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February 2, 2021

Race, Memes and Surveillance: Apryl Williams in conversation with Allissa Richardson

Apryl Williams (@aprylw) is talking with Allissa Richardson about “Surveillance and Black Digital Publics”  at a Harvard Berkman Klein Center event. Here’s a paper by Apryl on the topic.

I am live blogging, and thus making so many mistakes you could plotz. Mistakes of every sort: Missing points. Getting points wrong. Paraphrasing everything, and doing so in ways that don’t match the person’s content or tone. Day dreaming for a moment and missing an entire idea. Making articulate people sound choppy in my retelling. OMG, it’s just a mess.

Allissa begins by playing some Karen memes from The View, [Here are some others – dw] and asks whether “laughter is the best medicine,” as someone on The View concluded. She raises a case, one of thousands, in which a white person’s words were taken over those of Black people. “Those days haven’t really ended.”

Apryl says there’s a long history of Black people nicknaming white people who call the cops for offenses that wouldn’t be offenses if done by white people. Before “Karen” there was “Becky.”

AR: A lot of what we’re seeing isn’t meant to be funny. There’s underlying rage.

AW: Yes, there is. It’s easier to laugh if you have some distance from it. But for the people it’s happening to, it’s horrifying. If it happened to me, I’d feel lucky to walk away alive from an encounter with the police. There’s always a possibility for a Black person that she won’t. But there’s also the idea that humor helps us cope with trauma, especially as a collective. There’s lots of research that shows that humor can help us cope with physical and emotional pain. I like to think of these memes as a collective release of stress. The memes also act as a stand-in for media reporting where otherwise there wouldn’t be any.

AW: There were memes that preexisted the Internet – coded images that have a lot of intertextuality. To decode a meme you have to be embedded in that culture.

AR: In one of the memes we just saw, you’d have to know about Wakanda.

AW: Definitely.

AR: You’ve been curating Karen memes this year. Any favorites?

AW: Shout out to [Ack. I didn’t catch the name. Sorry about that! – dw ] who helped. We have about 60,000 memes that comment on around 15 incidents. My favorite is Barbecue Becky, a woman in Oakland who called the police because a Black group was using a grill in a park. Becky was “worried” that they wouldn’t dispose of the cinders safely. In the 911 call, she is very clear that it’s a Black family, but when the dispatcher asks her own color, she doesn’t want that to be part of the conversation.

AR: That incident spawned a meme within a meme.

AW: Yes, in celebration of this resistance, the people of Oakland had a BBQ in the same spot on the anniversary the next year. And maybe the year after that.

AW: It’s very interesting that as Oakland becomes gentrified, it’s white women who want to assert themselves. Black women experience Karens everyday, which is why these memes are so important., Why can’t white women just mind their own business? Because surveilling and policing Black people is their business, and that’s the problem. They Were Her Property by Stephanie Jones-Rogers is about the myth that white women did not take part in slavery, that they didn’t own slaves. But that myth is false. Women benefited from slavery, and white people benefited from maintaining the power differential that said that Black people are “less than.” A lot of those same ideologies underlay the Karen practices: the idea that white people are superior and there should be some natural order, or Black people are born bad and deserved to be patrolled. White people are socialized via the media that Black people are dangerous, giving rise to the idea that Black bodies are a threat. White women are compelled to perform this racial fear.

AR: “Perform” harks back to Amy Cooper [who thought she was in danger from a Black bird watcher]. Are memes meant to just point something out, to punish, to organize dialogue…?

AW: Everyone who’s Black has their own way of being Black. [I missed who Apryl was talking about] took it so far, but I’m here to take it all the way. We can see that Amy Cooper was performing this danger, saying that this Black man is threatening me. A lot of the fear white women have of Black men goes back to slavery and racial perceptions of Black men as animals. We owe a big debt to the people who are creating these memes. because they’re helping us have this dialogue about “casual” racism, which isn’t really casual. And they’re often calling for restitution. Often with a meme people are saying that the person should be fired. Amy Cooper did lose her job. Permit Patty, the CEO of a cannabis company had to resign after calling the police on a Black child who was selling water bottles “without a permit”.

