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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

You don't have a right to believe whatever you want to - Daniel DeNicola | Aeon Ideas - 0 views

aeon.co/...o-believe-whatever-you-want-to

responsibility beliefs rights truth

shared by Steve Bosserman on 14 Jun 18 - No Cached
  • Believing, like willing, seems fundamental to autonomy, the ultimate ground of one’s freedom. But, as Clifford also remarked: ‘No one man’s belief is in any case a private matter which concerns himself alone.’ Beliefs shape attitudes and motives, guide choices and actions. Believing and knowing are formed within an epistemic community, which also bears their effects. There is an ethic of believing, of acquiring, sustaining, and relinquishing beliefs – and that ethic both generates and limits our right to believe. If some beliefs are false, or morally repugnant, or irresponsible, some beliefs are also dangerous. And to those, we have no right.
Steve Bosserman

The complexity of social problems is outsmarting the human brain | Aeon Essays - 0 views

aeon.co/...is-outsmarting-the-human-brain

complexity brain wisdom cognition

shared by Steve Bosserman on 03 May 17 - No Cached
  • It’s time we asked whether political frustration, anger and resistance to conflicting ideas results in part from a basic lack of ability to sense how the present world works. The best defence against runaway combative ideologies isn’t more facts, arguments and a relentless hammering away at contrary opinions, but rather a frank admission that there are limits to both our knowledge and our assessment of this knowledge. If the young were taught to downplay blame in judging the thoughts of others, they might develop a greater degree of tolerance and compassion for divergent points of view. A kinder world calls for a new form of wisdom of the crowd.
Steve Bosserman

Why Trump's speech on terrorism was such a missed opportunity - The Washington Post - 0 views

www.washingtonpost.com/...-was-such-a-missed-opportunity

radicalization youth social contract unemployment

shared by Steve Bosserman on 22 May 17 - No Cached
  • The vast majority of young people in places like the Middle East and North Africa face a bleak socioeconomic future. Youth unemployment in the region hovers around 30 percent, which is expected to skyrocket as economies struggle to create enough jobs to keep pace with a massive demographic youth bulge. In 2000, the World Bank estimated that the region would need to create about 100 million new jobs to keep pace, and it’s nowhere near to closing the gap.
  • The effect of this socioeconomic crisis is about more than just employment and livelihoods for young people; it represents a fundamental breakdown of the social contract in the region. For decades, governments in the Middle East and North Africa promised socioeconomic support — free education, subsidies and public-sector employment — in exchange for limits on political participation and civic activity.
Bill Fulkerson

The misunderstood limits of folk science: an illusion of explanatory depth - 0 views

www.ncbi.nlm.nih.gov/...PMC3062901

folk science stories

shared by Bill Fulkerson on 25 Sep 20 - No Cached
  • Bill Fulkerson on 25 Sep 20
     
    People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion-an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real-time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1-6. Then we show differences in overconfidence about knowledge across different knowledge domains in Studies 7-10. Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12. Implications for the roles of intuitive theories in models of concepts and cognition are discussed.
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