Artificial Intelligence and Machine Learning in Capital Markets: Considerations for a Broad Framework for Transparency
18 Sep 2019
As the adoption of Artificial Intelligence (AI) and Machine Learning (ML) in capital markets continues at pace, attention is increasingly being focused on how capital markets firms can demonstrate a responsible approach to their use of the technology. This white paper has been developed by AFME’s AI Task Force to consider how to approach transparency in AI/ML, which is a key factor in demonstrating and ensuring the safe and effective deployment of trustworthy AI/ML in capital markets. The paper suggests a technology-neutral, principles-based approach to transparency, built around the assumptions used in the development of AI/ML models and testing of those models, to meet stakeholder needs.
In any use of AI/ML, transparency is important to a wide range of stakeholders, as it can demonstrate how an AI/ML model has been developed, how it will be used and monitored, and how it can stand up to scrutiny and challenge. This is crucial for building trust in the technology, both within a firm and with external stakeholders such as clients and regulators.
This is the third in a series of white papers produced by AFME’s AI Task Force. The first paper considered the use-cases, benefits and risks of AI in capital markets, while the second paper explored ethical considerations. This third, more technical, white paper discusses the concept of transparency in AI/ML.