarnished reputation. At times, the consequences are life-threatening and cause irreversible environmental damage. How are key players in different industries tackling the machine learning assurance challenges?

To answer this question, we invited machine learning, data science and artificial intelligence specialists from Microsoft, Equinor, SINTEF Digital, OG21 and DNV. They share their views and experiences and discuss the following topics:

  1. What challenges arise from the increasing automation of human activities? 
  2. What are the main risks associated with the use of machine learning? 
  3. What are possible approaches to mitigate these risks?

About the speakers


Justin Fackrell, Equinor 

Justin is a Principal Data Scientist at Equinor, working in the Digital Centre of Excellence, a unit that provides ML expertise in collaborative projects across Equinor’s business areas. Justin trained his first ML model, a classification tree to make synthetic speech sound more natural, in the mid 1990’s, and has since applied data science techniques in several fields, including music identification, inline pipeline inspection, maritime safety and energy markets.  He has a BSc from Bristol University, a Master’s from ISVR, Southampton University and a PhD from Edinburgh University. Originally from Wales, he has lived in Norway since 2004. 

Xiaopeng Li, Microsoft  

Xiaopeng leads Cloud & AI business in Microsoft Norway. Before joining Microsoft, Xiaopeng worked as an AI Advisor at Crayon Inmeta where he operated at the intersection of management consulting and data science, supporting enterprise clients in applying AI for real business value. Prior to joining Inmeta, Xiaopeng was Head of Strategy & Product Development at Telia Company's Data Insights Business Unit where he played a key role in establishing the new BU from the ground up. As a devoted community builder, Xiaopeng has co-founded "Oslo AI", a non-profit dedicated to building an AI community in Oslo and Norway. He is also a self-proclaimed evangelist of UN Sustainable Development Goals. Xiaopeng holds a double Master’s degree in Computer Science & Innovation from KTH Royal Institute of Technology in Sweden and Delft University of Technology in the Netherlands. 

Gunnar Hjelmtveit LilleOG21  

Gunnar has more than 25 years of international experience from the oil and gas and the public transportation industries. His experience includes strategy development and implementation, HES management, safety and environmental risk management and technology management. 

Mr. Lille has since 2013 been the Managing Director of OG21, the national technology strategy for the petroleum industry in Norway. Prior to that, Gunnar has worked for Chevron, DNV and ABB. Mr. Lille graduated from the Norwegian Institute of Technology in 1989 with a M.Sc. degree within mechanical engineering. 

Arne Jørgen Berre, SINTEF Digital 

Arne works with Digital Platforms and Systems Interoperability, focusing on Big Data and processing support for Analytics/AI/Machine Learning, involved with this in a number of Norwegian and European Horizon 2020 projects.  He is the Innovation Director of NorwAI (Norwegian Research center for AI Innovation)  and the leader of SN/K 586 KI – the Standard Norway Committee for Artificial Intelligence linked to the  ISO SC42 AI and Big Data committee. Leader of BDVA (Big Data Value Association) TF6 Technical Priorities, GEMINI Center for Big Data with SINTEF, NTNU and UiO and SINTEF BigLearn on Big Data and Machine Learning.   He is Chief Scientist at SINTEF Digital, Department for Software and Service Innovation, Group for Smart Data, and associate professor II at the University of Oslo Department of Informatics. 

Luca Garrè, DNV

Luca is a Data Scientist in the Digital Technology Center at DNV, a unit providing digitalization capabilities across DNV’s business areas. He works primarily with statistical analyses, machine learning and quality assurance of digital artifacts such as models and platforms. Prior to joining DNV, Luca has held research appointments in the academia. He holds a Master’s in Information and Data Science from the University of California, Berkeley and prior to that has earned a Master’s in Engineering and a PhD at the University of Genoa, Italy where he has carried out work on nonlinear structural dynamics and stochastic processes. 


Petter Myrvang, DNV

Petter is the technology lead of the new Digital Health Incubator (DHI) program in DNV. He has gained wide and deep experiences with digital technologies and data since his graduation in 1989. Mainly in the oil & gas and maritime industries, but also within energy and public sectors. Always with a particular focus on opportunities and risks – gains and pains. He has been leading several key initiatives within DNV as well in the industry communities dealing with collaboration, standardization and best practices in implementation and use of digital assets. Such as; data interoperability, data quality, software reliability, data management and cyber security. Petter holds a Master degree in offshore technology from NTH (now NTNU Trondheim).