How To Train Your AI Dragon (Safely, Legally And Without Bias)
Untrained dragons can cause a lot of damage. Likewise, as AI systems spread further and have more influence over our lives, it’s getting far more important to make sure they’re properly trained. Bias can creep into the reasoning of AI very easily, either via datasets that are not diverse enough or through irrelevant data attached to viable data points, leading to flawed results and in some cases prejudiced or dangerous conclusions.
Despite regulations like GDPR to protect the privacy of our data, personal consumer data is increasingly being used by companies to improve services or to gain customer insight. Ironically, these regulations also make it more difficult for companies to gather enough data to train an AI system or to prove how their AI reaches its decisions (an impossible task for many deep learning systems).
Therefore, as AI develops and its abilities grow, collecting useful data without breaching data regulations will be crucial to ensure that AI can make the right decisions, and that personal and sensitive data isn’t used in the wrong context.
With so much data flowing through cyberspace, companies areemploying more and more granular metrics to measure our behavior and improve their service. However, theGeneral Data Protection Regulation (GDPR) allows companies to only collect a person’s personal information with their explicit consent, or “if it is necessary for the purposes of legitimate interests pursued by the company,” says Sebastian Weyer, CEO of data anonymization companyStatice. Because article 6 of the GDPR (which outlines the requirements for compliant data processing) leaves the phrase ‘legitimate interests’ open for interpretation this means that companies “are safest when they obtain direct consent from data subjects,” according to Weyer.
However, due to anair of mistrust around companies’ usage of our data, Weyer makes the point that the majority of customers “will not consent to the use of their data for product tests and innovation,” which limits the amount of useful data available to train AI and improve products. This “lack of education around the importance of data in building personalized products and services” can stifle AI innovation, Weyer argues, and fears around data breaches and commercial misuse could in fact restrict the ability of AI to tackle pressing societal issues. Companies building AI products and services also need to be transparent about their use of data for automation, which isnot always as easy as it sounds. Machine Learning systems use data in incredibly complex ways, and the most advanced algorithms often “sacrifice interpretability as a price for performance” says Weyer. Article 15 of the GDPR requires, however, that companies must be able to explain the basic functionalities of their algorithms to a data subject, to give an indication of how their data was used.
Removing all identifying information from a dataset, known as data anonymization, is therefore incredibly important when collecting data as it allows usable information to be gleaned from a dataset without compromising data privacy regulations. Statice, for example, creates a synthetic dataset that follows the same structural and statistical properties of the original but without any identifying information attached. Proper data anonymization is not only a GDPR requirement when collecting data but it also helps to accurately train an AI system. “If the data is not correctly anonymized before being used to build machine learning models, the learned patterns could involve sensitive information,” says Weyer. This is because algorithms work by recognizing patterns in data—if there is extraneous data in the dataset, such as a person’s age, race, or address, then patterns could be drawn between those factors and not the relevant data.
