Read YouTube CEO Susan Wojcicki’s Response to the Controversial Google Anti-Diversity Memo - Fortune

Yesterday, after reading the news, my daughter asked me a question. “Mom, is it true that there are biological reasons why there are fewer women in tech and leadership?”

That question, whether it’s been asked outright, whispered quietly, or simply lingered in the back of someone’s mind, has weighed heavily on me throughout my career in technology. Though I’ve been lucky to work at a company where I’ve received a lot of support—from leaders like Larry Page, Sergey Brin, Eric Schmidt, and Jonathan Rosenberg to mentors like Bill Campbell—my experience in the tech industry has shown me just how pervasive that question is.

Gender Bias, Prejudice, SexismBrigitte BellanAugust 26, 2017Susan Wojcicki, Fortune, Google, YouTube

AI May Soon Replace Even the Most Elite Consultants - HBR

mazon’s Alexa just got a new job. In addition to her other 15,000 skills like playing music and telling knock-knock jokes, she can now also answer economic questions for clients of the Swiss global financial services company, UBS Group AG.

According to the Wall Street Journal (WSJ), a new partnership between UBS Wealth Management and Amazon allows some of UBS’s European wealth-management clients to ask Alexa certain financial and economic questions. Alexa will then answer their queries with the information provided by UBS’s chief investment office without even having to pick up the phone or visit a website. And this is likely just Alexa’s first step into offering business services. Soon she will probably be booking appointments, analyzing markets, maybe even buying and selling stocks. While the financial services industry has already begun the shift from active management to passive management, artificial intelligence will move the market even further, to management by smart machines, as in the case of Blackrock, which is rolling computer-driven algorithms and models into more traditional actively-managed funds.

Gender Bias, Bias, AIBrigitte BellanAugust 26, 2017HBR, Barry Libert, Open Matters, Megan Beck, Alexa, Cognitive Bias

How to make a racist AI without really trying - ConceptNet Blog

Rob Speer

Perhaps you heard about Tay, Microsoft’s experimental Twitter chat-bot, and how within a day it became so offensive that Microsoft had to shut it down and never speak of it again. And you assumed that you would never make such a thing, because you’re not doing anything weird like letting random jerks on Twitter re-train your AI on the fly.

My purpose with this tutorial is to show that you can follow an extremely typical NLP pipeline, using popular data and popular techniques, and end up with a racist classifier that should never be deployed.

There are ways to fix it. Making a non-racist classifier is only a little bit harder than making a racist classifier. The fixed version can even be more accurate at evaluations. But to get there, you have to know about the problem, and you have to be willing to not just use the first thing that works.

AI, RacismBrigitte BellanAugust 26, 2017Conceptnet, Rob Speers, Luminoso, GitHub

Machines trained on photos learn to be sexist towards women - Wired

Last Autumn, University of Virginia computer-science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. “It would see a picture of a kitchen and more often than not associate it with women, not men,” he says.

That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to “train” image-recognition software.

Their results are illuminating. Two prominent research-image collections—including one supported by Microsoft and Facebook—display a predictable gender bias in their depiction of activities such as cooking and sports. Images of shopping and washing are linked to women, for example, while coaching and shooting are tied to men. Machine-learning software trained on the datasets didn’t just mirror those biases, it amplified them. If a photo set generally associated women with cooking, software trained by studying those photos and their labels created an even stronger association.

Mark Yatskar, a researcher at the Allen Institute for Artificial Intelligence, says that phenomenon could also amplify other biases in data, for example related to race. “This could work to not only reinforce existing social biases but actually make them worse,” says Yatskar, who worked with Ordóñez and others on the project while at the University of Washington.

Alexa, Siri, Cortana: Our virtual assistants say a lot about sexism -Science Friction

OK, Google. We need to talk.

For that matter — Alexa, Siri, Cortana — we should too.

The tech world's growing legion of virtual assistants added another to its ranks last month, with the launch of Google Home in Australia.

And like its predecessors, the device speaks in dulcet tones and with a woman's voice. She sits on your kitchen table — discreet, rotund and white — at your beck and call and ready to respond to your questions.

But what's with all the obsequious, subservient small talk? And why do nearly all digital assistants and chatbots default to being female?

A handmaid's tale

Feminist researcher and digital media scholar Miriam Sweeney, from the University of Alabama, believes the fact that virtual agents are overwhelmingly represented as women is not accidental.

"It definitely corresponds to the kinds of tasks they carry out," she says.

Bots, Bias, Gender Bias, AI, Sexism, StereotypesBrigitte BellanAugust 13, 2017Natasha Mitchell, Alexa, Cortana, Siri, Google, Sexual harassment, sexism

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.

