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But worrying about data is probably just a distraction, because, ultimately, “pet” is a relative term—there are more fish in our home aquariums than there are pet dogs, and any category that lumps the two together feels inadequate.

Rabbits, as this passer-by is implying, are widely consumed in other countries. Western Europeans love rabbit sausage, slow-cooked rabbit stews, and braised bunny dishes, while the Chinese—who account for 30% of global rabbit consumption—consider rabbit’s head a delicacy. + Rabbit was even a staple of the American diet at one time. It helped sustain the European transplants who migrated west across the frontier, and during World War II, eating rabbit was promoted as an act of patriotism akin to growing a victory garden. But as small farms gave way to large-scale operations, rabbit meat’s popularity melted away and other meats took over.

Herzog started thinking about this 20 years ago, when he was sitting in a hotel bar having a beer with the psychologist and animal rights activist, Ken Shapiro. Herzog knew Shapiro was a vegan; Shapiro knew Herzog ate meat. Both men had read all of the same psychology and animal-rights literature, and both spent a lot of time working through the same philosophical questions. But somehow, they came to different conclusions about how to live their lives. + “Hal, I don’t get it: why aren’t you like us?” Shapiro suddenly asked. Herzog didn’t have an answer. He still doesn’t. + “I’ve been struggling with this for a long time,” Herzog says. “I can handle moral ambiguity. I can deal with it. So I don’t have that need for moral consistency that animal activists do.” He laughs a little. “And I know that their logic is better than mine, so I don’t even try arguing with them. They win in these arguments.” +

Is big data really all it’s cracked up to be? There is no doubt that big data is a valuable tool that has already had a critical impact in certain areas. For instance, almost every successful artificial intelligence computer program in the last 20 years, from Google’s search engine to the I.B.M. “Jeopardy!” champion Watson, has involved the substantial crunching of large bodies of data. But precisely because of its newfound popularity and growing use, we need to be levelheaded about what big data can — and can’t — do.The first thing to note is that although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, it never tells us which correlations are meaningful.

Like a number of up-and-coming researchers in his generation, Nosek was troubled by mounting evidence that science itself—through its systems of publication, funding, and advancement—had become biased toward generating a certain kind of finding: novel, attention grabbing, but ultimately unreliable. The incentives to produce positive results were so great, Nosek and others worried, that some scientists were simply locking their inconvenient data away. Related Stories Nick Stockton Science Gets Better at Being Wrong Katie M. Palmer Science Has Its Problems, But the Web Could Be the Fix Katie M. Palmer Psychology Is in Crisis Over Whether It’s in Crisis The problem even had a name: the file drawer effect.

Remember that study showing vaccines were linked to autism? The time scientists claimed to have cloned human embryonic stem cells? Or that simple, easy way that was supposed to revolutionize the creation of such stem cells?Those were all frauds published in the world’s top scientific journals — The Lancet, Science and Nature. The vaccine scare has been associated with a surge in cases of measles, some of them deadly.Every day, on average, a scientific paper is retracted because of misconduct. Two percent of scientists admit to tinkering with their data in some kind of improper way. That number might appear small, but remember: Researchers publish some 2 million articles a year, often with taxpayer funding. In each of the last few years, the Office of Research Integrity, part of the United States Department of Health and Human Services, has sanctioned a dozen or so scientists for misconduct ranging from plagiarism to fabrication of results.

As far as I could tell, there were no protesters in the vicinity of Lincoln Center on Nov. 15 before a Saturday matinee, the final performance of “The Death of Klinghoffer” at the Metropolitan Opera. This was a big change from the opening night of the Met’s season in September and the premiere of the “Klinghoffer” production last month, when hundreds of angry demonstrators gathered to denounce this opera by the composer John Adams and the librettist Alice Goodman as an anti-Semitic work that dared to humanize terrorists. Of course, Saturday is the Jewish Sabbath. But the only sign I saw being held outside the Met at the sold-out matinee said, “I need a ticket!” in big red letters.This was also to have been the day of a live HD simulcast of “Klinghoffer.” But Peter Gelb, the Met’s general manager, canceled the broadcast, bowing to pressure from the Anti-Defamation League, whose leaders were concerned about the work’s gaining international exposure at a time of a rise in anti-Semitic actions.

