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Explainable Artificial Intelligence (XAI) –
Interpretable Machine Learning
by Edward Adcock
 

Speaker

Edward Adcock
Data Science Consultant,
Delta Capita

DATE 14 November 2019, Thursday
VENUE Seminar Room 2-1,
Level 2,
SMU School of Information Systems
80 Stamford Road,
Singapore 178902
PROGRAMME
6.30pm
(Registration)
7.00pm to 8.30pm
(Presentation and Q&A by Edward Adcock)
 

Synopsis

After a brief look at the challenges facing machine learning adoption into mainstream applications, we will dive into the details of explainability and what this means for regulation and business. Specifically, we will be looking at why XAI is important, what properties it should exhibit and the scope. We will then look at some models that are ‘naturally’ explainable with some relevant financial use cases to help illustrate the points. Next, we will look at some model agnostic methods that can be applied to deep learning architectures and do a deep dive on two specific methods that are being widely adopted. Finally, we will look at the need for a quality assessment framework around explainability and the current best practice.

 

About the Speaker

Ed Adcock joined ‘Delta Capita Ltd’ in early 2018 as the Lead Data Science Consultant. Delta Capita Ltd is a financial services consultancy/management firm with head office in London, UK. He is responsible for the inception, development and deployment of machine learning and data science solutions within the financial services sector including tier 1 banks. Previously, he has worked in the financial services industry for over 15 years in London, UK, working for various stockbrokers, asset managers and financial advisers.

 
 
           
 
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