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Bill Fulkerson

Why a 400-Year Program of Modernist Thinking is Exploding | naked capitalism - 0 views

www.nakedcapitalism.com/...ernist-thinking-exploding.html

capitalism power society

shared by Bill Fulkerson on 11 Mar 17 - No Cached
  • Bill Fulkerson on 11 Mar 17
     
    " Fearless commentary on finance, economics, politics and power Follow yvessmith on Twitter Feedburner RSS Feed RSS Feed for Comments Subscribe via Email SUBSCRIBE Recent Items Links 3/11/17 - 03/11/2017 - Yves Smith Deutsche Bank Tries to Stay Alive - 03/11/2017 - Yves Smith John Helmer: Australian Government Trips Up Ukrainian Court Claim of MH17 as Terrorism - 03/11/2017 - Yves Smith 2:00PM Water Cooler 3/10/2017 - 03/10/2017 - Lambert Strether Why a 400-Year Program of Modernist Thinking is Exploding - 03/10/2017 - Yves Smith Links 3/10/17 - 03/10/2017 - Yves Smith Why It Will Take a Lot More Than a Smartphone to Get the Sharing Economy Started - 03/10/2017 - Yves Smith CalPERS' General Counsel Railroads Board on Fiduciary Counsel Selection - 03/10/2017 - Yves Smith Another Somalian Famine - 03/10/2017 - Yves Smith Trade now with TradeStation - Highest rated for frequent traders Why a 400-Year Program of Modernist Thinking is Exploding Posted on March 10, 2017 by Yves Smith By Lynn Parramore, Senior Research Analyst at the Institute for New Economic Thinking. Originally published at the Institute for New Economic Thinking website Across the globe, a collective freak-out spanning the whole political system is picking up steam with every new "surprise" election, rush of tormented souls across borders, and tweet from the star of America's great unreality show, Donald Trump. But what exactly is the force that seems to be pushing us towards Armageddon? Is it capitalism gone wild? Globalization? Political corruption? Techno-nightmares? Rajani Kanth, a political economist, social thinker, and poet, goes beyond any of these explanations for the answer. In his view, what's throwing most of us off kilter - whether we think of ourselves as on the left or right, capitalist or socialist -was birthed 400 years ago during the period of the Enlightenment. It's a set of assumptions, a particular way of looking at the world that pushed out previous modes o
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.
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