Interpretable or Explainable-AI?
Future of Fintech
Machine learning (ML) may be the future for investment management, but most ML approaches suffer from a dangerous affliction: the black-box problem. You may be able to observe the inputs to an ML approach, but how the outputs are reached can be a mystery. If as an investment manager you cannot explain your investment decisions to Compliance executives, regulators or clients, you might be exposing your firm to unacceptable levels of legal and regulatory risk*.
Our webinar brings together world authorities on the two solutions currently posed for the black-box problem: interpretable AI, where black-box approaches are rejected in favour of more simple, interpretable models; or explainable AI (XAI), where we attempt to explain the inner workings of black-box approaches.
* see comments from CFA Institute, 2019,; FINRA, 2020
Prof Cynthia Rudin
Cynthia Rudin is a professor of computer science, electrical and computer engineering, statistical science, and biostatistics & bioinformatics at Duke University, and directs the Interpretable Machine Learning Lab. Previously, Prof. Rudin held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is the recipient of the 2021 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI). This award, similar only to world-renowned recognitions, such as the Nobel Prize and the Turing Award, carries a monetary reward at the million-dollar level. She is also a three-time winner of the INFORMS Innovative Applications in Analytics Award, was named as one of the "Top 40 Under 40" by Poets and Quants in 2015, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics.
University College London
Dr Adriano Koshiyama
Dr Adriano Koshiyama is a Research Fellow in Computer Science at University College London, and the founder of Holistic AI, a start-up focused on providing a solution to organizations that want to harness AI ethically & safely. Academically, he has published more than 30 papers in international conferences and journals. He is responsible for ground-breaking results in the intersection of Machine Learning and Finance, with the earlier work on GANs and Transfer Learning in this area. His most recent research paper, 'Towards Algorithm Auditing', has been featured at MIT Sloan Management Review, NYU GovLab, Cisco's Research Centre, and many others
Explainable, Interpretable AI: The Future of Investment Management Schedule
Schedule: 2pm - 4pm London Time (9am - 11am EST)
Friday 19th November 2021
Dr Dan Philps and Professor Ram Gopal introduce one of the hottest topics in finance
Interpret or Explain?
Professor Cynthia Rudin, Duke University
Dr Daniele Magazzeni, JP Morgan XAI center of excellence
Professor Artur d'Avila Garcez, City, University of London
Fast format panel discussion
Applied Interpretable AI
Dr Timothy Law, Rothko Investment Strategies
Dr Adriano Koshiyama, UCL
Dr Pasquale Minervini, UCL
Closing remarks from Dr Dan Philps and Prof Ram Gopal
The Department of Computer Science at Duke University is an internationally recognized leader in research and education. Researchers at Duke use the tools of artificial intelligence to assist with various important societal problems, including (but not limited to) healthcare, antibiotic and cancer resistance, criminal justice, detecting fake news, allocation of public resources to those who need them, environmental sustainability, energy reliability, and political districting. For many of these applications, it is essential that the system satisfy certain interpretability, transparency, morality and/or fairness conditions.
In July 2020, J.P. Morgan created the firmwide Explainable AI Center of Excellence (XAI COE), led by AI Research, to perform cutting-edge research in explainability and fairness. The XAI COE brings together researchers and practitioners to develop and share techniques, tools, and frameworks to support AI/ML model explainability and fairness, and to advance the state-of-the-art by publishing in top AI/ML venues.
City, University of London
City has been at the leading edge of computer science in the UK for six decades; from laying the groundwork for the foundation of the British Computer Society and awarding some of the country’s first Computer Science degrees, to the vibrant, modern department that exists today.
University College London
UCL Computer Science is home to some of the world's most influential and creative researchers in the field of computer science.
Rothko Investment Strategies
Rothko is a systematic investment manager, driven by interpretable Artificial Intelligence (A.I.), specializing in international and emerging equity strategies. We believe alpha can be systematically extracted from inefficient asset classes and that A.I. can be used to learn and shape a systematic approach.
Hosting and Moderating
Professor Ram Gopal
Professor of Information Systems and Management at the Warwick Business School
Dr Dan Philps
Head of Rothko Investment Strategies, Honorary Research Fellow of University of Warwick