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You can read more about the GeSTE Windows Model on Dr Mandy Lupton's blog at 

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You can read more about the GeSTE Windows Model on Dr Mandy Lupton's blog at 

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https://inquirylearningblog.wordpress.com/

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Critically evaluating information using surface signs such as currency, authority, bias and provenance.

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Critically evaluating sources through a distinctive (often disciplinary) lens.

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Do you know what news sources are? Can you spot a reliable one in a news story? Test your re-source-fulness in our source checker quiz.

FOR TEACHERS
The Sources activity invites students to apply the skills and knowledge that they have acquired from the resources. They can test their ability to discern between reliable and unreliable sources of information and come to conclusions about the currency and accuracy of information presented and the purpose of the written piece.

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Do you know what news sources are? Can you spot a reliable one in a news story? Test your re-source-fulness in our source checker quiz. FOR TEACHERS The Sources activity invites students to apply the skills and knowledge that they have acquired from the resources.

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Critical evaluation forms and information

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Introduction to Crash Course Navigating Digital Information #1

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A brief detour from chemistry, branching out into science in general today. This graphic looks at the different factors that can contribute towards 'bad' science - it was inspired by the research I carried out for the recent aluminium chlorohydrate graphic, where many articles linked the compound to causing breast cancer, referencing scientific research which drew questionable conclusions from their results.

The vast majority of people will get their science news from online news site articles, and rarely delve into the research that the article is based on. Personally, I think it's therefore important that people are capable of spotting bad scientific methods, or realising when articles are being economical with the conclusions drawn from research, and that's what this graphic aims to do. Note that this is not a comprehensive overview, nor is it implied that the presence of one of the points noted automatically means that the research should be disregarded. This is merely intended to provide a rough guide to things to be alert to when either reading science articles or evaluating research.

EDIT: Updated to version 2!

EDIT 2 (April 2015): Update to version 3, taking into account a range of feedback and also sprucing up the design a little.
Support Compound Interest on Patreon for post previews and more!
The graphic in this article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Want to share it elsewhere? See the site's content usage guidelines.
You can download the previous version as a PDF here.
Purchase in A2, A1 & A0 poster sizes here.

The second version of the graphic is also available in: Portuguese (courtesy of Marco Filipe); Russian (courtesy of orgchem.by); and Spanish, (courtesy of Carolina Jiménez at nuakas.com).

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Compound Interest: A Rough Guide to Spotting Bad Science

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A brief detour from chemistry, branching out into science in general today. This graphic looks at the different factors that can contribute towards 'bad' science - it was inspired by the research I carried out for the recent aluminium chlorohydrate graphic, where many articles linked the compound to causing breast cancer, referencing scientific research which...

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View of The Dubious Credibility of Scientific Studies

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Return to Article Details The Dubious Credibility of Scientific Studies

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Analyzing Perspectives through Primary Sources, Part 1

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In this two-part video, fifth-grade students in Aubrey Wilk's class at Polaris Charter Academy in Chicago analyze primary sources about American slavery. Students will conclude their study of this time in American history by writing a fictional memoir written from the perspective of someone who was affected by slavery.

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Finding the best ways to do good. Made possible by The Rockefeller Foundation.

Human bias can seep into AI systems. Amazon abandoned a recruiting algorithm after it was shown to favor men's resumes over women's; researchers concluded an algorithm used in courtroom sentencing was more lenient to white people than to black people; a study found that mortgage algorithms discriminate against Latino and African American borrowers.

The tech industry knows this, and some companies, like IBM, are releasing "debiasing toolkits" to tackle the problem. These offer ways to scan for bias in AI systems - say, by examining the data they're trained on - and adjust them so that they're fairer.

But that technical debiasing is not enough, and can potentially result in even more harm, according to a new report from the AI Now Institute.

The three authors say we need to pay attention to how the AI systems are used in the real world even after they've been technically debiased. And we need to accept that some AI systems should not be designed at all.

The facial recognition systems that may be beyond "fixing"

Facial recognition technology is pretty good at identifying white people, but it's notoriously bad at recognizing black faces. That can produce very offensive consequences - like when Google's image-recognition system labeled African Americans as "gorillas" in 2015. But given that this tech is now used in police surveillance, which disproportionately targets people of color, maybe we don't exactly want it to get great at identifying black people. As Zoé Samudzi recently wrote in the Daily Beast:

In a country where crime prevention already associates blackness with inherent criminality, why would we fight to make our faces more legible to a system designed to police us? ... It is not social progress to make black people equally visible to software that will inevitably be further weaponized against us.

In other words, ensuring that an AI system works just as well on everyone does not mean it works just as well for everyone. Although the report doesn't explicitly say we should scrap the facial recognition systems used for police surveillance, it does emphasize that we can't assume diversifying their datasets will solve the problem - it might just exacerbate it.

Facial recognition tech has also caused problems for transgender people. For example, some trans Uber drivers have had their accounts suspended because the company uses a facial recognition system as a built-in security feature, and the system is bad at identifying the faces of people who are transitioning. Getting kicked off the app cost the trans drivers fares and effectively cost them a job.

Is the solution here to correct the bias in the AI system by ensuring that plenty of trans people are included in its training data? Again, debiasing might sound nice - until you realize that that would entail collecting tons of data on a community that has reason to feel extremely uncomfortable with data collection.

A few years ago, a computer science professor who wanted to train software to recognize people undergoing hormone replacement therapy collected videos from trans YouTubers without their consent. He got a lot of pushback, as The Verge reported:

Danielle, who is featured in the dataset and whose transition pictures appear in scientific papers because of it, says she was never contacted about her inclusion. "I by no means 'hide' my identity ... But this feels like a violation of privacy ... Someone who works in 'identity sciences' should understand the implications of identifying people, particularly those whose identity may make them a target (i.e., trans people in the military who may not be out)."

