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Data aren't dead, contrary to what some pundits stated post-election [2], rather the limitations of data are not always well reported. While pollsters will be reworking their models following the election, what can media journalists do to improve their overall coverage of statistical issues in the future? First, discuss possible statistical biases, such as errors in sampling and polling, and what impact these might have on the results. Second, always provide measures of uncertainty, and root these uncertainties in real-world examples.

some examples of how the 3 types of machine learning - supervised, unsupervised, and reinforcement - have already been used for journalistic purposes, and using those to explain what those are along the way. Examples include: supervised learning to investigate doctors and sex abuse; unsurprivsed learning to identify motifs in Wes Anderson films; reinforcement learning to create a rock-paper-scissors that can beat you...

Adrienne is a Marvel movie fanatic: Her favorite films all involve the Hulk, Thor or Black Panther. Brandon prefers animated features like Inside Out, The Incredibles and anything with Buzz Lightyear. I like both kinds, although I'm probably closer to Adrienne than Brandon. And I might skew a little toward Cora, who loves thrillers like Get Out and The Shining. Whose movie preferences are closest to yours: Adrienne's, Brandon's or Cora's? And how far are your cinematic tastes from those of the other two? It might seem strange to ask "how far" here. That's a question about distance, after all. What does distance mean when it comes to which movies you like? How would we measure it?

Mashable's series Algorithms explores the mysterious lines of code that increasingly control our lives - and our futures. The world in 2020 has been given plenty of reasons to be wary of algorithms. Depending on the result of the U.S. presidential election, it may give us one more. Either way, it's high time we questioned the impact of these high-tech data-driven calculations, which increasingly determine who or what we see (and what we don't) online. The impact of algorithms is starting to scale up to a dizzying degree, and literally billions of people are feeling the ripple effects. This is the year the Social Credit System, an ominous Black Mirror-like "behavior score" run by the Chinese government, is set to officially launch. It may not be quite as bad as you've heard, but it will boost or tighten financial credit and other incentives for the entire population. There's another billion unexamined, unimpeachable algorithms hanging over a billion human lives.

Blame the algorithm. That's become the go-to refrain for why your Instagram feed keeps surfacing the same five people or why YouTube is feeding you questionable "up next" video recommendations. But you should blame the algorithm - those ubiquitous instructions that tell computer programs what to do - for more than messing with your social media feed. Algorithms are behind many mundane, but still consequential, decisions in your life. The code often replaces humans, but that doesn't mean the results are foolproof. An algorithm can be just as flawed as their human creators. These are just some of the ways hidden calculations determine what you do and experience.

We learned this week that the Trump campaign may have tried to dissuade millions of Black voters from voting in 2016 through highly targeted online ads. The investigation, by Channel 4, highlighted a still little-understood online advertising technique, microtargeting. This targets ads at people based on the huge amount of data available about them online. Experts say Big Tech needs to be much more transparent about how microtargeting works, to avoid overblown claims but also counter a potential threat to democracy.