Bias, Bots, Gender Bias, AIBrigitte BellanAugust 13, 2017Bias, gebder, Washington Post, Sara Wachter-Boettcher

We tested bots like Siri and Alexa to see who would stand up to sexual harassment -Quartz

Women have been made into servants once again. Except this time, they’re digital.

Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Google Home peddle stereotypes of female subservience—which puts their “progressive” parent companies in a moral predicament.

People often comment on the sexism inherent in these subservient bots’ female voices, but few have considered the real-life implications of the devices’ lackluster responses to sexual harassment. By letting users verbally abuse these assistants without ramifications, their parent companies are allowing certain behavioral stereotypes to be perpetuated. Everyone has an ethical imperative to help prevent abuse, but companies producing digital female servants warrant extra scrutiny, especially if they can unintentionally reinforce their abusers’ actions as normal or acceptable.

Why we desperately need women to design AI - Medium

At the moment, only about 12–15% of the engineers who are building the internet and its software are women.

Here are a couple examples that illustrate why this is such a big a problem:

Do you remember when Apple released it’s health app a few years ago? Its purpose was to offer a ‘comprehensive’ access point to health information and data. But it left out a large health issue that almost all women deal with, and then took a year to fix that hole.
Then there was that frustrated middle school-aged girl who enjoyed gaming, but couldn’t find an avatar she related to. So she analyzed 50 popular games and found that 98% of them had male avatars (mostly free!), and only 46% of them had female avatars (mostly available for a charge!). Even more askew when you consider that almost half of gamers are women.

We don’t want a repeat of these kinds of situations. And we’ve been working to address this at Women 2.0 for over a decade. We think a lot about how diversity — or lack thereof. We think about it has affected — and is going to affect — the technology outputs that enter our lives. These technologoies engage with us. The determine our behaviors, thought processes, buying patterns, world views… you name it. This is part of the reason we recently launched Lane, a recruitment platform for female technologists.

Look Who’s Still Talking the Most in Movies: White Men -New York Times

With “Wonder Woman” and “Girls Trip” riding a wave of critical and commercial success at the box office this summer, it can be tempting to think that diversity in Hollywood is on an upswing.

But these high-profile examples are not a sign of greater representation in films over all. A new study from the University of Southern California’s Viterbi School of Engineering found that films were likely to contain fewer women and minority characters than white men, and when they did appear, these characters were portrayed in ways that reinforced stereotypes. And female characters, in particular, were generally less central to the plot.

Why AI Needs a Dose of Design Thinking - Deloitte/WSJ.com

Artificial intelligence technologies could reshape economies and societies, but more powerful algorithms do not automatically yield improved business or societal outcomes. Human-centered design thinking can help organizations get the most out of cognitive technologies.

Today’s artificial intelligence (AI) revolution has been made possible by the big data revolution. The machine learning algorithms researchers have been developing for decades, when cleverly applied to today’s web-scale data sets, can yield surprisingly good forms of intelligence. For instance, the United States Postal Service has long used neural network models to automatically read handwritten zip code digits. Today’s deep learning neural networks can be trained on millions of electronic photographs to identify faces, and similar algorithms may increasingly be used to navigate automobiles and identify tumors in X-rays. The IBM Watson information retrieval system could triumph on the game show “Jeopardy!” partly because most human knowledge is now stored electronically.

The Global Search for Education: How Are We Doing On The Gender Agenda? – Millennials Weigh In

Posted By C. M. Rubin on Jul 27, 2017

In an interview with CMRubinWorld, Dr. Linda Scott, Emeritus DP World Chair for Entrepreneurship and Innovation at the Said Business School, University of Oxford, reminds us that the gender gap is everywhere, “real and measurable” and not a “figment of some feminist’s imagination.” The global research confirms that gender inequality “retards economic growth, perpetuates poverty,” and “is bad for families.”

Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks.

ON A SPRING AFTERNOON IN 2014, Brisha Borden was running late to pick up her god-sister from school when she spotted an unlocked kid’s blue Huffy bicycle and a silver Razor scooter. Borden and a friend grabbed the bike and scooter and tried to ride them down the street in the Fort Lauderdale suburb of Coral Springs.

Just as the 18-year-old girls were realizing they were too big for the tiny conveyances — which belonged to a 6-year-old boy — a woman came running after them saying, “That’s my kid’s stuff.” Borden and her friend immediately dropped the bike and scooter and walked away.

But it was too late — a neighbor who witnessed the heist had already called the police. Borden and her friend were arrested and charged with burglary and petty theft for the items, which were valued at a total of $80.

Compare their crime with a similar one: The previous summer, 41-year-old Vernon Prater was picked up for shoplifting $86.35 worth of tools from a nearby Home Depot store.

Prater was the more seasoned criminal. He had already been convicted of armed robbery and attempted armed robbery, for which he served five years in prison, in addition to another armed robbery charge. Borden had a record, too, but it was for misdemeanors committed when she was a juvenile.

