Artificial intelligence could hardwire sexism into our future. Unless we stop it- WEF Blog

In five years’ time, we might travel to the office in driverless cars, let our fridges order groceries for us and have robots in the classroom. Yet, according to the World Economic Forum’s Global Gender Gap Report 2017, it will take another 100 years before women and men achieve equality in health, education, economics and politics.

What’s more, it's getting worse for economic parity: it will take a staggering 217 years to close the gender gap in the workplace.

How can it be that the world is making great leaps forward in so many areas, especially technology, yet it's falling backwards when it comes to gender equality?

Bias, AI, Biased decisions, Data, Gender Equality, Gender Gap, Women in Tech, Women in the WorkplaceBrigitte BellanDecember 16, 2017Alison Kay, EY, WEF

Can A.I. Be Taught to Explain Itself? New York Times

As machine learning becomes more powerful, the field’s researchers increasingly find themselves unable to account for what their algorithms know — or how they know it.

In September, Michal Kosinski published a study that he feared might end his career. The Economist broke the news first, giving it a self-consciously anodyne title: “Advances in A.I. Are Used to Spot Signs of Sexuality.” But the headlines quickly grew more alarmed. By the next day, the Human Rights Campaign and Glaad, formerly known as the Gay and Lesbian Alliance Against Defamation, had labeled Kosinski’s work “dangerous” and “junk science.”

(They claimed it had not been peer reviewed, though it had.) In the next week, the tech-news site The Verge had run an article that, while carefully reported, was nonetheless topped with a scorching headline: “The Invention of A.I. ‘Gaydar’ Could Be the Start of Something Much Worse.”

AI, Data, ethics, TransparencyBrigitte BellanDecember 1, 2017AI, Black Box, Deep Learning, Michal Kosinski

WHY AI IS STILL WAITING FOR ITS ETHICS TRANSPLANT- WIRED

There’s no lack of reports on the ethics of artificial intelligence. But most of them are lightweight—full of platitudes about “public-private partnerships” and bromides about putting people first. They don’t acknowledge the knotty nature of the social dilemmas AI creates, or how tough it will be to untangle them. The new report from the AI Now Institute isn’t like that. It takes an unblinking look at a tech industry racing to reshape society along AI lines without any guarantee of reliable and fair results.

The report, released two weeks ago, is the brainchild of Kate Crawford and Meredith Whittaker, cofounders of AI Now, a new research institute based out of New York University. Crawford, Whittaker, and their collaborators lay out a research agenda and a policy roadmap in a dense but approachable 35 pages. Their conclusion doesn’t waffle: Our efforts to hold AI to ethical standards to date, they say, have been a flop.

“New ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, governmental and military interests accountable as they design and employ AI,” they write. When tech giants build AI products, too often “user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit-driven business models based on aggregated data profiles…” Meanwhile, AI systems are being introduced in policing, education, healthcare, and other environments where the misfiring of an algorithm could ruin a life. Is there anything we can do? Crawford sat down with us this week for a discussion of why ethics in AI is still a mess, and what practical steps might change the picture.

Data, Bias, Biased decisions, AI, EthicsBrigitte BellanNovember 4, 2017AINOW, Kate Crawford, Meredith Whittaker

Your Data Are Probably Biased And That's Becoming A Massive Problem Beware of black boxes - INC

Nobody sets out to be biased, but it's harder to avoid than you would think. Wikipedia lists over 100 documented biases from authority bias and confirmation bias to the Semmelweis effect, we have an enormous tendency to let things other than the facts to affect our judgments. We all, as much as we hate to admit it, are vulnerable.

Machines, even virtual ones, have biases too. They are designed, necessarily, to favor some kinds of data over others. Unfortunately, we rarely question the judgments of mathematical models and, in many cases, their biases can pervade and distort operational reality, creating unintended consequences that are hard to undo.

Yet the biggest problem with data bias is that we are mostly unaware of it, because we assume that data and analytics are objective. That's almost never the case. Our machines are, for better or worse, extensions of ourselves and inherit our subjective judgments. As data and analytics increasingly become a core component of our decision making, we need to be far more careful.

Are algorithms making us W.E.I.R.D.? - alphr

Western, educated, industrialised, rich and democratic (WEIRD) norms are distorting the cultural perspective of new technologies

From what we see in our internet search results to deciding how we manage our investments, travel routes and love lives, algorithms have become a ubiquitous part of our society. Algorithms are not just an online phenomenon: they are having an ever-increasing impact on the real-world. Children are being born to couples who were matched by dating site algorithms, whilst the navigation systems for driverless cars are poised to transform our roads.

Bias, AI, Data, Gender BiasBrigitte BellanOctober 21, 2017alphr, Lucky Ghost, Matthew Blakstad, Cathy O'Neil, Weapon of Math Destruction

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.

Bias, AI, DataBrigitte BellanOctober 13, 2017Datanami, Cathy O'Neil, Weapon of Math Destruction, Bias, Training Data

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.

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