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

Women will wait 217 years for pay gap to close, WEF says - The Guardian

Gender parity ‘shifting into reverse’ as World Economic Forum adds 47 years to time needed to reach workplace equality.

The authors of a new report forecasting that it could take 170 years to eradicate the disparity in pay and employment opportunities for men and women have called for urgent action to close the gender equality gap.

The report by the World Economic Forum – best known for its high-profile gathering each year in Davos, Switzerland – found that economic disparity between women and men around the world was rising even though the gap was closing on other measures, such as education.

A Study Used Sensors to Show That Men and Women Are Treated Differently at Work - HBR

Gender equality remains frustratingly elusive. Women are underrepresented in the C-suite, receive lower salaries, and are less likely to receive a critical first promotion to manager than men. Numerous causes have been suggested, but one argument that persists points to differences in men and women’s behavior.

Which raises the question: Do women and men act all that differently? We realized that there’s little to no concrete data on women’s behavior in the office. Previous work has relied on surveys and self-reported assessments — methods of data collecting that are prone to bias. Fortunately, the proliferation of digital communication data and the advancement of sensor technology have enabled us to more precisely measure workplace behavior.

We decided to investigate whether gender differences in behavior drive gender differences in outcomes at one of our client organizations, a large multinational firm, where women were underrepresented in upper management. In this company, women made up roughly 35%–40% of the entry-level workforce but a smaller percentage at each subsequent level. Women made up only 20% of people at the two highest seniority levels at this organization.

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.

Emploi : réconcilier l’humain et la machine pour une IA éthique - Silicon

Emploi : réconcilier l’humain et la machine pour une IA éthique

Ariane Beky, 27 octobre 2017, 17:54

Le think tank Renaissance Numérique et le groupe Randstad France prônent une collaboration étroite entre humains et technologies d’intelligence artificielle.

Le débat public coordonné par la Commission nationale informatique et libertés (CNIL) sur les enjeux soulevés par les algorithmes et l’intelligence artificielle (IA) se poursuit.

Agents conversationnels, automates, outils de traduction, recommandation, géolocalisation… Les technologies d’intelligence artificielle ont une influence croissante sur la société, le travail et l’emploi. Le think tank Renaissance Numérique et le groupe d’intérim et services RH Randstad ont étudié la problématique. Jeudi, ils ont rendu public leur contribution intitulée : « L’éthique dans l’emploi à l’ère de l’intelligence artificielle ».

En France, un emploi sur deux va se transformer sous l’effet combiné de l’automatisation et de la numérisation. Certaines tâches, et pas uniquement les plus pénibles et répétitives, ne seront plus effectuées par les humains. En revanche, l’hypothèse d’une destruction massive d’emplois remplacés par des technologies d’intelligence artificielle est écartée.

AI, EthicsBrigitte BellanOctober 29, 2017Ariane Beky, Silicon, Renaissance Numerique, Randstad

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.

Biased decisions, Women in the WorkplaceBrigitte BellanOctober 28, 2017AI NOW, Kate Crawford, WSJ, Bias, Hiring

Can we talk about the gender pay gap? By Xaquín G.V., Washington Post

The median salary for women working full-time is about 80 percent of men’s. That gap, put in other terms, means women are working for free 10 weeks a year.

... you started working for free 15 hours ago

Well, that is a little blunt — there are gradients on that difference. The pay gap varies depending on the occupation, working hours, education attainment, experience, and geography.

Taking control of your unconscious bias? Guardian/HSBC

With attention now a scarce resource, we increasingly rely on algorithms to help us navigate the world. Only now are we beginning to experience the side-effects of these filter bubbles as our ability to see and understand the bigger picture is eroding.

Part 1: Six key unconscious biases when making decisions

Dr Norma Montague cites five key unconscious biases to be aware of when making decisions. We’ve added a sixth for good measure.

Full article: https://www.theguardian.com/hsbc-fuel-the-ambition-series/2017/aug/29/taking-control-of-your-unconscious-bias

Bias, Biased decisionsBrigitte BellanOctober 25, 2017Guardian, HSBC, Norma Montague, Uncounscious Bias

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

Biases in Algorithms - Cornell University Blog

http://www.pewinternet.org/2017/02/08/theme-4-biases-exist-in-algorithmically-organized-systems/

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.

Bias, Academic, Gender Bias, StereotypesBrigitte BellanOctober 20, 2017Bias, algorithms, Cornell

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

Start-Ups Use Technology to Redesign the Hiring Process - NY Times

Iris Bohnet, a behavioral economist and professor at the Harvard Kennedy School, spoke to the founders of two behavioral design start-ups, Kate Glazebrook of Applied and Frida Polli of Pymetrics, for the latest on the algorithmic design revolution that is transforming hiring practices.

Hatier publie le premier manuel scolaire en écriture inclusive - HuffPost

FÉMINISME - "Grâce aux agriculteur.rice.s, aux artisan.e.s et aux commerçant.e.s, la Gaule était un pays riche." C'est avec des phrases de ce type que des enfants de CE2 apprendront l'histoire dans un manuel Hatier pour l'année scolaire 2017-2018, comme l'a repéré Le Figaro vendredi 22 septembre.

