Companies are increasingly deploying artificial intelligence (AI) as they race to extract value from the personal data they hold. As with any project using personal data, AI is subject to data protection requirements arising from the GDPR in Europe and similar laws elsewhere. However, some types of AI, particularly systems based on machine learning, pose specific data protection risks and challenges. The Information Commissioner’s Office (ICO) recently issued new guidance on AI and data protection, which aims to support companies as they embark on AI projects. It helps companies to identify and assess risks, and to design and implement measures to mitigate those risks. This article offers practical suggestions for companies as they implement the new ICO guidance and build trusted, compliant AI.
More AI, more data and more AI regulation
More companies are using AI to process personal data, in some cases without a comprehensive risk assessment. AI often uses large volumes of personal data. Large scale, complex systems carry specific risks. This has prompted regulators to issue specific guidance on AI.
McKinsey’s 2019 survey of over 2,300 companies showed that 80% had trialled or adopted AI. Of those, 63% reported revenue increases and 44% realised cost savings, but 41% didn’t comprehensively identify and prioritise AI risks. Worryingly, only 30% were working to mitigate privacy risk. McKinsey defined AI in terms of machine learning, excluding rules-based systems or those based on decision trees. Machine learning often grabs the headlines, but other data-driven decision making can have significant impacts on individuals. For example, Ofqual’s system for assigning grades to students whose exams were cancelled because of COVID-19 was a statistical model based on attainment data, not a machine learning system.
Broadly speaking, machine learning systems work by identifying patterns in ‘training’ data, then applying those patterns to make inferences about individuals. For instance, a

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