AR: Have you seen Asian or Latinx women engage in this type of behavior?

AW: Yes, it’s not just white women. Some people who are “white adjacent”, as I like to call them, engage in this performance because it displays power. And it reinforces the racist idea that Black women are the lowest on the social ladder.

AR: People see a meme and wonder if maybe they’re a Karen…

AW: That’s good. You should stop and ask yourself why you even think that might be the case.

AR: Where do you intellectually anchor your work in surveillance scholarship?

AW: Simone Brown did groundbreaking work on blackness and surveillance, including at the state level. Her work focuses on blackness as a key identifier and a marker of difference … a reason to surveil. And there are studies in which the tech fails because it wasn’t designed to work with Black bodies. The truth shows up in the glitches, as Ruha Benjamin says.. And even though Michel Foucault is not without problems, his work about the surveillance society resonates with me: the Panopticon. When you’re surveilled so heavily, you start to behave as if you’re always surveilled. If Black people are always thinking they’re being watched and are concerned about their safety, that means we’re never free.

A: There’s a somatic concern in how our bodies take in that stress, of doing ordinary things and being punished for them. How about Bell Hooks‘ work?

AW: [I missed a sentence or maybe two.] Memes serve as a counter-surveillance. The memes hold up a mirror, saying just as you are patrolling us, we’re patrolling you. We won’t allow you to harass and terrorize our neighborhoods. What you’re doing isn’t just casual racism. It’s harmful and should be punished. “Your’e the one wasting tax payer money because you won’t say what you’re wearing or what your race is and you’re in a park full of people.”

AR: There were women on the frontlines in the Capitol invasion. Can you talk about how white supremacy has been upheld by women?

AW: White women were very much complicit in it. Jennifer Pierce’s Racing for Innocence is not just about women standing by their men, but women upholding the idea of the patriarchy. Jesse Daniels writes that white women are invested in the patriarchy because it supports them. In American society, women represent mythological ideologies about motherhood and nurturing. In the Capitol we saw that their ethos is that when a wrong is committed they feel the need to step in, even though in this case there was no wrong. The white women felt they had to uphold those values, and white entitlement made them feel comfortable doing it and feeling that they’d get away with it.

AR: A lot of this goes back to memes expressing rage in a humorous way even though there can be real danger.

AW: There’s a Caution Against Racially Exploited Non-emergencies (CAREN Act) proposal in California that would make it a crime to make Karen calls to the police. It passed in Oregon. I spoke with the woman who got it passed there and she said she was able to do it because she did it quietly. She herself had been Karen’ed.

Q&A

Q: Do these memes do more harm than good by trivializing the behavior?

A: They raise awareness.

Q: As James Baldwin says, isn’t this a white person’s problem?

A: Yes. Racism is a white invention. But it impacts us. For us it’s a fight for liberty and sometimes a fight for life. We need white people to take up their burden.

AR: What do you say to people who say that “Karen” is a slur?

AW: Black people are not trying to say that all people named Karen are bad. For me it’s just shorthand for white entitlement. If white people take it out of context, that is a white people problem, too.

Q: Foucault’s panopticon reminds me of the time that the police came because someone reported a Black person on the premises. I’m in a wheelchair, and am often invisible. Can you talk about how we can take all of this into a space of peace? [I’ve done a particularly bad job capturing this. Sorry. – dw]

AW: Black history has to be about moving forward as well as remembering the history of oppression. Black people thrive even as white people try to limit our agency and our joy.

Q: Can you talk about the white mainstreaming and coopting of memes started by Black people? And the use of Black audio by white teens on TikTok?

AW: Let’s talk about cultural appropriation and exploitation. I’ve been thinking all year about responsible ways of studying TikTok. Black creators are making all of these great sounds and memes, but often get no credit. It’s digital blackface: people performing blackness because it’s cool, but they wouldn’t want to be Black because being Black is hard.

