Trustworthy AI Without Trusted Data

Summary

Today, almost everybody has heard of AI and millions around the world already use, or are exposed, to it - from ChatGPT writing our emails, to helping in medical diagnosis.At its base, AI uses algorithms – sets of mathematically rigorous instructions - that tell a computer how to perform a variety of advanced functions or transform facts into useful information. The Large Language Models (LLMs), that drive today’s increasingly powerful AI, are special kinds of algorithms that learn from massive, mostly centralized datasets.Yet, centralizing these huge datasets generates issues around security, privacy and the ownership of data – indeed the phrase ‘data is the new oil’ signifies that it has become a crucial resource, driving innovation and growth in today’s digital economy.To counter these concerns, an approach called federated learning is now revolutionizing AI. Contrary to training AI models on huge, centralized datasets, federated learning allows these models to learn across a network of decentralized devices (or servers), keeping the raw data at its source.Untrusting Data “Today’s AI trained with federated learning gathers data from all over the world – the internet, other large databases, hospitals, smart devices and so on. These systems are very effective but at the same time there’s a paradox. What makes them so effective also makes them very vulnerable to learning from ‘bad’ data,” explains Professor Rachid Guerraoui, Head of the Distributed Computing Laboratory (DCL) in the School of Computer and Communication Sciences.Data can be bad for many reasons. Perhaps a lack of attention or human error means it is incorrectly entered into a database, maybe there are mistakes in the data to begin with, perhaps sensors or other instruments are broken or malfunctioning, incorrect or dangerous data may be recorded maliciously, etc. Sometimes, the data is good but the machine hosting it is hacked or bogus. In any case, if this data is used to train AI, it makes the syste...