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THANKS to Mark, the reader who spotted the typo in the second link. Now fixed.

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‘Middlemarch’ then and now

Today is the 200th anniversary of the birth of Mary Ann Evans, a woman whom we all know better as George Eliot. The New Yorker has a lovely essay by Rebecca Mead about Eliot and in particular about her great novel Middlemarch. Mead has already written a book about her own encounters with that novel — how she saw it differently each time she returned to it at various times in her own life. Middlemarch, she says “is a book that grows with the reader as the reader grows, which is why, two hundred years after Eliot’s birth, a reader can find it always has something to say to her or to him.”

But now she sees it in another, contemporary, light:

Lately, though, I have found myself thinking less about Eliot’s depiction of individual characters and more about the novel’s subtitle, “A Study of Provincial Life.” When Eliot set out to write “Middlemarch,” what she seemed to have in mind was a panoramic examination of a small town and its inhabitants that would capture not just the stories of individuals but would also say something about the way a community works, and about the state of the nation. “I am delighted to hear of a Novel of English Life having taken such warm possession of you,” her publisher, John Blackwood, remarked, when Eliot conveyed her intentions to him. Revisiting “Middlemarch” in the England of 2019—a year in which Britain was due to leave the European Union but instead has been mired in parliamentary paralysis, which the forthcoming election may or may not resolve—Eliot’s ironic observations about the electoral system have a new piquancy, and her representation of the innate conservatism of English provincial life has a topical relevance.

The parallel Mead sees is between the current UK government’s attempts to leave the European Union and the first Reform Bill of 1832. She focuses on one of the lesser characters in Middlemarch, Mr. Brooke, Dorothea Brooke’s uncle and guardian, who is a comfortable member of the landed gentry, and decides to run for office under the banner of Reform.

“There is no part of the country where opinion is narrower than it is here,” Mr. Brooke tells a reproving neighbor, Mrs. Cadwallader, the rector’s wife. Eliot shows, however, that Mr. Brooke’s commitment to reform is, at best, insubstantial. Having read theorists whose ideas underlie the movement, Mr. Brooke is inclined to ideas of liberalism, but, being a comfortable member of the landed gentry, his instincts are less than disruptive. (“Let Brooke reform his rent roll. He’s a cursed old screw, and the buildings all over his estate are going to rack,” one of the burghers of Middlemarch scathingly observes, when Brooke announces his forthcoming platform.) “This Reform will touch everybody by-and-by—a thoroughly popular measure—a sort of A, B, C, you know, that must come first before the rest can follow,” Mr. Brooke argues, to a voter, with “a sense of being a little out at sea, though finding it still enjoyable.” The hallmarks of Mr. Brooke’s character, and of his political campaign, are an inconsistency of mind and an absence of intellectual rigor.

Well, well. Which contemporary political figure does that bring to mind?

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‘Don’t be Evil’ changes to ‘Don’t ask me anything’

From Steven Levy, who knows as much about Google as any outsider:

Last week, Google CEO Sundar Pichai sent an email blast to his 100,000 or so employees, cutting back the company’s defining all-hands meeting known as TGIF. The famous free-for-alls had epitomized the company’s egalitarian ethos, a place where employees and leaders could talk freely about nearly anything. More recently, however, the biweekly meeting had become fraught as it increasingly reflected Google’s tensions as opposed to its aspirations. “It’s not working in its current form,” Pichai said of what was once the hallmark of Google culture. In 2020, he declared, the meetings would be limited to once a month, and they would be more constrained affairs, sticking to “product and business strategy.” Don’t Be Evil has changed to Don’t Ask Me Anything.

It was inevitable, really. You can’t run a giant company as if it were a small startup.

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Bias in machine learning

Nice example from Daphne Keller of Google:

Another notion of bias, one that is highly relevant to my work, are cases in which an algorithm is latching onto something that is meaningless and could potentially give you very poor results. For example, imagine that you’re trying to predict fractures from X-ray images in data from multiple hospitals. If you’re not careful, the algorithm will learn to recognize which hospital generated the image. Some X-ray machines have different characteristics in the image they produce than other machines, and some hospitals have a much larger percentage of fractures than others. And so, you could actually learn to predict fractures pretty well on the data set that you were given simply by recognizing which hospital did the scan, without actually ever looking at the bone. The algorithm is doing something that appears to be good but is actually doing it for the wrong reasons. The causes are the same in the sense that these are all about how the algorithm latches onto things that it shouldn’t latch onto in making its prediction.

Addressing bias in algorithms is crucial, especially in domains like healthcare where accurate predictions are vital. One effective approach to recognizing and mitigating biases is to rigorously test the algorithm in scenarios similar to its real-world applications. Suppose a machine-learning algorithm is trained on data from specific hospitals to predict fractures from X-ray images. In this case, it may appropriately incorporate prior knowledge about patient populations in those hospitals, resulting in reliable predictions within that context. However, the challenge arises when the algorithm is intended to be used in different hospitals not present in the initial training data set. To avoid unintended biases, a robust evaluation process is essential, and the use of a mobile learning management system can prove beneficial. Such a system enables continuous monitoring and assessment of the algorithm’s performance across various hospital settings, ensuring it doesn’t latch onto irrelevant factors and provides accurate predictions based on genuine medical insights.

To recognize and address these situations, you have to make sure that you test the algorithm in a regime that is similar to how it will be used in the real world. So, if your machine-learning algorithm is one that is trained on the data from a given set of hospitals, and you will only use it in those same set of hospitals, then latching onto which hospital did the scan could well be a reasonable approach. It’s effectively letting the algorithm incorporate prior knowledge about the patient population in different hospitals. The problem really arises if you’re going to use that algorithm in the context of another hospital that wasn’t in your data set to begin with. Then, you’re asking the algorithm to use these biases that it learned on the hospitals that it trained on, on a hospital where the biases might be completely wrong.

Facebook’s strategic obfuscation

Facebook’s Carolyn Everson, vice president of global marketing solutions, was interviewed by Peter Kafka at the 2019 Code Media conference in Los Angeles yesterday. Vox had a nice report of the interview. This section is particularly interesting:

When pressed on Facebook’s refusal to fact-check political ads, Everson tried to defend the company’s stance by referencing the rules that govern how broadcasters must handle political advertisements. In the US, the Federal Communications Commission has extensive guidelines for television and radio broadcasters around political advertising that bar broadcasters from censoring ads or from taking down ones that make false claims. Those guidelines don’t apply to online platforms, including Facebook, but the company has consistently tried to hide behind them.

“We have no ability, legally, to tell a political candidate that they are not allowed to run their ad,” Everson said.

That’s complete baloney. Facebook is not bound by any regulations governing TV ads. It can shut down anyone or anything it likes or dislikes.

After the interview, a Facebook spokeswoman walked back the comments and said that Everson misspoke when she said Facebook was legally barred from refusing to run political ads.

An audience member also asked Everson why Facebook has decided to allow right-wing website Breitbart to be listed in its new News tab, which is ostensibly an indication that Breitbart offers trusted news, despite being a known source of propaganda. “We’re treating them as a news source; I wouldn’t use the term ‘trusted news,’” Everson said, pointing out that Facebook will also include “far-left” publications.

Which of course raises interesting questions about Facebook’s standards for determining the “integrity” of the news sources it includes in its tab, which the company extolled when it launched the feature in October.

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