Q: We often don’t give the LGTBTQ and ballroom culture credit for all they’ve given us that we use online.

AW: Much of that was started by queer Black people. But we should always remember to give credit and give back to those communities. And the idea that people should be respected no matter what they look like benefits everyone, not just people in those bodies.

Q: What can be done by tech policy and practice to help activists fight back?

AW: There are so many examples of activists fighting back. E.g., people painting their faces so they can’t be identified from images of them at protests. Black technologists are calling for that same tech to be used on the Capitol insurgents; if it can be used on peaceful BLM protestors, it can and should be used on the insurgents.

Q: Are there failed memes because they pushed the envelope too far?

AW: I’m sure there are failed memes. Some are maybe too scary to be funny. And when events happen too close together, one meme can overshadow others.

What should be the role of Asian-Americans, especially in the development of surveillance tech?

I appreciate this question a lot. The myth of the model minority is really damaging; whites think of Asian-Americans as the “good” minority. There’s already a lot of research showing the racism of Asian communities. Everyone working on tech needs to take a step back and question what their project is doing.

Apryl says she has been working on the role of race in online dating, and a book should be coming out soon.

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Categories: culture, justice, liveblog, race Tagged with: culture • justice • memes • racism Date: February 2nd, 2021 dw

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December 31, 2019

Early animation

Here are links to the earliest cartoons in Riochard Brody’s excellent article, “Draw Stars,” in the Dec. 30, 2019 New Yorker. (Note: Racist and other stereotypes below.)


Emile Cole, Fantasmagorie, 1908, restored. (Original)

Winsor McCay, Little Nemo, 1911:

McCay, Gertie the Dinosaur, 1914:


Max and Dave Fleischer, Out of the Inkwell: The Tantalizing Fly, 1919 (remastered):

The Fleischers, Jumping Beans, 1922 (remastered):

Wallace Carlson (Bray Studios), How Cartoons Are Made, 1919:

Wallace Carlson, He resolves not to smoke, 1914:

Gregory La Cava, The Breath of a Nation, 1919:


Joseph Sunn claymation: Green Pastures, 1919:


Wallace McCutcheon’s merging of Green Pastures with live action, in The Sculptor’s Nightmare:


Howard S. Moss stop action, Mary & Gretel, part 1, 1916:


Mary & Gretel, part 2:


Walter Ruttmann’s abstract Opus 1, 1921:


Lette Reiniger’s silhouette Cinderella, 1921:


Bryant Fryer’s silhouette Follow the Swallow, 1927:

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Categories: culture, free culture, liveblog Tagged with: animation • culture • history • movies Date: December 31st, 2019 dw

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May 16, 2018

[liveblog] Aubrey de Grey

I’m at the CUBE Tech conference in Berlin. (I’m going to give a first keynote on the book I’m finishing.) Aubrey de Grey begins his keynote begins by changing the question from “Who wants to get old?” to “Who wants Alzheimers?” because we’ve been brainwashed into thinking that aging is somehow good for us: we get wiser, get to retire, etc. Now we are developing treatments for aging. Ambiguity about aging is now “hugely damaging” because it hinders the support of research. E.g., his SENS Research Foundation is going too slowly because of funding restraints.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

“The defeat of aging via medicine is foreseseeable now.” He says he has to be credible because people have been saying this forever and have been wrong.

“Why is aging still a problem?” One hundred years ago, a third of babies would die before they were one year old. We fixed this in the industrialized world through simple advances, e.g., hygiene, mosquito, antibiotics. So why are diseases of old age so much harder to control? People think it’s because so many things go wrong with us late in life, interacting with one another and creating incredible complexity. But that’s not the main answer.

