Artificial Intelligence

The range and diversity of AI applications means that the problems and risks are manifold. These include:

• Re-identification and de-anonymisation: AI applications can be used to identify and thereby track individuals across different devices, in their homes, at work, and in public spaces. For example, while personal data is routinely (pseudo-)anonymised within datasets, AI can be employed to de-anonymise this data. Facial recognition is another means by which individuals can be tracked and identified, which has the potential to transform expectations of anonymity in public space.
• Discrimination, unfairness, inaccuracies, bias: AI-driven identification, profiling, and automated decision-making may also lead to unfair, discriminatory, or biased outcomes. People can be misclassified, misidentified, or judged negatively, and such errors or biases may disproportionately affect certain groups of people.
• Opacity and secrecy of profiling: Some applications of AI can be opaque to individuals, regulators, or even the designers of the system themselves, making it difficult to challenge or interrogate outcomes. While there are technical solutions to improving the interpretability and/or the ability to audit of some systems for different stakeholders, a key challenge remains where this is not possible, and the outcome has significant impacts on people’s lives.
• Data exploitation: People are often unable to fully understand what kinds and how much data their devices, networks, and platforms generate, process, or share. As we bring smart and connected devices into our homes, workplaces, public spaces, and even bodies, the need to educate the public about such data exploitation becomes increasingly pressing. In this landscape, uses of AI for purposes like profiling, or to track and identify people across devices and even in public spaces, amplify this asymmetry.

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