The researchers culled tens of thousands of photos from an online-dating site, then used an off-the-shelf computer model to extract users’ facial characteristics—both transient ones, like eye makeup and hair color, and more fixed ones, like jaw shape. Then they fed the data into their own model, which classified users by their apparent sexuality. When shown two photos, one of a gay man and one of a straight man, Kosinski and Wang’s model could distinguish between them eighty-one per cent of the time; for women, its accuracy dropped slightly, to seventy-one per cent.

The invention of religion is a big bang in human history. Gods and spirits helped explain the unexplainable, and religious belief gave meaning and purpose to people struggling to survive. But what if everything we thought we knew about religion was wrong? What if belief in the supernatural is window dressing on what really matters—elaborate rituals that foster group cohesion, creating personal bonds that people are willing to die for. Anthropologist Harvey Whitehouse thinks too much talk about religion is based on loose conjecture and simplistic explanations. Whitehouse directs the Institute of Cognitive and Evolutionary Anthropology at Oxford University. For years he’s been collaborating with scholars around the world to build a massive body of data that grounds the study of religion in science. Whitehouse draws on an array of disciplines—archeology, ethnography, history, evolutionary psychology, cognitive science—to construct a profile of religious practices.

I suppose people do try to fill in the gaps in their knowledge by invoking supernatural explanations. But many other situations prompt supernatural explanations. Perhaps the most common one is thinking there’s a ritual that can help us when we’re doing something with a high risk of failure. Lots of people go to football matches wearing their lucky pants or lucky shirt. And you get players doing all sorts of rituals when there’s a high-risk situation like taking a penalty kick.

We tend to take a few bits and pieces of the most familiar religions and see them as emblematic of what’s ancient and pan-human. But those things that are ancient and pan-human are actually ubiquitous and not really part of world religions. Again, it really depends on what we mean by “religion.” I think the best way to answer that question is to try and figure out which cognitive capacities came first.

in the very worst manifestation it was actually kind of a weaponized mathematical algorithm. I was working in online advertising. Most of the people working online advertising represented it as a way of giving people opportunities. That’s true for most technologists, most educated people, most white people. On the other side of the spectrum you have poor people, who are being preyed upon, by the same kinds algorithms.

people need to stop trusting mathematics and they need to stop trusting black box algorithms. They need to start thinking to themselves. You know: Who owns this algorithm? What is their goal and is it aligned with mine? If they’re trying to profit off of me, probably the answer is no.

I think most philosophers of science would say that all scientific generalizations are probabilistic rather than logically certain, more so for the social sciences because the systems you are studying are more complex than, say, molecules, and because there are fewer opportunities to intervene experimentally and to control every variable. But the exis­tence of the social sciences, including psychology, to the extent that they have discovered anything, shows that, despite the uncontrollability of human behavior, you can make some progress: you can do your best to control the nuisance variables that are not literally in your control; you can have analogues in a laboratory that simulate what you’re interested in and impose an experimental manipulation. You can be clever about squeezing the last drop of causal information out of a correlational data set, and you can use converging evi­dence, the qualitative narratives of traditional history in combination with quantitative data sets and regression analyses that try to find patterns in them. But I also go to traditional historical narratives, partly as a sanity check. If you’re just manipulating numbers, you never know whether you’ve wan­dered into some preposterous conclusion by taking numbers too seriously that couldn’t possibly reflect reality. Also, it’s the narrative history that provides hypotheses that can then be tested. Very often a historian comes up with some plausible causal story, and that gives the social scientists something to do in squeezing a story out of the numbers.

How big do the numbers have to be for insensitivity to begin? Not very, it turns out.

