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Steve Bosserman

The Big Day is Here - Dave Pell - Medium - 0 views

medium.com/...e-big-day-is-here-6c5a2cf729b8

holocaust survivors voting politics

shared by Steve Bosserman on 06 Nov 18 - No Cached
  • And we’re voting in an American election at a moment when antisemitism (and racism, fascism, xenophobia, fear-mongering, etc.) is once again on the rise in places as far as Poland, Germany, and Hungary, and as close as Pittsburgh’s Tree of Life synagogue.While she understands that everything is political, my mom is far from being a rabid partisan. The morning after Trump’s election, she said, “He deserves a chance to lead.” Well, he’s been given that chance. And politicians across the country have been given the chance to decide how to respond to his brand of leadership.
Bill Fulkerson

Should I Major in the Humanities? - The Atlantic - 0 views

www.theatlantic.com/...567565

humanities education

shared by Bill Fulkerson on 27 Aug 18 - No Cached
  • Bill Fulkerson on 27 Aug 18
     
    "Right now, the biggest impediment to thinking about the future of the humanities is that, thanks to this entrenched narrative of decline-because we've been crying wolf for so long-we already think we know what's going on. The usual suspects-student debt, postmodern relativism, vanishing jobs-are once again being trotted out. But the data suggest something far more interesting may be at work. The plunge seems not to reflect a sudden decline of interest in the humanities, or any sharp drop in the actual career prospects of humanities majors. Instead, in the wake of the 2008 financial crisis, students seem to have shifted their view of what they should be studying-in a largely misguided effort to enhance their chances on the job market. And something essential is being lost in the process."
Bill Fulkerson

It's not all Pepes and trollfaces - memes can be a force for good - The Verge - 0 views

www.theverge.com/...d-behavioral-science-nazi-pepe

memes

shared by Bill Fulkerson on 30 Aug 18 - No Cached
  • Bill Fulkerson on 30 Aug 18
     
    "How the 'emotional contagion' of memes makes them the internet's moral conscience By Allie Volpe Aug 27, 2018, 11:30am EDT Illustration by Alex Castro & Keegan Larwin SHARE Newly single, Jason Donahoe was perusing Tinder for the first time since it started integrating users' Instagram feeds. Suddenly, he had an idea: follow the Instagram accounts of some of the women he'd been interested in but didn't match with on the dating service. A few days later, he considered taking it a step further and direct messaging one of the women on Instagram. After all, the new interface of the dating app seemed to encourage users to explore other areas of potential matches' online lives, so why not take the initiative to reach out? Before he had a chance, however, he came across the profile of another woman whose Tinder photo spread featured a meme with Parks and Recreation character Jean-Ralphio Saperstein (Ben Schwartz) leaning into the face of Ben Wyatt (Adam Scott) with the caption: hey I saw you on Tinder but we didn't match so I found your Instagram you're so beautiful you don't need to wear all that makeup ahah I bet you get a lot of creepy dm's but I'm not like all those other guys message me back beautiful btw what's your snap "I was like, 'Oh shit, wow,'" Donahoe says. Seeing his potential jerk move laid out so plainly as a neatly generalized joke, he saw it in a new light. "I knew a) to be aware of that, and b) to cut that shit out … It prompted self-reflection on my part." THE MOST SUCCESSFUL MEMES STRIKE A CULTURAL CHORD AND CAN GUIDE AND EVEN INFLUENCE BEHAVIOR Donahoe says memes have resonated with him particularly when they depict a "worse, extreme version" of himself. For Donahoe, the most successful memes are more than just jokes. They "strike a societal, cultural chord" and can be a potent cocktail for self-reflection as tools that can guide and even influence behavior. In the months leading up to the 2016 US
Bill Fulkerson

Neoliberalism drives climate breakdown, not human nature | openDemocracy - 0 views

www.opendemocracy.net/...-is-wrong-about-climate-change

breakdown human climate

shared by Bill Fulkerson on 08 Aug 18 - No Cached
  • Bill Fulkerson on 08 Aug 18
     
    "The idea that all humanity is equally and collectively responsible for climate change - or any other environmental or social problem - is extremely weak. In a basic and easily calculable way, not everyone is responsible for the same quantity of greenhouse gasses. People in the world's poorest countries produce roughly one hundredth of the emissions of the richest people in the richest countries. Through the chance of our births, and the lifestyle we choose we are not all equally responsible for climate change."
Bill Fulkerson

Millennials and Gen Z are spreading coronavirus-but not because of parties and bars - 0 views

www.nationalgeographic.com/...ch-parties-bars-inequality-cvd

millennials COVID-19 pandemic gen-z

shared by Bill Fulkerson on 17 Sep 20 - No Cached
  • Bill Fulkerson on 17 Sep 20
     