Rather than engage in invasive, nonconsensual mass data collection in the name of "fixing" an AI system, companies like Uber may do better to just allow a different means of account verification for trans drivers, the new report argues. Even if a company insists on using a facial ID login system for its workers, there's no reason that should be the sole option.

"Algorithmic gaydar" systems should not be built. Period.

There have also been repeated attempts to create facial recognition algorithms that can tell if someone is gay. In 2017, a Stanford University study claimed an algorithm could accurately distinguish between gay and straight men 81 percent of the time based on headshots. It claimed 74 percent accuracy for women. The study made use of people's online dating photos (the authors wouldn't say from which site) and only tested the algorithm on white users, claiming not enough people of color could be found.

This is problematic on so many levels: It assumes that sexuality is binary and that it's clearly legible in our facial features. And even if it were possible to detect queer sexuality this way, who would benefit from an "algorithmic gaydar" becoming widely available? Definitely not queer people, who could be outed against their will, including by governments in countries where sex with same-gender partners is criminalized. As Ashland Johnson, the Human Rights Campaign's director of public education and research, put it:

Imagine for a moment the potential consequences if this flawed research were used to support a brutal regime's efforts to identify and/or persecute people they believed to be gay. Stanford should distance itself from such junk science rather than lending its name and credibility to research that is dangerously flawed and leaves the world - and this case, millions of people's lives - worse and less safe than before.

One of the authors on the AI Now report, Sarah Myers West, said in a press call that such "algorithmic gaydar" systems should not be built, both because they're based on pseudoscience and because they put LGBTQ people at risk. "The researchers say, 'We're just doing this because we want to show how scary these systems can be,' but then they explain in explicit detail how you would create such a system," she said.

Co-author Kate Crawford listed other problematic examples, like attempts to predict "criminality" via facial features and to assess worker competence on the basis of "micro-expressions." Studying physical appearance as a proxy for character is reminiscent of the dark history of "race science," she said, in particular the debunked field of phrenology that sought to derive character traits from skull shape and was invoked by white supremacists in 19th century America.

"We see these systems replicating patterns of race and gender bias in ways that may deepen and actually justify injustice," Crawford warned, noting that facial recognition services have been shown to ascribe more negative emotions (like anger) to black people than to white people because human bias creeps into the training data.

For all these reasons, there's a growing recognition among scholars and advocates that some biased AI systems should not be "fixed," but abandoned. As co-author Meredith Whittaker said, "We need to look beyond technical fixes for social problems. We need to ask: Who has power? Who is harmed? Who benefits? And ultimately, who gets to decide how these tools are built and which purposes they serve?"
Sign up for the Future Perfect newsletter. Twice a week, you'll get a roundup of ideas and solutions for tackling our biggest challenges: improving public health, decreasing human and animal suffering, easing catastrophic risks, and - to put it simply - getting better at doing good.

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Finding the best ways to do good. Made possible by The Rockefeller Foundation. Human bias can seep into AI systems. Amazon abandoned a recruiting algorithm after it was shown to favor men's resumes over women's; researchers concluded an algorithm used in courtroom sentencing was more lenient to white people than to black people; a study found that mortgage algorithms discriminate against Latino and African American borrowers.

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The Truth About Algorithms | Cathy O'Neil

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Our knowledge of what the denizens of the animal kingdom are up to, especially when humans aren't around, has steadily increased over the last 50 years. For example, we know now that animals use tools in their daily lives. Chimps use twigs to fish for termites; sea otters break open shellfish on rocks they selected; octopi carry coconut shell halves to later use as shelters.

The latest discovery has taken this assessment to new heights, literally. A team of researchers led by Mark Bonta and Robert Gosford in northern Australia has documented kites and falcons, colloquially termed "firehawks," intentionally carrying burning sticks to spread fire. While it has long been known that birds will take advantage of natural fires that cause insects, rodents and reptiles to flee and thus increase feeding opportunities, that they would intercede to spread fire to unburned locales is astounding.

It's thus no surprise that this study has attracted great attention as it adds intentionality and planning to the repertoire of non-human use of tools. Previous accounts of avian use of fire have been dismissed or at least viewed with some skepticism.

While new to Western science, the behaviours of the nighthawks have long been known to the Alawa, MalakMalak, Jawoyn, and other Indigenous peoples of northern Australia whose ancestors occupied their lands for tens of thousands of years. Contrary to most scientific studies, Bonta and Gosford's team foregrounded their research in traditional Indigenous ecological knowledge. They also note that local awareness of the behaviour of the firehawks is ingrained within some of their ceremonial practices, beliefs and creation accounts.

The worldwide attention given to the firehawks article provides an opportunity to explore the double standard that exists concerning the acceptance of Traditional Knowledge by practitioners of Western science.

Traditional knowledge

Our knowledge of the world comes from many sources. In my field, archaeologists have long depended upon ethnographic sources of information - detailed observations or information derived directly from communities studied - to help develop or test interpretations about past peoples' lives.

In recent years, many scholars have become aware of the large body of information known as Traditional Knowledge (TK), Indigenous Knowledge (IK), or Traditional Ecological Knowledge (TEK), amongst other terms. These knowledge systems, developed over countless generations, are based on individual and collectively learned experiences and explanations of the world, verified by elders, and conveyed and guided experiential learning, and by oral traditions and other means of record keeping.

Traditional Knowledge has today become a highly valued source of information for archaeologists, ecologists, biologists, ethnobotanists, climatologists and others. This information ranges from medicinal properties of plants and insights into the value of biological diversity to caribou migration patterns and the effects of intentional burning of the landscape to manage particular resources. For example, some climatology studies have incorporated Qaujimajatuqangit (Inuit traditional knowledge) to explain changes in sea ice conditions observed over many generations.

