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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.
Bill Fulkerson

Anatomy of an AI System - 1 views

anatomyof.ai

AI amazon

shared by Bill Fulkerson on 14 Sep 18 - No Cached
  • Bill Fulkerson on 14 Sep 18
     
    "With each interaction, Alexa is training to hear better, to interpret more precisely, to trigger actions that map to the user's commands more accurately, and to build a more complete model of their preferences, habits and desires. What is required to make this possible? Put simply: each small moment of convenience - be it answering a question, turning on a light, or playing a song - requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch. A full accounting for these costs is almost impossible, but it is increasingly important that we grasp the scale and scope if we are to understand and govern the technical infrastructures that thread through our lives. III The Salar, the world's largest flat surface, is located in southwest Bolivia at an altitude of 3,656 meters above sea level. It is a high plateau, covered by a few meters of salt crust which are exceptionally rich in lithium, containing 50% to 70% of the world's lithium reserves. 4 The Salar, alongside the neighboring Atacama regions in Chile and Argentina, are major sites for lithium extraction. This soft, silvery metal is currently used to power mobile connected devices, as a crucial material used for the production of lithium-Ion batteries. It is known as 'grey gold.' Smartphone batteries, for example, usually have less than eight grams of this material. 5 Each Tesla car needs approximately seven kilograms of lithium for its battery pack. 6 All these batteries have a limited lifespan, and once consumed they are thrown away as waste. Amazon reminds users that they cannot open up and repair their Echo, because this will void the warranty. The Amazon Echo is wall-powered, and also has a mobile battery base. This also has a limited lifespan and then must be thrown away as waste. According to the Ay
Bill Fulkerson

New Potential Credit Risk Bombs: Exotic, 'Nonlinear' and Private Transactions | naked c... - 0 views

www.nakedcapitalism.com/...inear-private-transaction.html

capitalism markets risk

shared by Bill Fulkerson on 08 Nov 17 - No Cached
  • Bill Fulkerson on 08 Nov 17
     
    "The large point is that this development is a sign of how desperate search for yield has become. In and of itself this "new" product appears not to be significant in aggregate terms, so in isolation it is not too alarming. But it is the sort of thing that can burn the unwary badly. If it becomes a meaningful-sized activty, the large-scale hazards go up too. Stay tuned."
Steve Bosserman

Will AI replace Humans? - FutureSin - Medium - 0 views

medium.com/...ai-replace-humans-42c19c114cb8

humans robotics artificial intelligence

shared by Steve Bosserman on 26 Apr 18 - No Cached
  • According to the World Economic Forum’s Future of Jobs report, some jobs will be wiped out, others will be in high demand, but all in all, around 5 million jobs will be lost. The real question is then, how many jobs will be made redundant in the 2020s? Many futurists including Google’s Chief Futurist believe this will necessitate a universal human stipend that could become globally ubiquitous as early as the 2030s.
  • AI will optimize many of our systems, but also create new jobs. We don’t know the rate at which it will do this. Research firm Gartner further confirms the hypothesis of AI creating more jobs than it replaces, by predicting that in 2020, AI will create 2.3 million new jobs while eliminating 1.8 million traditional jobs.
  • In an era where it’s being shown we can’t even regulate algorithms, how will we be able to regulate AI and robots that will progressively have a better capacity to self-learn, self-engineer, self-code and self-replicate? This first wave of robots are simply robots capable of performing repetitive tasks, but as human beings become less intelligent trapped in digital immersion, the rate at which robots learn how to learn will exponentially increase.How do humans stay relevant when Big Data enables AI to comb through contextual data as would a supercomputer? Data will no longer be the purvey of human beings, neither medical diagnosis and many other things. To say that AI “augments” human in this respect, is extremely naive and hopelessly optimistic. In many respects, AI completely replaces the need for human beings. This is what I term the automation economy.
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  • If China, Russia and the U.S. are in a race for AI supremacy, the kind of manifestations of AI will be so significant, they could alter the entire future of human civilization.
  • THE EXPONENTIAL THREATFrom drones, to nanobots to 3D-printing, automation could lead to unparalleled changes to how we live and work. In spite of the increase in global GDP, most people’s quality of living is not likely to see the benefit as it will increasingly be funneled into the pockets of the 1%. Capitalism then, favors the development of an AI that’s fundamentally exploitative to the common global citizen.Just as we exchanged our personal data for convenience and the illusion of social connection online, we will barter convenience for a world a global police state where social credit systems and AI decide how much of a “human stipend” (basic income) we receive. Our poverty or the social privilege we are born into, may have a more obscure relationship to a global system where AI monitors every aspect of our lives.Eventually AI will itself be the CEOs, inventors, master engineers and creator of more efficient robots. That’s when we will know that AI has indeed replaced human beings. What will Google’s DeepMind be able to do with the full use of next-gen quantum computing and supercomputers?
  • Artificial Intelligence Will Replace HumansTo argue that AI and robots and 3D-printing and any other significant technology won’t impact and replace many human jobs, is incredibly irresponsible.That’s not to say humans won’t adapt, and even thrive in more creative, social and meaningful work!That AI replacing repetitive tasks is a good thing, can hardly be denied. But will it benefit all globally citizens equally? Will ethics, common sense and collective pragmatism and social inclusion prevail over profiteers?Will younger value systems such as decentralization and sustainable living thrive with the advances of artificial intelligence?Will human beings be able to find sufficient meaning in a life where many of them won’t have a designated occupation to fill their time?These are the question that futurists like me ponder, and you should too.
Steve Bosserman

