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Steve Bosserman

60 Minutes: Facial and emotional recognition; how one man is advancing artificial intel... - 0 views

www.cbsnews.com/...ancing-artificial-intelligence

data mining data analysis artificial intelligence facial recognition climate change cognitive dissonance conservatives man intelligence news

shared by Steve Bosserman on 15 Jan 19 - No Cached
  • Basically chauffeurs, truck drivers anyone who does driving for a living their jobs will be disrupted more in the 15 to 20 year time frame and many jobs that seem a little bit complex, chef, waiter, a lot of things will become automated we'll have automated stores, automated restaurants, and all together in 15 years, that's going to displace about 40 percent of the jobs in the world.
  • Because I believe in the sanctity of our soul. I believe there is a lot of things about us that we don't understand. I believe there's a lot of love and compassion that is not explainable in terms of neural networks and computation algorithms. And I currently see no way of solving them. Obviously, unsolved problems have been solved in the past. But it would be irresponsible for me to predict that these will be solved by a certain timeframe.
Steve Bosserman

The wealth of our collective data should belong to all of us | Chris Hughes - 0 views

www.theguardian.com/...ook-google-data-tax-regulation

data mining

shared by Steve Bosserman on 28 Apr 18 - No Cached
  • Nearly every moment of our lives, we’re producing data about ourselves that companies profit from. Our smartwatches know when we wake up, Alexa listens to our private conversations, our phones track where we go, Google knows what we email and search, Facebook knows what we share with friends, and our loyalty cards remember what we buy. We share all this data about ourselves because we like the services these companies provide, and business leaders tell us we must to make it possible for those services to be cheap or free.
  • We should not only expect that these companies better protect our data – we should also ensure that everyone creating it shares in the economic value it generates. One person’s data is worth little, but the collection of lots of people’s data is what fuels the insights that companies use to make more money or networks, like Facebook, that marketers are so attracted to. Data isn’t the “new oil”, as some have claimed: it isn’t a non-renewable natural resource that comes from a piece of earth that a lucky property owner controls. We have all pitched in to create a new commonwealth of information about ourselves that is bigger than any single participant, and we should all benefit from it.
  • The value of our data has a lot in common with the value of our labor: a single individual worker, outside of the rarest professions, can be replaced by another with similar skills. But when workers organize to withhold their labor, they have much more power to ensure employers more fairly value it. Just as one worker is an island but organized workers are a force to be reckoned with, the users of digital platforms should organize not only for better protection of our data, but for a new contract that ensures everyone shares in the historic profits we make possible.
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  • A data dividend would be a powerful way to rebalance the American economy, which currently makes it possible for a very small number of people to get rich while everyone else struggles to make ends meet.
  • A data dividend on its own would not be enough to stem growing income inequality, but it would create a universal benefit that would guarantee people benefit from the collective wealth our economy is creating more than they do today. If paired with fairer wages, more progressive taxation, and stricter enforcement of monopoly and monopsony power, it could help us turn the corner and create a country where we take care of one another and ensure that everyone has basic economic security.
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.
Bill Fulkerson

Full article: Re-assembling the surveillable refugee body in the era of data-craving - 0 views

www.tandfonline.com/...13621025.2020.1784641

Data data science data mining

shared by Bill Fulkerson on 28 Jul 20 - No Cached
  • Bill Fulkerson on 28 Jul 20
     
    This article traces the travel of biometric data of Syrian refugees in Jordan through a hastily evolving political economy characterized by a pervasive craving for the extraction, storage and brokering of displacement data. It analyzes iris-enrollment as problematic acts of quasi-citizenship for the displaced requiring the performance of social and economic docility in order to attain identity, cash and service provision. Quasi-objects in the form of digital footprints are fashioned through infrastructures that simultaneously seek to model, yet fail to capture, socioeconomic existence in displacement contexts. Discourses of anti-fraud, donor dictates, upward accountability and strategies of financial inclusion of 'the unbanked', facilitate the marketization of the creation of data-doubles in laboratories of displacement and loopholes for externalization. Driven by increasingly blurred lines between technological, humanitarian and financial interests, this development has transformative effects on both those displaced, and on a humanitarian sector tasked with safeguarding their rights.
Bill Fulkerson

Do algorithms discriminate - 0 views

www.fastcompany.com/...tubborn-to-be-eliminated-by-ai

bias HR data mining

shared by Bill Fulkerson on 20 May 20 - No Cached
  • Bill Fulkerson on 20 May 20
     
    As the executive and academic director of a leadership center, my research indicates that relying on data analytics to eliminate human bias in choosing leaders won't help.
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