“Aging is easy to define: it is a side effect of being alive.” “It’s a fact of the operation of the human body generates damage.” It accumulates. The body tolerates a certain amount. When you pass that amount, you get pathologies of old age. Our approach has been to develop geriatric medicine to counteract those pathologies. That’s where most of the research goes.

aubrey de gray metabolism diagram

“Metabolism: The ultimate undocumented spaghetti code”

But that won’t work because the damage continues. Geriatric medicine bangs away at the pathologies, but will necessarily become less effective over time. “We make this mistake because of a misclassification we make.”

If you ask people to make categories of disease, they’ll come up with communicable, congenital, and chronic. Then most people add a fourth way of being sick: aging itself. It includes fraility, sarcopenia (loss of muscle), immunosenesence (aging of the immune system)…But that’s silly. Aging in a living organism is the same as aging in a machine. “Aging is the accumulation of damage that occurs as a side-effect of the body’s normal operation.”It is the accumulation of damage to the body that occurs as an intrinsic side-effect of the body’s normal operation. That means the categories are right, except aging covers column 3 and 4. Column 3 — specific diseases such as alzheimer’s and cancer — is also part aging. This means that aging isn’t a blessing in surprise, and that we can’t say that column 3 are high-priorities of medicine but those in 4 are not.

A hundred years ago a few people started to think about this and realized that if we tried to interfere with the process of aging earlier one, we’d do better. This became the field of gerontology. Some species age much more slowly than others. Maybe we can figure out the basis for that variation. But the metabolism is really really complicated. “This is the ultimate nightmare of uncommented spaghetti code.” We know so little about how the body works.

“There is another approach. And it’s completely bleeding obvious”: Periodically repair the damage. We don’t need to slow down the rate at which metabolism causes damage. We need to engineer a system we don’t understand. But “we don’t need to understand how metabolism causes damag”we don’t need to understand how metabolism causes damage. Nor do we need to know what to do when the damage is too great, because we’re not going to let it get to that state. We do this with, say, antique cars. Preventitive maintenance works. “The only question is, can we do it for a much more complicated machine like the human body?

“We’re sidestepping our ignorance of metabolism and pathology. But we have to cope with the fact that damage is complicated” All of the types of damage, from cell loss toe extracellular matrix stiffening — there are 7 categories — can be repaired through a single approach: genetic repair. E.g., loss of cells can be repaired by replacing them using stem cells. Unfortunately, most of the funding is going only to this first category. SENS was created to enable research on the other seven. Aubrey talks about SENS’ work on protecting cells from the bad effects of cholesterol.

He points to another group (unnamed) that has reinvented this approach and is getting a lot of notice.

He says longevity is not what people think it is. These therapies will let people stay alive longer, but they will also stay youthful longer. “”Longevity is a side effect of health.” ”“Longevity is a side effect of health.”

Will this be only for the rich? Overpopulation? Boredom? Pensions collapse? We’re taking care of overpopulation by cleaning up its effects, he says. He says there are solutions to these problems. But there are choices we have to make. No one wants to get Alzheimers. We can’t have it both ways. Either we want to keep people healthy or not.

He says SENS has been successful enough that they’ve been able to spin out some of the research into commercial operations. But we need to cary on in the non-profit research world as well. Project 21 aims at human rejuvenation clinical trials.

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Categories: culture, liveblog Tagged with: 2b2k • aging Date: May 16th, 2018 dw

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May 6, 2018

[liveblog][ai] Primavera De Filippi: An autonomous flower that merges AI and Blockchain

Primavera De Filippi is an expert in blockchain-based tech. She is giving a ThursdAI talk on Plantoid, an event held by Harvard’s Berkman Klein Center for Internet & Society and the MIT Media Lab. Her talk is officially on operational autonomy vs. decisional autonomy, but it’s really about how weird things become when you build a computerized flower that merges AI and the blockchain. For me, a central question of her talk was: Can we have autonomous robots that have legal rights and can own and spend assets, without having to resort to conferring personhood on them the way we have with corporations?

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

Autonomy and liability

She begins by pointing to the 3 industrial revolutions so far: Steam led to mechanized production ; Electricity led to mass production; Electronics led to automated production. The fourth — AI — is automating knowledge production.

