1. Quant Foundry
Labs – Forge - Works
Pearls of Risk Wisdom For Artificial Intelligence Models
Chris Cormack and David Kelly
• Artificial Intelligence (AI) is at its core a stack of mathematical models from statistics, calculus
and algebra – however there is more to what AI can achieve
• People who make decisions off the back of a recommendation from an AI model really need to
appreciate what it is doing – black boxes are only useful in aircraft and are actually yellow, this
must be matched with the greater understanding of individuals that apply it
• Users must still appreciate that “Garbage in Garbage out” still applies to AI - choosing the right
data sets and features is more art than science and requires the right expertise
• AI should not be allowed to propagate in a lazy, unaccountable way – it requires critical
supervision and monitoring at all times by those with detailed business and technical
understanding- this requires the right type of management in an organisation
• If you look hard enough, you will be able to find a correlated pattern of behaviour across a wide
enough of data – but that is not the same a causality
• Predictions must always be probability-weighted together with heavy caveats – beware of those
seeking absolutes in uncertainty
• Models that learn from history may cement biases around areas such as gender, race and
location that are considered unacceptable – this creates reputational risk
• Applying pressure on the quants to perform will develop models that conform to management’s
viewpoint, but are likely to lead to unintended consequences or absurd predictions
• Understand that AI and Machine Learning will not at this point solve all problems in an
organisation beware of snake oil to grease the wheels - understand the value proposition.
• Be aware of strategic motivation for building AI capability. Understand the ethical choices in
terms of the impact on both employees, customers and society at large
HAPPY NEW 2019 FROM ALL OF US AT THE QUANT FOUNDRY