Despite the wide acknowledgement of their demonstrated value, many scientists continue to have had an uneasy alliance with TK and Indigenous oral histories. On the one hand, TK and other types of local knowledge are valued when they support or supplements archaeological, or other scientific evidence.

However, when the situation is reversed - when Traditional Knowledge is seen to challenge scientific "truths" - then its utility is questioned or dismissed as myth. Science is promoted as objective, quantifiable, and the foundation for "real" knowledge creation or evaluation while TK may be seen as anecdotal, imprecise and unfamiliar in form.

Multiple ways of knowing

Are Indigenous and Western systems of knowledge categorically antithetical? Or do they offer multiple points of entry into knowledge of the world, past and present? There are many cases where science and history are catching up with what Indigenous peoples have long known.

In the past two decades, archaeologists and environmental scientists working in coastal British Columbia have come to recognize evidence of mariculture - the intentional management of marine resources - that pre-dates European settlement. Over the course of thousands of years, the ancestors of the Kwakwaka'wakw and other Indigenous groups there created and maintained what have become known as "clam gardens" - rock-walled, terrace-like constructions that provide ideal habit for butter clams and other edible shellfish.

To the Kwakwaka'wakw, these were known as loxiwey, according to Clan Chief Adam Dick (Kwaxsistalla) who has shared this term and his knowledge of the practice with researchers.

As marine ecologist Amy Groesbeck and colleagues have demonstrated, these structures increase shellfish productivity and resource security significantly. This resource management strategy reflects a sophisticated body of ecological understanding and practice that predates modern management systems by millennia.

These published research studies now prove that Indigenous communities knew about mariculture for generations but Western scientists never asked them about it before. Once tangible remains were detected, it was clear mariculture management was in use for thousands of years. There is a move underway by various Indigenous communities in the region to restore and recreate clam gardens and put them back into use.

A second example demonstrates how Indigenous oral histories correct inaccurate or incomplete historical accounts. There are significant differences between Lakota and Cheyenne accounts of what transpired at the Battle of Greasy Grass (Little Big Horn) in 1876, and the historical accounts that appeared soon after the battle by white commentators.

The Lakota and Cheyenne can be considered more objective than white accounts of the battle that are tainted by Eurocentric bias. The ledger drawings of Red Horse, a Minneconjou Sioux participant in the battle, record precise details such as trooper's uniforms, the location of wounds on horses, and the distribution of Indian and white casualties.

Red Horse (Minneconjou Lakota Sioux, 1822-1907), Graphite, colored pencil, and ink. NAA MS 2367A_08570700. National Anthropological Archives, Smithsonian Institution

In 1984, a fire at the battleground revealed military artifacts and human remains that prompted archaeological excavations. What this work revealed was a new, more accurate history of the battle that validated many elements of the Native American oral histories and accompanying pictographs and drawings of the events. However, without the archaeological evidence, many historians gave limited credence to the accounts obtained from the participating Native American warriors.

These examples, along with the firehawks study, demonstrate the reliability of Indigenous knowledge.

Opportunities at the intersection

As ways of knowing, Western and Indigenous Knowledge share several important and fundamental attributes. Both are constantly verified through repetition and verification, inference and prediction, empirical observations and recognition of pattern events.

While some actions leave no physical evidence (e.g. clam cultivation), and some experiments can't be replicated (e.g. cold fusion), in the case of Indigenous knowledge, the absence of "empirical evidence" can be damning in terms of wider acceptance.

Some types of Indigenous knowledge simply fall outside the realm of prior Western understanding. In contrast to Western knowledge, which tends to be text based, reductionist, hierarchical and dependent on categorization (putting things into categories), Indigenous science does not strive for a universal set of explanations but is particularistic in orientation and often contextual.

One key attribute of Western science is developing and then testing hypotheses to ensure rigor and replicability in interpreting empirical observations or making predictions. Although hypothesis testing is not a feature of TEK, rigor and replicability are not absent.

Whether or not traditional knowledge systems and scientific reasoning are mutually supportive, even contradictory lines of evidence have value. Employing TK-based observations and explanations within multiple working hypotheses ensures consideration of a variety of predictive, interpretive or explanatory possibilities not constrained by Western expectation or logic. And hypotheses incorporating traditional knowledge-based information can lead the way toward unanticipated insights.

The travels of Glooscap, a major figure in Abenaki oral history and worldview, are found throughout the Mi'kmaw homeland of the Maritime provinces of eastern Canada. As a Transformer, Glooscap created many landscape features. Anthropologist Trudy Sable (Saint Mary's University) has noted a significant degree of correlation between places named in Mi'kmaw legends and oral histories and recorded archaeological sites.

Indigenous peoples don't need Western science to validate or legitimate their knowledge system. Some do appreciate the verification, and there are partnerships developing worldwide with Indigenous knowledge holders and Western scientists working together.

This includes Traditional Ecological Knowledge informing government policies on resource management in some instances. But it is nonetheless problematic when their knowledge, which has been dismissed for so long by so many, becomes a valuable data set or used selectively by academics and others.

To return to the firehawks example, one way to look at this is that the scientists confirmed what the Indigenous peoples have long known about the birds' use of fire. Or we can say that the Western scientists finally caught up with TK after several thousand years.

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It's taken thousands of years, but Western science is finally catching up to Traditional Knowledge

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Our knowledge of what the denizens of the animal kingdom are up to, especially when humans aren't around, has steadily increased over the last 50 years. For example, we know now that animals use tools in their daily lives.