Consider the recent death of the Syrian child Aylan Kurdi when his family braved the choppy seas off the coast of Turkey. The image of Aylan lying face down on the beach captivated the world’s attention and even, in short order, resulted in refugee policy changes in countries as far away as the United States. But 14 Syrian children drowned in the Aegean Sea the next day. Did you notice? Did you care?

Much of the moral case for bodycams, that they reduce police violence, rests on a single experiment: the 2012 Rialto study

People projected thoughts into Harambe’s mind. “Our tendency is to see our actions through human lenses,” a neuroscientist named Kurt Gray told the network as the frenzy peaked. “We can’t imagine what it’s like to actually be a gorilla. We can only imagine what it’s like to be us being a gorilla.” This simple fact is responsible for centuries of ethical dispute. One Harambe activist might believe that killing a gorilla as a safeguard against losing human life is unjust due to our cognitive similarity: the way gorillas think is a lot like the way we think, so they merit a similar moral standing. Another might believe that gorillas get their standing from a cognitive dissimilarity: because of our advanced powers of reason, we are called to rise above the cat-eat-mouse game, to be special protectors of animals, from chickens to chimpanzees. (Both views also support untroubled omnivorism: we kill animals because we are but animals, or because our exceptionalism means that human interests win.) These beliefs, obviously opposed, mark our uncertainty about whether we’re rightful peers or masters among other entities with brains.

Thanks to advances in technology, we may soon revisit a question raised four centuries ago: Are there things we should try not to know? That’s because the collection of data is increasing, in both scale and type, at a phenomenal rate.

“Big Brother couldn’t have imagined we’d tell him where we were, who we talk to, how we feel – and we’d pay to do it,” said Vivek Wadhwa, a tech entrepreneur and social critic. “We need an amendment in the Constitution that says you own your data.”

A former journalist, equipped with an algorithm and the largest collection of murder records in the country, finds patterns in crime.

Is there a connection between language and how we think and behave? In particular, Chen wanted to know: does our language affect our economic decisions? Chen designed a study — which he describes in detail in this blog post — to look at how language might affect individual’s ability to save for the future. According to his results, it does — big time.

Using vast inventories of data and meticulous analysis, Chen found that huge economic differences accompany this linguistic discrepancy. Futureless language speakers are 30 percent more likely to report having saved in any given year than futured language speakers.

But that’s only the beginning. There’s a wide field of research on the link between language and both psychology and behavior. Here, a few fascinating examples:

There is indeed ample data that institutions and individuals can wreak havoc when they deviate from certain principles of logic and objectivity. There is also a good deal of dispiriting evidence about the rationality of voters that spurs questions about the effectiveness—and even the legitimacy—of democratic government. Yet much of the work of cognitive scientists—even fascinating Nobel Prize-winning research by Daniel Kahneman—leaves me edified but not alarmed. The human capacity for reason may be fragile and partial but it is not belied by studies in which large percentages of subjects answer a few tricky questions incorrectly.

My hypothesis is that while we love reading about humanity’s tendency toward the irrational, we take offense when light is thrown on our own individual incompetencies. 

“Non-overlapping magisteria” has a nice ring to it. The problem is that there are many religious claims that not only “overlap” with empirical data but are incompatible with it. As a scientist who also spends a fair amount of time in the public arena, if I am asked if our understanding of the Big Bang conflicts with the idea of a six-thousand-year-old universe, I face a choice: I can betray my scientific values, or encourage that person to doubt his or her own beliefs. More often than you might think, teaching science is inseparable from teaching doubt.

In Israel, for instance, we have one of the largest laboratories for A.I. surveillances in the world — it’s called the Occupied Territories. In fact, one of the reasons Israel is such a leader in A.I. surveillance is because of the Israeli-Palestinian conflict.Explain this a bit further.Part of why the occupation is so successful is because of A.I. surveillance technology and big data algorithms.

As machine learning becomes more powerful, the field’s researchers increasingly find themselves unable to account for what their algorithms know — or how they know it.