La maison d'édition, qui publie notamment le Bescherelle, a diffusé au mois de mars dernier ce premier manuel en 'écriture inclusive', un mode d'écriture qui féminise les mots en plaçant, entre des points, la terminaison du féminin.

Intitulé "Questionner le Monde", le livre a été repéré par un professeur de physique-chimie et une image diffusée dans un groupe d'enseignants sur Facebook, comme l'explique Le Figaro.

EDUCATION, Sexism, Gender Bias, Gender EqualityBrigitte BellanOctober 12, 2017HuffPost, Ecriture Inclusive, TextBook, French

Women in the Workplace 2017 - LeanIn.Org and McKinsey

More companies are committing to gender equality. But progress will remain slow unless we confront blind spots on diversity—particularly regarding women of color, and employee perceptions of the status quo.

Women remain underrepresented at every level in corporate America, despite earning more college degrees than men for 30 years and counting. There is a pressing need to do more, and most organizations realize this: company commitment to gender diversity is at an all-time high for the third year in a row.

Despite this commitment, progress continues to be too slow—and may even be stalling. Women in the Workplace 2017, a study conducted by LeanIn.Org and McKinsey, looks more deeply at why, drawing on data from 222 companies employing more than 12 million people, as well as on a survey of over 70,000 employees and a series of qualitative interviews. One of the most powerful reasons for the lack of progress is a simple one: we have blind spots when it comes to diversity, and we can’t solve problems that we don’t see or understand clearly.

Here's why gender equality is taking so long - World Economic Forum

The World Economic Forum estimates gender parity globally may now be over 170 years away. Previously they estimated an 80-year time, then it was 120 years. It keeps slowing down. The Forum's Annual Gender Gap Report shows slow progress and minimal change in many countries worldwide. What is causing this glacial pace of change, something the airline industry calls a “creeping delay”?

There are many headwinds that can lengthen the time required for desired systemic change, but there is one I’d like to address here, head on, and it’s this: unconscious bias.

In general, there is a lack of awareness about who others are and what their capabilities and inherent qualities may be. In corporations, this often manifests as a culture that is unfriendly or unhelpful to women.

Bias, Gender Gap, Gender EqualityBrigitte BellanOctober 7, 2017World Economic Forum

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.

Artificial Intelligence: Making AI in our Images - Savage Mind

Savage Minds welcomes guest blogger Sally Applin

Hello! I’m Sally Applin. I am a technology anthropologist who examines automation, algorithms and Artificial Intelligence (AI) in the context of preserving human agency. My dissertation focused on small independent fringe new technology makers in Silicon Valley, what they are making, and most critically, how the adoption of the outcomes of their efforts impact society and culture locally, and/or globally. I’m currently spending the summer in a corporate AI Research Group where I contribute to anthropological research on AI. I’m thrilled to blog for the renowned Savage Minds this month and hope many of you find value in my contributions.

Bias, AI, Gender Bias, Sexism, Racism, Women in ScienceBrigitte BellanSeptember 10, 2017Sally Applin, Anthroplogy, Savage Minds

Debiasing AI Systems- Luminoso Blog

One of the most-discussed topics in AI recently has been the growing realization that AI-based systems absorb human biases and prejudices from training data. While this has only recently become a hot news topic, AI organizations, including Luminoso, have been focused on this issue for a while. Denise Christie sat down with Luminoso’s Chief Science Officer, Rob Speer, to talk about how AI becomes biased in the first place, the impact such bias can have, and - more importantly - how to mitigate it.

Bias, AI, Gender BiasBrigitte BellanSeptember 7, 2017Luminoso, Rob Speers, Debiasing

She Giggles, He Gallops - The Pudding

Analyzing gender tropes in film with screen direction from 2,000 scripts.

By Julia Silge

In April 2016, we broke down film dialogue by gender. The essay presented an imbalance in which men delivered more lines than women across 2,000 screenplays. But quantity of lines is only part of the story. What characters do matters, too.

Gender tropes (e.g., women are pretty/men act, men don’t cry) are just as important as dialogue in understanding how men and women are portrayed on-screen. These stereotypes result from many components, including casting, acting, directing, etc.

Gender Bias, Stereotypes, SexismBrigitte BellanSeptember 3, 2017Julia Silge, The Pudding, Movie, Film, Screen Direction

Words ascribed to female economists: 'Hotter,' 'feminazi.' Men?: 'Goals,' 'Nobel.' - The Washington Post

In 1970, the economics department at the University of California at Berkeley hired three newly minted economics PhDs from the Massachusetts Institute of Technology. Two - both men - were hired as assistant professors. But a woman, Myra Strober, was hired as a lecturer, a position of inferior pay and status and no possibility of tenure. When she asked the department chairman why she was denied an assistant professorship, he put her off with excuses. She kept pressing him until he gave a frank answer: She had two young children; the department couldn't possibly put her on the tenure track.

So Strober took another offer. In 1972, she became the first female economist at Stanford's Graduate School of Business. "They didn't know what to make of me," she said. The faculty retreat, which had been held every year at a men's club, had to be moved. There were jokes about putting a bag over her head so they could keep going to the club.

"It was like trying to run a race with one of your legs tied behind you," Strober said of the culture.