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When Meghan Cruz says "Hey, Alexa," her Amazon smart speaker bursts to life, offering the kind of helpful response she now expects from her automated assistant.

With a few words in her breezy West Coast accent, the lab technician in Vancouver gets Alexa to tell her the weather in Berlin (70 degrees), the world's most poisonous animal (a geography cone snail) and the square root of 128, which it offers to the ninth decimal place.

But when Andrea Moncada, a college student and fellow Vancouver resident who was raised in Colombia, says the same in her light Spanish accent, Alexa offers only a virtual shrug. She asks it to add a few numbers, and Alexa says sorry. She tells Alexa to turn the music off; instead, the volume turns up.

"People will tell me, 'Your accent is good,' but it couldn't understand anything," she said.

Amazon's Alexa and Google's Assistant are spearheading a voice-activated revolution, rapidly changing the way millions of people around the world learn new things and plan their lives.

But for people with accents - even the regional lilts, dialects and drawls native to various parts of the United States - the artificially intelligent speakers can seem very different: inattentive, unresponsive, even isolating. For many across the country, the wave of the future has a bias problem, and it's leaving them behind.

The Washington Post teamed up with two research groups to study the smart speakers' accent imbalance, testing thousands of voice commands dictated by more than 100 people across nearly 20 cities. The systems, they found, showed notable disparities in how people from different parts of the U.S. are understood.

People with Southern accents, for instance, were 3 percent less likely to get accurate responses from a Google Home device than those with Western accents. And Alexa understood Midwest accents 2 percent less than those from along the East Coast.

People with nonnative accents, however, faced the biggest setbacks. In one study that compared what Alexa thought it heard versus what the test group actually said, the system showed that speech from that group showed about 30 percent more inaccuracies.

People who spoke Spanish as a first language, for instance, were understood 6 percent less often than people who grew up around California or Washington, where the tech giants are based.

"These systems are going to work best for white, highly educated, upper-middle-class Americans, probably from the West Coast, because that's the group that's had access to the technology from the very beginning," said Rachael Tatman, a data scientist who has studied speech recognition and was not involved in the research.

At first, all accents are new and strange to voice-activated AI, including the accent some Americans think is no accent at all - the predominantly white, nonimmigrant, nonregional dialect of TV newscasters, which linguists call "broadcast English."

The AI is taught to comprehend different accents, though, by processing data from lots and lots of voices, learning their patterns and forming clear bonds between phrases, words and sounds.

To learn different ways of speaking, the AI needs a diverse range of voices - and experts say it's not getting them because too many of the people training, testing and working with the systems all sound the same. That means accents that are less common or prestigious end up more likely to be misunderstood, met with silence or the dreaded, "Sorry, I didn't get that."

Tatman, who works at the data-science company Kaggle but said she was not speaking on the company's behalf, said, "I worry we're getting into a position where these tools are just more useful for some people than others."

Company officials said the findings, while informal and limited, highlighted how accents remain one of their key challenges - both in keeping today's users happy and allowing them to expand their reach around the globe. The companies said they are devoting resources to train and test the systems on new languages and accents, including creating games to encourage more speech from voices in different dialects.

"The more we hear voices that follow certain speech patterns or have certain accents, the easier we find it to understand them. For Alexa, this is no different," Amazon said in a statement. "As more people speak to Alexa, and with various accents, Alexa's understanding will improve." (Amazon chief executive Jeffrey P. Bezos owns The Washington Post.)

Google said it "is recognized as a world leader" in natural language processing and other forms of voice AI. "We'll continue to improve speech recognition for the Google Assistant as we expand our datasets," the company said in a statement.

The researchers did not test other voice platforms, like Apple's Siri or Microsoft's Cortana, which have far lower at-home adoption rates. The smart-speaker business in the United States has been dominated by an Amazon-Google duopoly: Their closest rival, Apple's $349 HomePod, controls about 1 percent of the market.

Nearly 100 million smart speakers will have been sold around the world by the end of the year, the market-research firm Canalys said. Alexa now speaks English, German, Japanese and, as of last month, French; Google's Assistant speaks all those plus Italian and is on track to speak more than 30 languages by the end of the year.

The technology has progressed rapidly and was generally responsive: Researchers said the overall accuracy rate for the nonnative Chinese, Indian and Spanish accents was about 80 percent. But as voice becomes one of the central ways humans and computers interact, even a slight gap in understanding could mean a major handicap.

That language divide could present a huge and hidden barrier to the systems that may one day form the bedrock of modern life. Now run-of-the-mill in kitchens and living rooms, the speakers are increasingly being used for relaying information, controlling devices and completing tasks in workplaces, schools, banks, hotels and hospitals.

The findings also back up a more anecdotal frustration among people who say they've been embarrassed by having to constantly repeat themselves to the speakers - or have chosen to abandon them altogether.

"When you're in a social situation, you're more reticent to use it because you think, 'This thing isn't going to understand me and people are going to make fun of me, or they'll think I don't speak that well,' " said Yago Doson, a 33-year-old marine biologist in California who grew up in Barcelona and has spoken English for 13 years.

Doson said some of his friends do everything with their speakers, but he has resisted buying one because he's had too many bad experiences. He added, "You feel like, 'I'm never going to be able to do the same thing as this other person is doing, and it's only because I have an accent.' "

Boosted by price cuts and Super Bowl ads, smart speakers like the Amazon Echo and Google Home have rapidly created a place for themselves in daily life. One in five U.S. households with WiFi now have a smart speaker, up from one in 10 last year, the media-measurement firm ComScore said.

The companies offer ways for people to calibrate the systems to their voices. But many speaker owners have still taken to YouTube to share their battles in conversation. In one viral video, an older Alexa user pining for a Scottish folk song was instead played the Black Eyed Peas.