People are increasingly moving into the digital world, and digital systems are moving back into the physical worlds, creating cyber-physical systems. E.g., the Internet of Things senses, communicates, and acts. The Internet of Smart Things learns from the data the things collect, makes inferences, and then acts. The Internet of Autonomous Things creates new legal challenges. Various actors can be held liable: manufacturer, software developer, user, and a third party. “When do we apply legal personhood to non-humans?”

With autonomous things, the user and third parties become less liable as the software developer takes on more of the liability: There can be a bug. Someone can hack into it. The rules that make inferences are inaccurate. Or a bad moral choice has led the car into an accident.

The sw developer might have created bug-free sw but its interaction with other devices might lead to unpredictability; multiple systems operating according to different rules might be incompatible; it can be hard to identify the chain of causality. So, who will be liable? The manufacturers and owners are likely to have only limited liability.

So, maybe we’ll need generalized insurance: mandatory insurance that potentially harmful devices need to subscribe to.

Or, perhaps we will provide some form of legal personhood to machines so the manufacturers can be sued for their failings. Suing a robot would be like suing a corporation. The devices would be able to own property and assets. The EU is thinking about creating this type of agenthood for AI systems. This is obviously controversial. At least a corporation has people associated with it, while the device is just a device, Primavera points out.

So, when do we apply legal personhood to non-humans? In addition to people and corporations, some countries have assigned personhood to chimpanzees (Argentina, France) and to natural resources (NZ: Whanganui river). We do this so these entities will have rights and cannot be simply exploited.

If we give legal personhood to AI-based systems, can AI have property rights over their assets and IP? If they are legally liable, they can be held responsible for their actions, and can be sued for compensation? “Maybe they should have contractual rights so they can enter into contracts. Can they be rewarded for their work? Taxed?”Maybe they should have contractual rights so they can enter into contracts. Can they be rewarded for their work? Taxed? [All of these are going to turn out to be real questions. … Wait for it …]

Limitations: “Most of the AI-based systems deployed today are more akin to slaves than corporations.” They’re not autonomous the way people are. They are owned, controlled and maintained by people or corporations. They act as agents for their operators. They have no technical means to own or transfer assets. (Primavera recommends watching the Star Trek: The Next Generation episode “The Measure of the Man” that asks, among other things, whether Data (the android) can be dismantled and whether he can resign.)

Decisional autonomy is the capacity to make a decision on your own, but it doesn’t necessarily bring what we think of as real autonomy. E.g., an AV can decide its route. For real autonomy we need operational autonomy: no one is maintaining the thing’s operation at a technical level. To take a non-random example, a blockchain runs autonomously because there is no single operator controlling. E.g., smart contracts come with a guarantee of execution. Once a contract is registered with a blockchain, no operator can stop it. This is operational autonomy.

Blockchain meets AI. Object: Autonomy

We are getting first example of autonomous devices using blockchain. The most famous is the Samsung washing machine that can detect when the soap is empty, and makes a smart contract to order more. Autonomous cars could work with the same model; they could not be owned by anyone and collect money when someone uses them. These could be initially purchased by someone and then buy themselves off: “They’d have to be emancipated,” she says. Perhaps they and other robots can use the capital they accumulate to hire people to work for them. [Pretty interesting model for an Uber.]

She introduces Plantoid, a blockchain-based life form. “Plantoid is autonomous, self-sufficient, and can reproduce.”It’s autonomous, self-sufficient, and can reproduce. Real flowers use bees to reproduce. Plantoids use humans to collect capital for their reproduction. Their bodies are mechanical. Their spirit is an Ethereum smart contract. It collects cryptocurrency. When you feed it currency it says thank you; the Plantoid Primavera has brought, nods its flower. When it gets enough funds to reproduce itself, it triggers a smart contract that activates a call for bids to create the next version of the Plantoid. In the “mating phase” it looks for a human to create the new version. People vote with micro-donations. Then it identifies a winner and hires that human to create the new one.

