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The broadcast has become an origin story of fake news and technological anxiety in the United States, and its tentacled aliens watch when we talk of fake news today. Then, as now, the worry over whether the news can be believed was a proxy for something else entirely – fear of the new technologies that brought it. Scholars have convincingly questioned the scale of the 1938 panic. Everybody loves a good story – especially the newspapers threatened by radio news, the social scientists seeking a claim to relevance, and Welles, great ham that he was. Firsthand accounts attest that some listeners did panic, but many more did not. Why, then, did millions more find the panic so easy to believe these past 80 years?

In that decade, radio became more trusted than newspapers. The reasons had to do partially with the unique characteristics of the medium – its intimacy and ability to put you on the spot to hear as an event unfolded without a reporting gap in which craven newspapermen could insert their own slant. It also had to do with the trueness of the sounds that radio reproduced.

In November, three days after the War of the Worlds broadcast, Dorothy Thompson, a syndicated columnist and radio reporter, published an oft-cited piece in response, entitled ‘Mr Welles and Mass Delusion’, in which she argued that the broadcast suggested American susceptibility to foreign propaganda:All unwittingly Mr Orson Welles and the Mercury Theatre of the Air have made one of the most fascinating and important demonstrations of all time. They have proved that a few effective voices, accompanied by sound effects, can so convince masses of people of a totally unreasonable, completely fantastic proposition as to create nationwide panic … If people can be frightened out of their wits by mythical men from Mars, they can be frightened into fanaticism by the fears of Reds, or convinced that America is in the hands of 60 families, or aroused to revenge against any minority, or terrorised into subservience to leadership because of any imaginable menace.

Nearly every moment of our lives, we’re producing data about ourselves that companies profit from. Our smartwatches know when we wake up, Alexa listens to our private conversations, our phones track where we go, Google knows what we email and search, Facebook knows what we share with friends, and our loyalty cards remember what we buy. We share all this data about ourselves because we like the services these companies provide, and business leaders tell us we must to make it possible for those services to be cheap or free.

We should not only expect that these companies better protect our data – we should also ensure that everyone creating it shares in the economic value it generates. One person’s data is worth little, but the collection of lots of people’s data is what fuels the insights that companies use to make more money or networks, like Facebook, that marketers are so attracted to. Data isn’t the “new oil”, as some have claimed: it isn’t a non-renewable natural resource that comes from a piece of earth that a lucky property owner controls. We have all pitched in to create a new commonwealth of information about ourselves that is bigger than any single participant, and we should all benefit from it.

The value of our data has a lot in common with the value of our labor: a single individual worker, outside of the rarest professions, can be replaced by another with similar skills. But when workers organize to withhold their labor, they have much more power to ensure employers more fairly value it. Just as one worker is an island but organized workers are a force to be reckoned with, the users of digital platforms should organize not only for better protection of our data, but for a new contract that ensures everyone shares in the historic profits we make possible.

In the 1990s, the term “modern grocery retail” was essentially a proxy for a small group of multinational grocers including Ahold, Aldi, Auchan, Carrefour, Costco, Lidl, Metro, Tesco, and Walmart. It was widely presumed that these retailers’ entry into any market would lead to the demise of the traditional trade—the family-owned grocery chains, small independent stores, and informal merchants that at the time accounted for the vast majority of grocery sales in emerging markets. The prevailing expectation was that although there would be local differences due to cultural specificities, in every country the retail landscape would eventually consist of a combination of modern formats: full-line supermarkets and hypermarkets, convenience stores, and discounters. These assumptions have been proved wrong. Global grocery giants are struggling to grow profitably in many emerging markets. Traditional trade has proved remarkably resilient. And the market and channel structures taking shape in individual emerging economies are distinct from one another, following no obvious pattern.

or entertaining.

To hear Zuckerberg explain it, the increase in news articles and marketing has created an imbalance that “is crowding out the personal moments that lead us to connect more with each other.” Based on Facebook’s internal research and outside studies, he said that people are generally happier and have a better “well-being” when they use social media to connect “with people we care about.” What “may not be as good,” however, is merely “reading articles or watching videos,” even if they’re informative or entertaining.

Built from scratch by people in poorly governed, disaster-stricken Haiti, the city is emerging as an alternative model of urban existence — and its struggle is holding out lessons for similar future pockets that spring up in the aftermath of disasters.

The UN estimates there are 65 million displaced persons in the world today, more than at any time since World War II. Most live in camps where their lives are tightly restricted by host governments. They are barred from owning land or holding jobs, destined to remain dependent on foreign aid.

Canaan is the opposite. Instead of being micro-managed, it has no formal government at all. The pioneers of Canaan formed hundreds of committees that each work on a particular task or oversee the development of a particular neighborhood. These informal power structures give street names to the dirt alleyways, and set aside space for future hospitals and schools.

Artificial intelligence may have cracked the code on certain tasks that typically require human smarts, but in order to learn, these algorithms need vast quantities of data that humans have produced. They hoover up that information, rummage around in search of commonalities and correlations, and then offer a classification or prediction (whether that lesion is cancerous, whether you’ll default on your loan) based on the patterns they detect. Yet they’re only as clever as the data they’re trained on, which means that our limitations—our biases, our blind spots, our inattention—become theirs as well.

The majority of AI systems used in commercial applications—the ones that mediate our access to services like jobs, credit, and loans— are proprietary, their algorithms and training data kept hidden from public view. That makes it exceptionally difficult for an individual to interrogate the decisions of a machine or to know when an algorithm, trained on historical examples checkered by human bias, is stacked against them. And forget about trying to prove that AI systems may be violating human rights legislation.

Data is essential to the operation of an AI system. And the more complicated the system—the more layers in the neural nets, to translate speech or identify faces or calculate the likelihood someone defaults on a loan—the more data must be collected.

It is possible that US technology adoption is somehow different to that in other developed countries and that this is contributing to jobless recoveries. For example, Bloom et al. (2012) show that US multinationals achieved more productivity gains from using information technology than their European counterparts, so it is possible that job creation patterns in the US also differed. At the same time, the extension of US unemployment benefits in recent years may have played a role in explaining slow employment recoveries. Meanwhile, European labour markets have become more flexible, which may have contributed to their robust employment growth during recent recoveries. Exploring these possibilities is an important task for future research.

Human minds simply cannot reconstruct highly complex natural phenomena efficiently enough in the age of big data. A modern Baconian method that incorporates reductionist ideas through data-mining, but then analyses this information through inductive computational models, could transform our understanding of the natural world. Such an approach would enable us to generate novel hypotheses that have higher chances of turning out to be true, to test those hypotheses, and to fill gaps in our knowledge. It would also provide a much-needed reminder of what science is supposed to be: truth-seeking, anti-authoritarian, and limitlessly free.