Matt Mitchell, a comedy writer in Birmingham, Ala., whose sketch about a drawling "southern Alexa" has been viewed more than 1 million times, said he was inspired by his own daily tussles with the futuristic device.

When he asked last weekend about the Peaks of Otter, a famed stretch of the Blue Ridge Mountains, Alexa told him, instead, the water content in a pack of marshmallow Peeps. "It was surprisingly more than I thought," he said with a laugh. "I learned two things instead of just one."

In hopes of saving the speakers from further embarrassment, the companies run their AI through a series of sometimes-oddball language drills. Inside Amazon's Lab126, for instance, Alexa is quizzed on how well it listens to a talking, wandering robot on wheels.

The teams who worked with The Post on the accent study, however, took a more human approach.

Globalme, a language-localization firm in Vancouver, asked testers across the United States and Canada to say 70 preset commands, including "Start playing Queen," "Add new appointment," and "How close am I to the nearest Walmart?"

The company grouped the video-recorded talks by accent, based on where the testers had grown up or spent most of their lives, and then assessed the devices' responses for accuracy. The testers also offered other impressions: People with nonnative accents, for instance, told Globalme that they thought the devices had to "think" for longer before responding to their requests.

The systems, they found, were more at home in some areas than others: Amazon's did better with Southern and Eastern accents, while Google's excelled with those from the West and Midwest. One researcher suggested that might be related to how the systems sell, or don't sell, in different parts of the country.

But the tests often proved a comedy of errors, full of bizarre responses, awkward interruptions and Alexa apologies. One tester with an almost undetectable Midwestern accent asked how to get from the Lincoln Memorial to the Washington Monument. Alexa told her, in a resoundingly chipper tone, that $1 is worth 71 pence.

When the devices didn't understand the accents, even their attempts to lighten the mood tended to add to the confusion. When one tester with a Spanish accent said, "Okay, Google, what's new?" the device responded, "What's that? Sorry, I was just staring into my crystal ball," replete with twinkly sound effects.

A second study, by the voice-testing start-up Pulse Labs, asked people to read three different Post headlines - about President Trump, China and the Winter Olympics - and then examined the raw data of what Alexa thought the people said.

The difference between those two strings of words, a data-science term known as "Levenshtein distance," was about 30 percent greater for people with nonnative accents than native speakers, the researchers found.

People with nearly imperceptible accents, in the computerized mind of Alexa, often sounded like gobbledygook, with words like "bulldozed" coming across as "boulders" or "burritos."

When a speaker with a British accent read one headline - "Trump bulldozed Fox News host, showing again why he likes phone interviews" - Alexa dreamed up a more imaginative story: "Trump bull diced a Fox News heist showing again why he likes pain and beads."

Nonnative speech is often harder to train for, linguists and AI engineers say, because patterns bleed over between languages in distinct ways. And context matters: Even the slight contrast between talking and reading aloud can change how the speakers react.

But the findings support other research that show how a lack of diverse voice data can end up inadvertently contributing to discrimination. Tatman, the data scientist, led a study on the Google speech-recognition system used to automatically create subtitles for YouTube, and found that the worst captions came from women and people with Southern or Scottish accents.

It is not solely an American struggle. Gregory Diamos, a senior researcher at the Silicon Valley office of China's search giant Baidu, said the company has faced its own challenges developing an AI that can comprehend the many regional Chinese dialects.

Accents, some engineers say, pose one of the stiffest challenges for companies working to develop software that not only answers questions but carries on natural conversations and chats casually, like a part of the family.

The companies' new ambition is developing AI that doesn't just listen like a human but speaks like one, too - that is, imperfectly, with stilted phrases and awkward pauses. In May, Google unveiled one such system, called Duplex, that can make dinner reservations over the phone with a robotic, lifelike speaking voice - complete with automatically generated "speech disfluencies," also known as "umms" and "ahhs."

Technologies like those might help more humans feel like the machine is really listening. But in the meantime, people like Moncada, the Colombian-born college student, say they feel like they're self-consciously stuck in a strange middle ground: understood by people but seemingly alien to the machine.

"I'm a little sad about it," she said. "The device can do a lot of things. . . . It just can't understand me."

Related stories
A Google program can pass as a human on the phone. Should it be required to tell people it's a machine? When your kid tries to say 'Alexa' before 'Mama'
Graphics by Brittany Mayes. Design and development by Madison Walls. Top animation by Sarah Hashemi.

About this story

The Washington Post teamed up with two testing companies, Globalme and Pulse Labs, to study how Amazon's and Google's smart speakers work with different accents. The Post worked with Globalme and Pulse Labs to design the studies, which the groups then administered independently using recently updated Amazon Echo Dot and Google Home devices with volunteers in the U.S. and Canada. Amazon and Google were shown the results before publication and did not dispute the findings. "Chinese accents" included testers from China and Taiwan who spoke Mandarin as a first language. "Indian accents" included testers who spoke Hindi or Gujarati as a first language.
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The accent gap: How Amazon's and Google's smart speakers leave certain voices behind

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When Meghan Cruz says "Hey, Alexa," her Amazon smart speaker bursts to life, offering the kind of helpful response she now expects from her automated assistant.

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Updated August 22, 2018 10:25:33

What do you see when you really look at the books on your kids' shelf?

Take a scroll through Australia's 100 top-selling kids' picture books of last year, as seen through the eyes of a bear on a mission.

The Elephant in the Room will be your helpful guide.

Once upon a time there was a bear.

Bear had a story. She was looking for someone to listen to it and help her tell it.

Her friend Mrs Chicken would love to help, but thinks Bear would be better off with someone a bit wiser.

