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It is vital that public policy be driven by rigorous research. In the last decade key policy changes have had profound impacts on nightlife in Sydney's inner city and suburbs. The most significant and controversial of these has been the 2014 "lockout laws".

Analysis of the day's news in an engaging & entertaining way. Host John Barron is joined by a panel of journalists, political & social commentators for a lively, thought-provoking discussion. #TheDrum This week includes discussion of vaccinations and the lock out laws - a great episode for aei

The more serious problem with California's law is one of effect size. Health, and cancer, aren't binary. Consumers can't just be concerned with whether a danger exists; they also need to be concerned about the magnitude of that risk. Even if there's a statistically significant risk between huge quantities of coffee and some cancer (and that's not proven), it's very, very small. Cigarettes have a clear and easily measured negative impact on people's health. Acrylamide, especially the acrylamide in coffee, isn't even close. Warning labels should be applied when a danger is clear, a danger is large and a danger is avoidable. It's not clear that, with respect to acrylamide, any of these criteria are met. It's certainly not the case regarding coffee. Whatever the intentions of Proposition 65, this latest development could do more harm than good.

One way of working out if the data you're gathering is particularly sensitive is to do a thought experiment: what would happen if this data got into the hands of a malicious actor? Who would be keen to get their hands on it? What are the worst things that they could do with this data? Sometimes, though, it can be hard to put yourself in the shoes of your enemies, or to envision potential future actions. As a result, practising data minimisation is a keystone of a rights-based, responsible data approach. And sadly, it's the opposite of the approach we're seeing governments around the world take.

An introduction to a set of deep dive articles an important issue....When we tell people that we work at the intersection of statistics and human rights, the reaction is often surprise. Everyone knows that lawyers and journalists think about human rights problems … but statisticians? Yet, documenting and proving human rights abuses frequently involves the need for quantification. In the case of war crimes and genocide, guilt or innocence can hinge on questions of whether violence was systematic and widespread or one group was targeted at a differential rate compared to others. Similar issues can arise in assessing violations of civil, social, and economic rights. Sometimes the questions can be answered through simple tabulations, but often, more-complex methods of data collection and analysis are required.

The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance - even an allergy - to taking math and statistics seriously. For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court's desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices - and thus the court - are at risk of making decisions without fully grappling with the evidence. This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering - and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map's partisan bias and to create a standard for when a gerrymandered map infringes on voters' rights?