Demystifying Artificial Intelligence and Machine Learning Explainability

We’re convening public and private leaders to highlight the importance of building trust with customers and regulators to tackle explainability.

Session Recap: To read a recap of the key takeaways from this session, please click here.

 

Where: Virtual roundtable

When:  June 30, 2021, 9:00 a.m. EDT

Overview:

In the third installment the Institute of International Finance’s (IIF) DataTalk series, an interactive monthly forum bringing together experts from IIF member firms, leading tech firms, and other partners and officials, participants discussed artificial intelligence (AI) and machine learning (ML) explainability.

The discussion focused on how financial institutions are increasingly using AI and ML to make better use of data and increase efficiencies. Participants explained how it’s important to start by building high-level principles around the use of AI and ML, and discussed their experience using them to broaden their approach to develop a company-wide data ethics framework that goes further than just AI and ML.

The session went on to cover taking a layered approach that is interlinked with the risk and materiality of the specific use case, inherent and post-hoc explainability techniques, and the importance of governance in this context. There was also consensus around the role of regulation as opposed to supervision regarding the continued development of new techniques and technologies, with an emphasis on robust supervision over regulation.

The series includes three global leading experts as its co-chairs:

    Douglas Elliott, Partner, Oliver Wyman

    Jennifer Stott, Chief Data Officer, Royal Bank of Canada

    Tom Wilson, Chief Executive Officer, Allianz Ayudhya

    Attendance for the DataTalk series is available by invitation only. To get in touch, email us at OWForum-Data@oliverwyman.com. You can learn more about our collaboration with IIF here.