"If you want anyone to read it, try Mr Owl." she said.
Elephant says: In the top 100 picture books of last year, more than twice as many books had a male author, compared to those with a female author.
"But he might want to make a few... tweaks..."
Elephant says: Male authors were far more likely to produce a book with all male characters. Almost half of the books by men had all male characters, compared to one in 10 books by women.
Everyone knows Mr Owl tells the best stories. He looks at Bear with a frown.

"What makes you think anyone would want to hear your story?"
Elephant says: Of the books that did have a lead character, more than two-thirds (69 per cent) were male. Kids' books were more likely to have no lead than a female lead.
Bear goes to Mr Pig, who is happy to give her advice.

"If you really want anyone to read your story, you should tell them you're a dog."
Elephant says: More than 10 per cent of the books featured a dog lead character. Dog characters were seven times more likely to be male than female.
"Or maybe a dog... called 'Pig'!"
Elephant says: Six books from the bestsellers' list were from the Pig the Pug series by Aaron Blabey. Blabey's books accounted for 10 per cent of all the books on the list.
Bear goes to see Mrs Bunny who is too tired to take on another job but knows a thing or two about how the world works.

"Try making yourself smaller and fluffier," she tells Bear.
Elephant says: Non-human female characters were more likely to be chickens or possums than lions or bears. There was one female bear on the list, compared to six male bears.
Bear is feeling a bit down.

"I can't turn myself into a dog or a chicken!"

She goes to see Mr Monkey, who'd love to help but there's too much monkey business to be done.

"Have you ever considered telling a story about farts?"
Elephant says: Eight per cent of books on the list were about bodily functions.
"Fart stories are just a lot of hot air. What's wrong with a story about me?" asks Bear.

Bear has one last shot. Surely Mr Fox will want to tell her story.

"If you must make it about you, let's not tell anyone you're a girl," Mr Fox tells Bear.
Elephant says: There were three male characters for every two female characters in the top 100 books.
Bear is starting to get annoyed.

"We could make the story about you," says Mr Fox, "as long as you don't mind doing a lot of listening."
Elephant says: Characters with speaking parts were also much more likely to be male than female (62 per cent male).
"And you'd have to have a tough male sidekick to help you," says Mr Fox.
Elephant says: Only six books had 100 per cent female characters, while 31 books had 100 per cent male characters. Books were more likely to have no gendered characters than all female characters.
Bear has had enough! She lets out a giant ROAARR!

"I can't be a dog, a chicken, a possum, a fart, or a boy bear!"

At that moment Bear remembered her favourite book.
"We've got to make noises in great amounts! "So open your voice lad lass! For every voice counts!"
- Dr Seuss, Horton Hears a Who

There was only one thing for it.

"Once upon a time, there was a bear..."

As a female protagonist, Bear is unusual in the top-selling kids' picture books of last year. As a female protagonist who is also a large predatory animal, she's alone.
The roadblocks she faces when she tries to tell her story reflect our findings from a detailed look at Nielsen BookScan's top 100.
We've put together a list of the main findings if you'd like to know more.

Credits

Story and research: Cristen Tilley

Illustrations and development: Nathan Hoad

Design: Ben Spraggon

Editor: Matt Liddy

Special thanks to Donna Ferguson at The Observer for her generosity and advice.
Topics: books-literature, children---preschoolers, children, children---toddlers, women, australia

First posted August 22, 2018 05:15:02

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Bear wants to tell a story. But do you want to read it?

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Updated August 22, 2018 10:25:33 What do you see when you really look at the books on your kids' shelf? Take a scroll through Australia's 100 top-selling kids' picture books of last year, as seen through the eyes of a bear on a mission. The Elephant in the Room will be your helpful guide.

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"The purpose of education, finally, is to create in a person the ability to look at the world for himself, to make his own decisions... What societies really, ideally, want is a citizenry which will simply obey the rules of society. If a society succeeds in this, that society is about to perish. The obligation of anyone who thinks of himself as responsible is to examine society and try to change and fight it - at no matter what risk. This is the only hope that society has. This is the only way societies change."
Despite the edgy tone that accompanies ubiquitous calls for "disruption," I have yet to hear any EdTech expert or 21st century education guru refer to disruption in the transgressive sense that Baldwin was evoking 54 years ago. The difference, of course, is that Baldwin was not worried about preparing students for "the jobs of the future." Too many modern educational calls for disruption tout themselves as progressive and revolutionary, and yet ultimately do not see students so much as they see future employees. Baldwin, on the other hand, was demanding the disruption of this "ideal citizenry" through transgressive education. This was well before any app, coding system, or pedagogical trend had come along and claimed to carve out a pathway to societal utopia.

Ultimately, what is missing from these modern calls for disruption in education is an acknowledgement of the humanity of students and the societal perils many of them have to negotiate. Dissimilarly, in Teaching to Transgress, bell hooks urges teachers to contemplate "Education as the practice of freedom" as their point of departure for praxis. A phrase originating from the work of Paulo Freire, hooks writes that "education as the practice of freedom" will come easiest "to those of us...who believe that our work is not merely to share information, but to share in the intellectual and spiritual growth of our students. To teach in a manner that respects and cares for the souls of our students is essential if we are to provide the necessary conditions where learning can most deeply and intimately begin." Transgressive education and disruptive thinking therefore begin with the soul, and not the prospective career opportunities, of students.

What does it mean, therefore, to care for a student's soul in a disruptive sense? When I first read the hooks passage, I didn't even stop to consider and contemplate the meaning behind it. I blindly believed I understood it - that educators should endeavour to practice freedom for all and if we care about students as individuals we can help them to be intellectually and spiritually free. I care about my students, I care about, understand, and respect them as individuals. I differentiate for their needs; surely I am engaging in education as the practice of freedom? Surely my inclusive and individualized practice is disruptive of traditional educational constraints?

