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The fields of AI bias and AI fairness are still very young. And just like most young technical fields, they\\u2019re dominated by theoretical discussions: researchers argue over what words like \\u201cprivacy\\u201d and \\u201cfairness\\u201d mean, but don\\u2019t do much in the way of applying these definitions to real-world problems.

Slowly but surely, this is all changing though, and government oversight has had a big role to play in that process. Laws like GDPR\\u200a\\u2014\\u200apassed by the European Union in 2016 \\u2014are starting to impose concrete requirements on companies that want to use consumer data, or build AI systems with it. There are pros and cons to legislating machine learning, but one thing\\u2019s for sure: there\\u2019s no looking back. At this point, it\\u2019s clear that  government-endorsed definitions of \\u201cbias\\u201d and \\u201cfairness\\u201d in AI systems are going to be applied to companies (and therefore to consumers), whether they\\u2019re well-developed and thoughtful or not.

Keeping up with the philosophy of AI is a full-time job for most, but actually applying that philosophy to real-world corporate data is its own additional challenge. My guest for this episode of the podcast is doing just that: Nicolai Baldin is a former Cambridge machine learning researcher, and now the  founder and CEO of Synthesized, a startup that specializes in helping companies apply privacy, AI fairness and bias best practices to their data. Nicolai is one of relatively few people working on concrete problems in these areas, and has a unique perspective on the space as a result.