Expert Series: Principles to Promote FEAT in the Use of AI and Data Analytics | SGInnovate
November 15 2019


32 Carpenter Street
Singapore 059911

Expert Series: Principles to Promote FEAT in the Use of AI and Data Analytics

Presented by Monetary Authority of Singapore, SGInnovate and EY

In line with the principles to promote Fairness, Ethics, Accountability and Transparency (FEAT) in the use of Artificial Intelligence and Data Analytics (AIDA) in Singapore's financial sector, the Monetary Authority of Singapore (MAS), SGInnovate and EY are co-presenting an Expert Series where we bring together global thought leaders, financial sector leaders and AIDA experts to explore ways firms can recalibrate their current frameworks to realign with the FEAT principles in providing financial products and services. 

Date: 15 November 2019, Friday
Time: 9:00am – 12:00pm
Venue: 32 Carpenter Street, Singapore 059911


  • Dr Nicolas Chapados, Co-Founder & Chief Science Officer, Element AI
  • Prof. Simon Chesterman, Dean – Faculty of Law, National University of Singapore

Programme Details:
9:00am – 9:30am: Guest arrivals
9:30am – 10:15am: Talk by Dr. Nicolas Chapados, Co-Founder & Chief Science Officer, Element AI

Raising the Standard with Explanability 

In the 20th century, chemistry made a transition from science to engineering. In order to be industrially useful, the field needed robust standardisation to prevent explosive errors. Turning it into an engineering practice allowed for highly controlled, and safe, product development. In the coming years, AI will need a similar transition.

There are numerous efforts around the world to create the necessary standards, including the FEAT principles for the financial industry. Adhering to, as well as monitoring, the FEAT principles will be an enormous technical challenge in itself, and developing explainability techniques will be key to this effort. In this talk, Dr. Nicolas Chapados will dive into the explainability field, what it is and how it can be applied to greater transparency for customers, detecting bias, ethical compliance, improving models, and defending against adversarial attacks.

10:15am – 11:00am: Talk by Prof. Simon Chesterman, Dean – Faculty of Law, National University of Singapore

Artificial Intelligence and the Problem of Opacity

This presentation will address the regulatory challenges posed by the increasing opacity of artificial intelligence (AI) systems affecting the rights and obligations of individuals. As computer programmes become more complex, the ability of non-specialists to understand how an AI system has reached a given output diminishes. Opaqueness may also be built into programmes by companies seeking to protect their proprietary interests. Both types of system are capable of being explained, either through recourse to experts or an order to produce information. Another class of system may be naturally opaque, however, using deep learning methods that are difficult or impossible to explain in a manner that humans can comprehend.

Such opacity gives rise to three sets of challenges. First, opacity may encourage — or fail to discourage — inferior decisions by removing the potential for oversight and accountability. Secondly, it may allow impermissible decisions, notably those that explicitly or implicitly rely on categories such as gender or race in making a determination. Thirdly, it may render illegitimate decisions in which the process by which an answer is reached is as important as the answer itself.

11:00am – 12:00pm: Facilitated Q&A and Discussion


Speakers' Profiles:

Dr. Nicolas Chapados, Co-Founder & Chief Science Officer, Element AI

Dr Nicolas Chapados is Co-Founder and Chief Science Officer at Element AI. He holds an Engineering Degree from McGill University and a PhD in Computer Science from the University of Montreal. In 2001, while still writing his thesis, he co-founded Machine Learning Technology Transfer company ApSTAT Technologies with his advisor Yoshua Bengio.  ApSTAT Technologies applies cutting-edge research ideas to areas such as insurance risk evaluation, supply chain planning, business forecasting, national defence, and hedge fund management. From this, Dr Chapados also co-founded spin-off companies Imagia - to detect and quantify cancer early with AI analysis of medical images, Element AI - to help organizations plan and implement their AI transformation, and Chapados Couture Capital - a quantitative asset manager. Dr Chapados holds the Chartered Financial Analyst (CFA) designation.

Prof. Simon Chesterman, Dean – Faculty of Law, National University of Singapore

Professor Simon Chesterman is Dean of the National University of Singapore Faculty of Law. He is also Editor of the Asian Journal of International Law.

Educated in Melbourne, Beijing, Amsterdam, and Oxford, Professor Chesterman's teaching experience includes periods at the Universities of Melbourne, Oxford, Southampton, Columbia, and Sciences Po. From 2006-2011, he was Global Professor and Director of the New York University School of Law Singapore Programme.

Prior to joining NYU, he was a Senior Associate at the International Peace Academy and Director of UN Relations at the International Crisis Group in New York. He has previously worked for the UN Office for the Coordination of Humanitarian Affairs in Yugoslavia and interned at the International Criminal Tribunal for Rwanda.

Professor Chesterman is the author or editor of seventeen books, including Law and Practice of the United Nations (with Ian Johnstone and David M. Malone, OUP, 2016); One Nation Under Surveillance (OUP, 2011); You, The People (OUP, 2004); and Just War or Just Peace? (OUP, 2001).

He is a recognized authority on international law, whose work has opened up new areas of research on conceptions of public authority - including the rules and institutions of global governance, state-building and post-conflict reconstruction, the changing role of intelligence agencies, and the emerging role of artificial intelligence and big data. He also writes on legal education and higher education more generally.

Topics: AI / Machine Learning / Deep Learning, Data Science / Data Analytics