Posts tagged Bias
DeepMind's Mustafa Suleyman: In 2018, AI will gain a moral compass - Wired

Humanity faces a wide range of challenges that are characterised by extreme complexity, from climate change to feeding and providing healthcare for an ever-expanding global population. Left unchecked, these phenomena have the potential to cause devastation on a previously untold scale. Fortunately, developments in AI could play an innovative role in helping us address these problems.

At the same time, the successful integration of AI technologies into our social and economic world creates its own challenges. They could either help overcome economic inequality or they could worsen it if the benefits are not distributed widely. They could shine a light on damaging human biases and help society address them, or entrench patterns of discrimination and perpetuate them. Getting things right requires serious research into the social consequences of AI and the creation of partnerships to ensure it works for the public good.

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Microsoft Researcher Details The Real-World Dangers Of Algorithm Bias

However quickly artificial intelligence evolves, however steadfastly it becomes embedded in our lives -- in health, law enforcement, sex, etc. -- it can't outpace the biases of its creators, humans. Microsoft Researcher Kate Crawford delivered an incredible keynote speech, titled "The Trouble with Bias" at Spain's Neural Information Processing System Conference on Tuesday.

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Understanding Bias in Algorithmic Design - ASME Demand

In 2016, The Seattle Times uncovered an issue with a popular networking site’s search feature. When the investigative reporters entered female names into LinkedIn’s search bar, the site asked if they meant to search for similar sounding male names instead — “Stephen Williams” instead of “Stephanie Williams,” for example. According to the paper’s reporting, however, the trend wouldn’t happen in reverse, when a user searched for male names.

Within a week of The Seattle Times article’s release, LinkedIn introduced a fix. Spokeswoman Suzi Owens told the paper that the search algorithm had been guided by “relative frequencies of words” from past searches and member profiles, not by gender. Her explanation suggests that LinkedIn’s algorithm was not intentionally biased. Nevertheless, using word frequency — a seemingly objective variable — as a key parameter still generated skewed results. That could be because men are more likely to have a common name than American women, according to Social Security data. Thus, building a search function based on frequency criteria alone would more likely increase visibility for Stephens than Stephanies.

Examples like this demonstrate how algorithms can unintentionally reflect and amplify common social biases. Other recent investigations suggest that such incidents are not uncommon. In a more serious case, the investigative news organization ProPublica uncovered a correlation between race and criminal recidivism predictions in so-called “risk assessments” — predictive algorithms that are used by courtrooms to inform terms for bail, sentencing, or parole. The algorithmic predictions for recidivism generated a higher rate of false-negatives for white offenders and a higher rate of false-positives for black offenders, even though overall error rates were roughly the same.

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Artificial Intelligence—With Very Real Biases-WSJ

According to AI Now co-founder Kate Crawford, digital brains can be just as error-prone and biased as ours.

What do you imagine when someone mentions artificial intelligence? Perhaps it’s something drawn from science-fiction films: Hal’s glowing eye, a shape-shifting terminator or the sound of Samantha’s all-knowing voice in the movie “Her.”

As someone who researches the social implications of AI, I tend to think of something far more banal: a municipal water system, part of the substrate of our everyday lives. We expect these systems to work—to quench our thirst, water our plants and bathe our children. And we assume that the water flowing into our homes and offices is safe. Only when disaster strikes—as it did in Flint, Mich.—do we realize the critical importance of safe and reliable infrastructure.

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Biases in Algorithms - Cornell University Blog

In class we have recently discussed how the search algorithm for Google works. From the very basic material that we learned about the algorithm, it seems like the algorithm is resistant to failure due to its very systematic way of organizing websites. However, after considering how it works, is it possible that the algorithm is flawed? More specifically, how so from a social perspective?

Well, as it turns out, many algorithms are indeed flawed- including the search algorithm. The reason being is that algorithms are ultimately coded by individuals who inherently have biases. And although there continues to be a push for the promotion of people of color in STEM fields, the reality at the moment is that the majority of people in charge of designing algorithms are White males.

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Who Controls Our Algorithmic Future? - Datanami

Alex Woodie

The accelerating pace of digitization is bringing real, tangible benefits to our society and economy, which we cover daily in the pages on this site. But increased reliance on machine learning algorithms brings its own unique set of risks that threaten to unwind progress and turn people against one another. Three speakers at last week’s Strata Data Conference in New York put in all in perspective.

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Examining Gender and Emotion in Political News Debates - Affectiva Blog

Blog post by: Juliana Viola, Intern at Affectiva

Today in the US and around the world, women are undeniably underrepresented in politics. American women make up just 19.4% of Congress and 24.9% of state legislators. Globally, just ten women serve as head of state and nine as head of government. This lack of diversity brings huge consequences; time and time again, studies have documented how diversity can spur workplace innovation and boost productivity. Therefore, increasing the representation of women, specifically women of color, in government offices would likely lead to a more effective government.

Along the same vein, as an avid news junkie, I have often noticed homogeneity in the panel discussions I watch on TV. Political panels in particular are often comprised of mostly men. I wondered how I could capture metrics about how panel members emote and participate in the discussion, and how these metrics might vary by gender. For example, how is airtime split between men and women? Since the American public relies on political talk shows for perspective, these panels would ideally represent a diversity of voices to interpret objective information.

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How Silicon Valley's sexism affects your life - Washington Post

It was a rough week at Google. On Aug. 4, a 10-page memo titled "Google's Ideological Echo Chamber" started circulating among employees. It argued that the disparities between men and women in tech and leadership roles were rooted in biology, not bias. On Monday, James Damore, the software engineer who wrote it, was fired; he then filed a labor complaint to contest his dismissal.

We've heard lots about Silicon Valley's toxic culture this summer - venture capitalists who proposition female start-up founders, man-child CEOs like Uber's Travis Kalanick, abusive nondisparagement agreements that prevent harassment victims from describing their experiences. Damore's memo added fuel to the fire, arguing that women are more neurotic and less stress-tolerant than men, less likely to pursue status, and less interested in the "systemizing" work of programming. "We need to stop assuming that gender gaps imply sexism," he concludes.

Like the stories that came before it, coverage of this memo has focused on how a sexist tech culture harms people in the industry - the women and people of color who've been patronized, passed over, and pushed out. But what happens in Silicon Valley doesn't stay in Silicon Valley. It comes into our homes and onto our screens, affecting all of us who use technology, not just those who make it.

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