There are many Plantoids in the world. Each has its own “DNA”. New artists can add to it. E.g., each artist has to decide on its governance, such as whether it will donate some funds to charity. The aim is to make it more attractive to be contributed to. The most fit get the most money and reproduces themselves. BurningMan this summer is going to feature this.

Every time one reproduces, a small cut is given to the pattern that generated it, and some to the new designer. This flips copyright on its head: the artist has an incentive to make her design more visible and accessible and attractive.

So, why provide legal personhood to autonomous devices? We want them to be able to own their own assets, to assume contractual rights, and legal capacity so they can sue and be sued, and limit their liability. “ Blockchain lets us do that without having to declare the robot to be a legal person.” Blockchain lets us do that without having to declare the robot to be a legal person.

The plant effectively owns the cryptofunds. The law cannot affect this. Smart contracts are enforced by code

Who are the parties to the contract? The original author and new artist? The master agreement? Who can sue who in case of a breach? We don’t know how to answer these questions yet.

Can a plantoid sure for breach of contract? Not if the legal system doesn’t recognize them as legal persons. So who is liable if the plant hurts someone? Can we provide a mechanism for this without conferring personhood? “How do you enforce the law against autonomous agents that cannot be stopped and whose property cannot be seized?”

Q&A

Could you do this with live plants? People would bioengineer them…

A: Yes. Plantoid has already been forked this way. There’s an idea for a forest offering trees to be cut down, with the compensation going to the forest which might eventually buy more land to expand itself.

My interest in this grew out of my interest in decentralized organizations. This enables a project to be an entity that assumes liability for its actions, and to reproduce itself.

Q: [me] Do you own this plantoid?

A: Hmm. I own the physical instantiation but not the code or the smart contract. If this one broke, I could make a new one that connects to the same smart contract. If someone gets hurt because it falls on the, I’m probably liable. If the smart contract is funding terrorism, I’m not the owner of that contract. The physical object is doing nothing but reacting to donations.

Q: But the aim of its reactions is to attract more money…

A: It will be up to the judge.

Q: What are the most likely senarios for the development of these weird objects?

A: A blockchain can provide the interface for humans interacting with each other without needing a legal entity, such as Uber, to centralize control. But you need people to decide to do this. The question is how these entities change the structure of the organization.

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Categories: ai, law, liveblog, philosophy Tagged with: ai • blockchain • law • robots Date: May 6th, 2018 dw

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April 5, 2018

[liveblog] Neil Gaikwad Human-AI Collaboration for Sustainable Market Design

I’m at a ThursdAI talk (Harvard’s Berkman Klein Center for Internet & Society and MIT Media Lab) being given by Neil Gaikwad (Twitter: @neilthemathguy, a Ph.D. at the MediaLab, in the Space Enabled Group.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.

Markets and institutions are parts of complex ecosystem, Neil says. His research looks at data from satellites that show how the Earth is changing: crops, water, etc. Once you’ve gathered the data, you can use machine learning to visualize the changes. There are ecosystems, including of human behavior, that are affected by this. It affects markets and institutions. E.g., a drought may require an institutional response, and affect markets.

Traditional markets, financial markets, and gig economies all share characteristics. Farmers markets are complex ecosystems of people with differing information and different amounts of it, i.e. asymmetric info. Same for financial markets. Same for gig economies.

Indian markets have been failing; there have been 300,000 suicides in the last 30 years. Stock markets have crashed suddenly due to blackbox marketing; in some cases we still don’t know why. And London has banned Uber. So, it doesn’t matter which markets or institutions we look at, they’re losing our trust.

An article in New Scientist asked what we can do to regain this trust. For black box AI, there are questions of fairness and equity. But what would human-machine collaboration be like? Are there design principles for markets.?

Neil stops for us to discuss.

Q: How do you define the justice?

A: Good question. Fairness? Freedom? The designer has a choice about how to define it.

