Last month, WhatsApp was hit with a EUR225 million ($352.25 million) fine for violating Europe’s privacy rules. It is the largest fine ever from the Irish Data Protection Commission and the second-highest under EU GDPR rules. Meanwhile, China just passed its first data protection law — the Personal Information Protection Law (PIPL) — a game-changer for companies with data or businesses in China. While privacy and data related laws already impact companies in material ways, they are probably only the tip of the iceberg of technology regulations yet to come.

Attention is now shifting to how data is used by the software — particularly by Artificial Intelligence (AI) algorithms that can diagnose cancer, drive a car, approve a loan or assess examination grades. For example, the EU considers regulation to be essential to the development of AI tools that consumers can trust, and China puts additional legal responsibilities on algorithm owners operating in a number of areas such as web search, e-commerce or short-video sharing. Companies will need to prepare for these upcoming regulations both to avoid regulatory risks but also to maintain their stakeholders’ trust. In our work, we explore three main risks organisations face as they integrate AI into their business.

Unfair outcomes: The risks of using AI
AI systems that produce biased results have been making headlines. Unfair outcomes can crop up in online advertisement algorithms, which target viewers by age, race, religion or gender, but the stakes can be higher. For example, one study published in the Journal of General Internal Medicine found that the software used by prominent hospitals to prioritise recipients of kidney transplants discriminated against Black patients. In most cases, the problem stems from the data (created by people) used to train the AI. If that data is biased, then the AI will acquire and may even amplify the bias.

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