    Younger generations are blamed for the pandemic's spread, but also face the brunt of the transmission risk that comes with keeping the economy going. 6 MINUTE READ BY REBECCA RENNER PUBLISHED SEPTEMBER 17, 2020 WHEN PARAMEDICS RUSHED the pregnant Honduran woman into the emergency room, 28-year-old Chuan-Jay Jeffrey Chen stood ready to receive her. It was April, and the pandemic had already taken over his final year as an emergency medicine resident. Of all the coronavirus patients surging into Massachusetts General Hospital in Boston, this 32-year-old patient remains Chen's most memorable. The woman was so short of breath she could barely speak, so Chen would need to intubate her-a tricky procedure that requires precision as well as speed. Every moment without oxygen causes a patient's chances of survival to decline; pregnancy further complicates the scenario by making airways swollen, causing blood pressure to drop more quickly. As Chen set to work and talked her through the steps in Spanish, he also tried to calm his own nerves. "I knew I had very little margin for error," says Chen. The woman's husband had been barred from entering the building because of coronavirus restrictionsgen-z
Bill Fulkerson

The violent blasts that can add to an avalanche's devastation : Research Highlights - 0 views

www.nature.com/...d41586-020-02514-y

avalanche research

shared by Bill Fulkerson on 05 Sep 20 - No Cached
  • Bill Fulkerson on 05 Sep 20
     
    Scientists zero in on the factors that heighten the chance of 'airblasts' after a slope collapses.
Steve Bosserman

Are You Creditworthy? The Algorithm Will Decide. - 0 views

undark.org/...redit-scoring-machine-learning

algorithms discrimination bias credit scoring

shared by Steve Bosserman on 12 May 18 - No Cached
  • The decisions made by algorithmic credit scoring applications are not only said to be more accurate in predicting risk than traditional scoring methods; its champions argue they are also fairer because the algorithm is unswayed by the racial, gender, and socioeconomic biases that have skewed access to credit in the past.
  • Algorithmic credit scores might seem futuristic, but these practices do have roots in credit scoring practices of yore. Early credit agencies, for example, hired human reporters to dig into their customers’ credit histories. The reports were largely compiled from local gossip and colored by the speculations of the predominantly white, male middle class reporters. Remarks about race and class, asides about housekeeping, and speculations about sexual orientation all abounded.
  • By 1935, whole neighborhoods in the U.S. were classified according to their credit characteristics. A map from that year of Greater Atlanta comes color-coded in shades of blue (desirable), yellow (definitely declining) and red (hazardous). The legend recalls a time when an individual’s chances of receiving a mortgage were shaped by their geographic status.
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  • These systems are fast becoming the norm. The Chinese Government is now close to launching its own algorithmic “Social Credit System” for its 1.4 billion citizens, a metric that uses online data to rate trustworthiness. As these systems become pervasive, and scores come to stand for individual worth, determining access to finance, services, and basic freedoms, the stakes of one bad decision are that much higher. This is to say nothing of the legitimacy of using such algorithmic proxies in the first place. While it might seem obvious to call for greater transparency in these systems, with machine learning and massive datasets it’s extremely difficult to locate bias. Even if we could peer inside the black box, we probably wouldn’t find a clause in the code instructing the system to discriminate against the poor, or people of color, or even people who play too many video games. More important than understanding how these scores get calculated is giving users meaningful opportunities to dispute and contest adverse decisions that are made about them by the algorithm.
Steve Bosserman

How We Made AI As Racist and Sexist As Humans - 0 views

thewalrus.ca/as-racist-and-sexist-as-humans

artificial intelligence algorithms sexism data mining racism

shared by Steve Bosserman on 25 May 18 - No Cached
  • 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.
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  • But not everyone will be equally represented in that data.
  • And sometimes, even when ample data exists, those who build the training sets don’t take deliberate measures to ensure its diversity
  • The power of the system is its “ability to recognize that correlations occur between gender and professions,” says Kathryn Hume. “The downside is that there’s no intentionality behind the system—it’s just math picking up on correlations. It doesn’t know this is a sensitive issue.” There’s a tension between the futuristic and the archaic at play in this technology. AI is evolving much more rapidly than the data it has to work with, so it’s destined not just to reflect and replicate biases but also to prolong and reinforce them.
  • Accordingly, groups that have been the target of systemic discrimination by institutions that include police forces and courts don’t fare any better when judgment is handed over to a machine.
  • A growing field of research, in fact, now looks to apply algorithmic solutions to the problems of algorithmic bias.
  • Still, algorithmic interventions only do so much; addressing bias also demands diversity in the programmers who are training machines in the first place.
  • A growing awareness of algorithmic bias isn’t only a chance to intervene in our approaches to building AI systems. It’s an opportunity to interrogate why the data we’ve created looks like this and what prejudices continue to shape a society that allows these patterns in the data to emerge.
  • Of course, there’s another solution, elegant in its simplicity and fundamentally fair: get better data.
Steve Bosserman

Science has outgrown the human mind and its limited capacities | Aeon Ideas - 0 views

aeon.co/...ind-and-its-limited-capacities

models artificial intelligence machine learning data mining

shared by Steve Bosserman on 25 Apr 17 - No Cached
  • 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.
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