Q: A UN project created an IT platform that put together farmers and direct consumers. The pricing seemed fairer to both parties. So, maybe avoid intermediaries, as a design principle?

Neil continues. So, what is the concept of justice here?

1. Rawls and Kant: Transcendental institutionalism. It’s deontological: follow a principle for perfect justice. Use those principles to define a perfect institution. The properties are defined by a social contract. But it doesn’t work, as in the examples we just saw. What is missing. People and society. [I.e., you run the institution according to principles, but that doesn’t guarantee that the outcome will be fair and just. My example: Early Web enthusiasts like me thought the Web was an institution built on openness, equality, creative anarchy, etc., yet that obviously doesn’t ensure that the outcome will share those properties.]

2. Realized-focused institutionalism (Sen
2009): How to reverse this trend. It is consequentialist: what will be the consequences of the design of an institution. It’s a comparative assessment of different forms of institutions. Instead of asking for the perfectly justice society, Sen asks how justice can be advanced. The most critical tool for evaluating any institution is to look at how it actually realizes how people’s lives change.

Sen argues that principles are important. They can be expressed by “niti,” Sanskrit for rules and institutions. But you also need nyaya: a form of social arrangement that makes sure that those rules are obeyed. These rules come from social choice, not social contract.

Example: Gig economies. The data comes from mechanical turk, upwork, crowdflower, etc. This creates employment for many people, but it’s tough. E.g., identifying images. Use supervised learning for this. The Turkers, etc., do the labelling to train the image recognition system. The Turkers make almost no money at this. This is the wicked problem of market design: The worker can have identifications rejected, sometimes with demeaning comments.

“The Market for Lemons” (Akerlog, et al., 1970): all the cars started to look alike and now all gig-workers look alike to those who hire them: there’s no value given to bringing one’s value to the labor.

So, who owns the data? Who has a stake in the models? In the intellectual property?

If you’re a gig worker, you’re working with strangers. You don’t know the reputation of the person giving me data. Or renting me the Airbnb apartment. So, let’s put a rule: reputation is the backbone. In sharing economies, most of the ratings are the highest. Reputation inflation. So, can we trust reputation? This happens because people have no incentive to rate. There’s social pressure to give a positive rating.

So, thinking about Sen, can we think about an incentive for honest reputation? Neil’s group has been thinking about a system [I thought he said Boomerang, but I can’t find that]. It looks at the workers’ incentives. It looks at the workers’ ratings of each other. If you’re a requester, you’ll see the workers you like first.

Does this help AI design?

MoralMachine has had 1.3M voters and 18M pairwise comparisons (i.e., people deciding to go straight or right). Can this be used as a voting based system for ethical decision making (AAAI 2018)? You collect the pairwise preferences, learn the model of preference, come to a collective preference, and have voting rules for collective decision.

Q: Aren’t you collect preferences, not normative judgments? The data says people would rather kill fat people than skinny ones.

A: You need the social behavior but also rules. For this you have to bring people into the loop.

Q: How do we differentiate between what we say we want and what we really want?

A: There are techniques, such as “Bayesian Truth Serum”nomics.mit.edu/files/1966”>Bayesian Truth Serum.

Conclusion: The success of markets, institutions or algorithms, is highly dependent on how this actually affects people’s lives. This thinking should be central to the design and engineering of socio-technical systems.

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Categories: ai, liveblog Tagged with: fairness • justice • machine learning Date: April 5th, 2018 dw

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December 17, 2017

[liveblog] Mariia Gavriushenko on personalized learning environments

I’m at the STEAM ed Finland conference in Jyväskylä where Mariia Gavriushenko is talking about personalized learning environments.


Web-based learning systems are being more and more widely used in large part because they can be used any time, anywhere. She points to two types: Learning management systems and game-based systems. But they lack personalization that makes them suitable for particular learners in terms of learning speed, knowledge background, preferences in learning and career, goals for future life, and their differing habits. Personalized systems can provide assistance in learning and adapt their learning path. Web-based learning shouldn’t just be more convenient. It should also be better adapted to personal needs.