Yet something odd happened when Borden and Prater were booked into jail: A computer program spat out a score predicting the likelihood of each committing a future crime. Borden — who is black — was rated a high risk. Prater — who is white — was rated a low risk.

Transparency, Bias, RacismBrigitte BellanJuly 22, 2017ProPublica, Academic, Criminal Justice, Risk Assessments

Diversity in the Robot Reporter Newsroom

The Associated Press recently announced a big new hire: A robot reporter from Automated Insights (AI) would be employed to write up to 4,400 earnings report stories per quarter. Last year, that same automated writing software produced over 300 million stories — that’s some serious scale from a single algorithmic entity.

So what happens to media diversity in the face of massive automated content production platforms like the one Automated Insights created? Despite the fact that we’ve done pretty abysmally at incorporating a balance of minority and gender perspectives in the news media, I think we’d all like to believe that by including diverse perspectives in the reporting and editing of news we fly closer to the truth. A silver lining to the newspaper industry crash has been a profusion of smaller, more nimble media outlets, allowing for far more variability and diversity in the ideas that we’re exposed to.

Inspecting Algorithms for Bias - MIT Technology Review

Courts, banks, and other institutions are using automated data analysis systems to make decisions about your life. Let’s not leave it up to the algorithm makers to decide whether they’re doing it appropriately.

It was a striking story. “Machine Bias,” the headline read, and the teaser proclaimed: “There’s software used across the country to predict future criminals. And it’s biased against blacks.”

Racism, AIBrigitte BellanJuly 22, 2017MIT Technology Review, Matthias Spielkamp

Is Your AI Racist? - American Banker

WASHINGTON — The question of whether an algorithm making underwriting decisions can be racially biased has long gnawed at lenders fearful of inadvertently running afoul of consumer protection laws. Now, the question has sparked interest from Congress.

AI, Prejudice, Racism, Gender BiasBrigitte BellanJuly 2, 2017American Banker, Law, Congress

AI robots learning racism, sexism, and other prejudices from humans, study finds - The Independent

Artificially intelligent robots and devices are being taught to be racist, sexist and otherwise prejudiced by learning from humans, according to new research.

A massive study of millions of words online looked at how closely different terms were to each other in the text – the same way that automatic translators use “machine learning” to establish what language means.

Some of the results were stunning.

Women In Data Science- Betterbuys

Women have always been instrumental in technology development. Yet, according to a report by the National Center for Women & Information Technology (NCWIT), the amount of women in computing occupations has steadily declined since 1991, when it peaked at 36%.

Do women now have a general lack of interest in Science, Technology, Engineering and Mathematics (STEM) positions? Is there a bias inhibiting women’s success in these roles? Are there cultural elements causing the decline?

We’ve tried to get to the bottom of the large gender gap in the tech and data science field.

Women in Science, DataBrigitte BellanJuly 1, 2017Women in Data Science, Data Science, STEM, NCWIT

Women in Data Science are Invisible - Wired

I HAVE TO admit that I never really gave the number of women in data science much thought until recently. Maybe it was because, by some lucky accident, my NYU faculty advisor’s two other PhD students also happened to be female. And about half of my predictive modelling group peers at IBM Research were female. And half of the PhDs here at Dstillery are female.

Data, Gender BiasBrigitte BellanJune 29, 2017Women in Data Science, wired

You may be stereotyped if you use these words - Business Insider

Stereotyping happens. A new study helps identify how it happens and what it gets wrong by asking participants to make predictions about people based on their Tweets.

Researchers at the University of Pennsylvania and other institutions had subjects read sets of 20 Tweets and predict the writer's gender, age, political orientation, and education level based on the words they used. Subjects' guesses were fairly accurate — they guessed right 76% of the time on gender, 69% on whether the person was older or younger than 24, and 82% on liberal versus conservative. They were only right in 46% of cases, however, when predicting whether the Tweet-writers had no bachelor’s degree, a bachelor’s degree, or an advanced degree.

Academic, StereotypesBrigitte BellanJune 29, 2017University of Penn, Business Insider

We Recorded VCs' Conversations and Analyzed How Differently They Talk About Female Entrepreneurs - HBR

When venture capitalists (VCs) evaluate investment proposals, the language they use to describe the entrepreneurs who write them plays an important but often hidden role in shaping who is awarded funding and why. But it’s difficult to obtain VCs’ unvarnished comments, given that they are uttered behind closed doors. We were given access to government venture capital decision-making meetings in Sweden and were able to observe the types of language that VCs used over a two-year period. One major thing stuck out: The language used to describe male and female entrepreneurs was radically different. And these differences have very real consequences for those seeking funding — and for society in general.

Stereotypes, Gender BiasBrigitte BellanJune 29, 2017HBR, VC, Female Entrepreneurs