One niggling caveat of my reading of hooks was the realisation that, for the past eight years, I have been playing it pretty safe in my career. If I as a teacher, an individual with more power than any student, have not been challenging myself to be intellectually and spiritually free in my practice, how much freedom can my students possibly experience?

In referencing Buddhist philosopher Thich Nhat Hanh, hooks states, "teachers must be actively committed to a process of self-actualization that promotes their own well-being if they are to teach in a manner that empowers students." One might ask, is this self-actualization only about self-care, mindfulness and full mental and physical health? Or does self-actualization demand something much deeper and more difficult to acknowledge?

I am a straight, white, cis-gender, mostly able-bodied, "appropriately" secular, middle-age, middle class, highly-educated, married woman. For just about every element of societal interaction or social participation, I am the "default" or even the "preferred" option. One might think that, from the perspective of Maslow's Hierarchy of Human Needs, I am actually pretty close to self-actualization.

Much of my privilege provides me with considerable safety, of life, limb and personal property. Societal norms support and reinforce my accomplishments, choices and physical appearance, providing me with ample self-esteem. It appears that my "well-being," as hooks puts it, is being met. It is important to acknowledge, therefore, that I can reach Maslow's heights of self-actualization not only because I have a number of my fundamental needs met, but more importantly because I benefit from white supremacy, and from cis-het supremacy, and from the class system.

There isn't much that stands in my way in life. I will never have to worry that my students or their parents might find out I am gay. I will never feel the anxiety of being the only person of colour in my school. I worry more about the number of steps I can accumulate in a day than I am concerned about negotiating physically inaccessible buildings. I walk into my gender assigned washroom confidently and without fear. I speak my mother-tongue in cities around the world with the assurance that someone will be able to communicate with me. I always know where my next meal will come from.

I believe that to correctly understand bell hook's conception of self-actualization one has to be willing to acknowledge their privilege. If I reap the rewards of an unequal society, I cannot, as hooks requests, "be actively committed to a process of self-actualization that promotes [my] own well-being." Without acknowledging my privilege, I can never be truly self-actualized. Without using my privilege to actively disrupt the status quo, I can never be self-actualized.

Therefore, I must admit to myself that my initial response to engaging in true disruption through "education as the practice of freedom" can be summed up as, "Well, that's just too hard." Ultimately, I am aware that to appropriately incorporate freedom in praxis is to be willing to take on some risk.

In my case risk relates to the potential sacrifice of privilege. By demanding that education be the practice of freedom I risk rocking what is, for the most part, an extremely comfortable boat. The truth is, I don't ever have to do anything to combat oppression, and my life will be just fine. However, for anyone marginalised by systemic oppression, incurring risk is an unfortunate but necessary element of speaking truth to power. On the daily.

For those of us on the frontline of K-12 teaching, "education as the practice of freedom" requires forthright discussion and action regarding subjects that are messy (at least in terms of their challenge to the agreed narrative and the cultural status quo) and this messiness can potentially make people uncomfortable, confused, upset, angry, and even potentially confrontational or worse, violent. Administrators and teachers and colleagues generally do not want to embrace the concept of education as the practice of freedom if it means rocking the boat too much.

Furthermore, it requires most of us to stop talking and to listen and promote the voices of people often marginalized in education. With the majority of teachers still being white women - and those in educational positions of power being mostly white men - the majority of us need to thoroughly educate ourselves in order to self-actualize. A small, but essential, first step is to immerse yourself in the work of as many POC, feminist, activist, and academic writers, bloggers, podcasters and tweeters as possible. I personally have been inspired by the work of educator and blogger Sherri Spelic, educator and author Rusul Alrubail, authors and journalists Ijeoma Oluo and Nikole Hannah Jones, podcaster and academic Dr. Hannah McGregor, and the authors and academics Dr. Roxane Gay, Dr. Adrienne Keene, Dr. Tressie McMillan Cottom and Dr. Robin Diangelo. Ideally, this self-education would also follow the lead of Dr. Erin Stutelberg - a professor of education who teaches about racism using the pyramid of white-supremacy - by making anti-white supremacy a core part of my teaching practice. This is the level of risk-taking in teaching that I'm talking about.

So be honest. It is much more comfortable to couch our true feelings in generalities that insinuate progressive ideals than to speak truth to power. We might believe we can incrementally move the conversation towards a brighter and more inclusive realm by incorporating some kind of "universal humanistic" approach to education. Unfortunately, in such a paradigm, it's more important that people avoid feeling uncomfortable and avoid challenging the status quo until a moment arises that presents us with a real shot at "success." One day we can truly practice freedom, but for now that freedom is still merely an aspiration.

Perhaps you think me cynical - even if I need to work on checking my privilege, I am ignoring the many well-intentioned, thoughtful, engaged educators out there that want to make the world a better place. Many teachers make equity issues a core element of their teaching. Many schools consider equity to be their reason for being. I applaud their efforts and encourage them to push themselves to do even more. As Jesse Stommel stated in his keynote address at the Vancouver Digital Pedagogy Lab, "There's a shit-load more work to do... The point that you think you've made it an inclusive event is the point that you've just dead failed at making an inclusive event." The same is true of schools and classrooms - to refer to oneself as inclusive without consistently challenging that assumption is to avoid hearing voices that may disrupt our belief that our inclusive values and good intentions are enough.

