Blog

In the previous two blogs in the series, we addressed what makes machine learning models trustworthy and how machine learning models fail. This blog is focused on how machines learn and one particular approach called meta-learning, which enables machine learning models to improve over time such that they can generalise to perform related but distinct tasks. We will look at how humans learn compared to machine learning, what meta-learning is, and how meta-learning can be done responsibly.

In our first blog in this four-part series, we discussed how Machine Learning (ML) models might be considered ‘trustworthy’, and we emphasised challenges in bias and fairness, the need for explainability & interpretability, and types of data drift.

This blog will focus on how ML models fail, the value of model observability, monitoring and governance, and most importantly, how we can prevent model failure.

Last year, Mind Foundry worked closely with Professor Brent Mittelstadt (of the Oxford Internet Institute) to get a deeper understanding of the law, policy, and standards in development surrounding machine learning. The output of this work was an in-depth research paper highlighting the key aspects of machine learning governance. This 4-part blog series summarises some of the key points from that paper to give a flavour of the most pressing problems in AI governance and some of the solutions that Mind Foundry are developing to solve them. 

Mind Foundry are thrilled to have been included in the ‘Ethical AI startup landscape’ research, mapped by researchers at the EAIGG (Ethical AI Governance Group), who have vetted nearly 150 companies working in Ethical AI across the globe. This important research by the EAIGG is being conducted to provide transparency on the ecosystem of companies working on ethical AI.