Could a Basic Income Plan End Poverty in Washington, D.C.? - 0 views

undark.org/...yd-blog-universal-basic-income

universal basic income

shared by Steve Bosserman on 12 May 18 - No Cached
  • The 86-page report, from the District’s Office of the Budget Director, applies evidence-based methodology and economic forecasting to compare two approaches to alleviating poverty: a negative local income tax (similar to the federal earned-income tax credit for poor families) versus a straightforward cash grant, known as guaranteed or universal basic income (UBI for short).
  • “There are certainly a lot of advantages to a UBI program,” one of the researchers, Susanna Groves, a senior budget analyst for the city, said on a recent episode of “The Basic Income Podcast.” “It doesn’t have a social stigma attached to it. It provides a benefit for everyone and could really help. But what we found is that a negative income tax was a more feasible approach for the District to provide a minimum income benefit.”
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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  • 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.
  • And sometimes, even when ample data exists, those who build the training sets don’t take deliberate measures to ensure its diversity
  • But not everyone will be equally represented in that data.
  • 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

Here's why robots are making income inequality even worse - 0 views

mic.com/...g-income-inequality-even-worse

robotics unemployment job displacement

shared by Steve Bosserman on 30 Apr 17 - No Cached
  • Automation has not triggered mass unemployment, despite concerns about increasingly-realistic innovations like driverless trucks and ATMs that can handle mortgages. But already, around the edges, robots are on the rise — creating cost savings that benefit corporations and Americans who run businesses. These benefits are increasingly going to a small group at the top.In fact, the income gap between the super rich and middle class has also increased at a rapid clip — by $58,800 between 2010 and 2015, as Bloomberg reported. And within the middle class itself, the gap between upper and lower middle class grew by $9,000.
Steve Bosserman

It wasn't just hate. Fascism offered robust social welfare | Aeon Ideas - 0 views

aeon.co/...-wing-anti-capitalist-movement

fascism capitalism social contract democracy

shared by Steve Bosserman on 27 Mar 17 - No Cached
  • The origins of fascism lay in a promise to protect people. In the late 19th and early 20th centuries, a rush of globalisation destroyed communities, professions and cultural norms while generating a wave of immigration. Right-wing nationalist movements promising to protect people from the pernicious influence of foreigners and markets arose, and frightened, disoriented and displaced people responded. These early fascist movements disrupted political life in some countries, but they percolated along at a relatively low simmer until the Second World War.
  • After coming to power, the Italian fascists created recreational circles, student and youth groups, sports and excursion activities. These organisations all furthered the fascists’ goals of fostering a truly national community. The desire to strengthen (a fascist) national identity also compelled the regime to extraordinary cultural measures. They promoted striking public architecture, art exhibitions, and film and radio productions. The regime intervened extensively in the economy. As one fascist put it: ‘There cannot be any single economic interests which are above the general economic interests of the state, no individual, economic initiatives which do not fall under the supervision and regulation of the state, no relationships of the various classes of the nation which are not the concern of the state.’
  • When, in January 1933, Hitler became chancellor, the Nazis quickly began work-creation and infrastructure programmes. They exhorted business to take on workers, and doled out credit. Germany’s economy rebounded and unemployment figures improved dramatically: German unemployment fell from almost 6 million in early 1933 to 2.4 million by the end of 1934; by 1938, Germany essentially enjoyed full employment. By the end of the 1930s, the government was controlling decisions about economic production, investment, wages and prices. Public spending was growing spectacularly.
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  • There can be no question that violence and racism were essential traits of fascism. But for most Italians, Germans and other European fascists, the appeal was based not on racism, much less ethnic cleansing, but on the fascists’ ability to respond effectively to crises of capitalism when other political actors were not. Fascists insisted that states could and should control capitalism, that the state should and could promote social welfare, and that national communities needed to be cultivated. The fascist solution ultimately was, of course, worse than the problem. In response to the horror of fascism, in part, New Deal Democrats in the United States, and social democratic parties in Europe, also moved to re-negotiate the social contract. They promised citizens that they would control capitalism and provide social welfare policies and undertake other measures to strengthen national solidarity – but without the loss of freedom and democracy that fascism entailed.
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