Ethics & Regulation – AI (Machine Learning) Challenges In Financial Fairness
Machine-learning is used in a wide variety of contexts from trying to replicate human sensory interpretation to mining massive datasets.
Machine-learning has been used in financial services for decades, however, the confluence of larger datasets, increased connectivity, and distributed computing power has resulted in a torrent of applied systems, many old in design, some new.
Michael will provide a swift survey of some of the applications that are increasingly applied to sustainable investment, e.g. asset mapping & tracking, smart ledger timestamping, idea mining, and trade surveillance.
He will also touch on some of the dangers and perils in the application of machine-learning in finance. He will conclude with some of the paradoxes that machine learning poses in ethics and regulation.
Some learning points include:
- Machine learning and AI ethical issues are not new,but are now more complex.
- There are two different standards for evaluating machine learning applications, the human and the statistical.
- Bias in machine learning systems cannot be removed, but it can be governed.
Michael Mainelli, Co-Founder, Z/Yen Group