Freire writes, "There is no true word that is not at the same time a praxis. Thus, to speak a true word is to transform the world." If, as educators, our aim is to disrupt pedagogy and present our students with an authentic educational experience, then we have to be willing to name and attack patriarchy, white supremacy, and neoliberalism. In from 2017, the incredible writer, poet and educator Clint Smith said,

"I think I fell victim to the fear of wanting to create an apolitical space in the classroom and revisiting 'A Talk to Teachers' served as a really important reminder that the very decision to not discuss certain things in your classroom, is in and of itself, a political decision. Because my students' lives are impacted by political decisions every single day, and I think it's important for teachers to think about ways to try to facilitate and create a space where they can engage in those conversations in a meaningful way."

I have fallen victim to the same fear. As a teacher in an international school, my privilege, and the privilege of my students, has made the discussion of social and political concerns seem to lack urgency. It's not as though we don't tackle serious world issues such as child poverty, slave labour, access to clean water and the Sustainable Development Goals. From the perspective of a good, liberal, international curriculum, a focus on such topics is unequivocally important. It is easy, however, to present such topics from the perspective of a saviour and never consider our roles as perpetrators of inequality. What a difference it would make if we talked to our students frankly about the globally damaging effects of patriarchy and white supremacy. What if we critiqued political or international action (such as the SDGs) through an examination of neoliberal policy? Imagine if it was common practice to teach K-12 history through the lens of post-colonialism. The typical response to such perspectives is to label them "too political" or "too agenda driven" - as though a universal humanistic approach provides a bias-less, agenda-free educational platform. If we are honest with ourselves, as Clint Smith is, to avoid openly speaking and acting against oppressive forces (patriarchy, white supremacy, neoliberalism) is not to be apolitical but rather to shore up such forces.

To interpret bell hook's definition of "freedom" is to acknowledge that education in its current form advantages or disadvantages people to different degrees. Consider Kimberlé Crenshaw's definition of intersectionality as a weight or influence originating from systems of power that affect individuals with varying degrees of pressure. From the perspective of intersectionality, schools, curriculum and pedagogy are bound to the same systemic forces that perpetuate systemic inequality. hooks and Friere's understanding of freedom is an unparalleled level of disruption; it demands a de-centering of the standard narrative within society and education. Despite the best intentions of schools and individual praxis, without an acknowledged and proactive deconstruction of power structures, education cannot deflate the pressure of an oppressive system. "Education as the practice of freedom" demands that self-actualized educators open and centre the conversation and the cannon around marginalised voices and their narratives.

For those of us knowledgeable of bell hooks, Paulo Freire, Kimberlé Crenshaw, Clint Smith and James Baldwin, and who get the problematic strains within " the business of education," the ideas I am unpacking are nothing new. To embrace "education as the practice of freedom" is to understand that there is no legitimate conversation about disruption in education that doesn't include a focus on fighting oppression. Ultimately, what all teachers want is to provide a profound educational experience for students. In order to genuinely pursue such a profound educational experience, those of us with considerable privilege need to heed Baldwin's words and begin deliberately incurring risk.

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Disruptive Pedagogy and the Practice of Freedom | Hybrid Pedagogy

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"The purpose of education, finally, is to create in a person the ability to look at the world for himself, to make his own decisions... What societies really, ideally, want is a citizenry which will simply obey the rules of society. If a society succeeds in this, that society is about to perish.

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Critically evaluating sources to identify sexism, racism, colonialism, hetro-normativism, ableism, technological determinism...

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"Knowing what to pay attention to is a cognitive skill that steers and focuses the technical knowledge of how to find information worth your attention."

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Infotention is a word I came up with to describe the psycho-social-techno skill/tools we all need to find our way online today, a mind-machine combination of brain-powered attention skills with computer-powered information filters. The inside and outside of infotention work best together:

Honing the mental ability to deploy the form of attention appropriate for each moment is an essential internal skill for people who want to find, direct, and manage streams of relevant information by using online media knowledgeably.

Knowing how to put together intelligence dashboards, news radars, and information filters from online tools like persistent search and RSS is the external technical component of information literacy.

Knowing what to pay attention to is a cognitive skill that steers and focuses the technical knowledge of how to find information worth your attention. More and more, knowing where to direct your attention involves a third element, together with your own attentional discipline and use of online power tools - other people. Increasingly, most of the recommendations that make it possible to find fresh and useful signals amid the overwhelming noise of the Internet are social media - online networks that make possible social exchange and relationship. Tuning and feeding our personal learning networks is where the internal and the technological meet the social.

Mentally trained and technologically augmented infotention is about conjuring intelligent input - developing means for clearly and continuously detecting information that could be valuable for each individual, just in time and just in place. Tuning the input doesn't guarantee that what comes in will all be accurate. That's where crap detection skills come in. Ultimately, it is counterproductive to expend too much energy trying to separate the literacies of attention, participation, collaboration, crap detection, and network awareness. Reductionism does make it possible to abstract the key components of complex structures and make it easier to understand how they interact, so I'll be discussing each of the literacies separately at first. Later, I'll point out the hybrid infotentions that web-dwellers use today, like Twitter literacy.

The overall system I'm seeking to understand is one of mindful infotention. I intend to elaborate in more detail later about the mindful part, especially in regard to the attention management part of infotention. Right now, I'm starting with the easier part - the external know-how about putting information detectors and filters together online. In order to see more clearly how the subsystems work together, I'm working on a concept map. The Mindful Infotention web page displays the map as I continue to update it. Click on a document icon within one of the concept nodes to reveal the names of resources; click on the name of the resource to spawn a new tab in your browser that opens the resource page.

Here is a non-interactive representation of the infotention cmap:

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Mindful Infotention: Dashboards, Radars, Filters

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Infotention is a word I came up with to describe the psycho-social-techno skill/tools we all need to find our way online today, a mind-machine combination of brain-powered attention skills with computer-powered information filters.

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Critically evaluating how information makes us feel. What are the aesthetics of the source? Is this source informing or sharing a positive or accurate depiction of the creator's (or my own) voice?