But this is hard. But if you can do it, it can monitor the learner’s knowledge level and automatically present the right materials. In can help teachers create suitable material and find the most relevant content and convert it into comprehensive info. It can also help students identify the best courses and programs.


She talks about two types of personalized learning systems: 1. systems that allow the user to change the system or 2. the sysytem changes itself to meet the users needs. The systems can be based on rules and context or can be algorithm driven.


Five main features of adaptive learning systems:

  • Pre-test

  • Pacing and control

  • Feedback and assessment

  • Progress tracking and reports

  • Motivation and reward


The ontological presentation of every learner keeps something like a profile for each user, enabling semantic reasoning.


She gives an example of this model: automated academic advising. It’s based on learning analytics. It’s an intelligent learning support system based on semantically-enhanced decision support, that identifies gaps, and recommends materials and courses. It can create a personal study plan. The ontology helps the system understand which topics are connected to others so that it can identify knowledge gaps.


An adaptive vocabulary learning environment provides cildren with an adaptive way to train their vocabulary, taking into account the individuality of the learner. It assumes the more similar the words, the harder they are to recognize.


Mariia believes we will make increasing use of adaptive educational tech.

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Categories: ai, education, liveblog Tagged with: ai • education • personalization • teaching Date: December 17th, 2017 dw

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[liveblog] Maarit Rossi on teaching math that matters

I’m at the STEAM ed Finland conference in Jyväskylä. Maarit Rossi, who teaches math teaching around the world, is talking on the topic: “AI forces us to change maths education.”

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.


Finnish teachers are doing a great, great job, she says. “But we are doing it too quietly.”


Education is too similar to industrial assembly lines. Students sit passively in rows. Students find math education to be boring, meaningless, and frightening. Typically this happens sometime in 5-7th grade. Teaching math has not changed in 100 years. It is a global problem.


Meanwhile, tech is changing really quickly. (She shows a photo from 1956 of workers shoving a 5 megabyte drive onto a truck.)

1956 5mb drive loaded onto truck

These days we are talking about personalizing math education. Easily available programs solve math problems. In the USA, people say the students are “cheating.” No, they’re being educated wrong. We need to be asking if we’re teaching students 10 critical skills, including cognitive flexibility, nebotiation, coordinating with others, emotional intelligence, critical thinking, creaetivity, complex problem solving, service orientation [and a couple of others I didn’t have time to copy down].


A modern math curriculum addresses attitudes, metacognition (e.g., self-regulation), skills, concepts, and processes. Instead, we focus on the concepts (e.g., algebraic, statistical, etc.).


A classroom has to be a safe place where you can make mistakes.


There are four pillars: practice, learning by doing, social learning, and interdisciplinary math. She gives some examples. Students estimate the price of a week’s shopping for a family of four. Maaritt has students work in groups of four. After that, they go to the nearest shop to find the actual prices; the students have to divide up the task to get it done in time. (You can have them do online shopping if there isn’t nearby shop.) Students estimate and round the numbers, tasks that are usually taught separately.


For higher grades, the students deal with real data from an African refugee camp. The students have to estimate how much food is needed to keep everyone alive for two weeks. “This is meaningful to them.”


It’s important for math to have double the lesson length. If it’s only one hour, it is not enough. “The students love it when they have the opportunity to think, to discover, to find themselves.”


Re-arrange the classroom. Cluster the tables rather than rows. The students can teach one another. “It is important that the feel successful.”


“And of course we use computers. And apps. And phones.”


“Math is also interesting because it can model many things.” If they have an embodied sense of a cubic meter, for example, they learn how to convert them to other measures. Or model the size of the solar system outside.


She has students estimate collections of objects, e.g. a bowl of noodles. Then they round. Then they count. Groups come up with strategies for counting, including doing it in ways that enable the count to be interrupted and resumed.


Physical exercise makes brains work better.

Classifying is important. She asks students to take sheets of paper and make the biggest triangle they can, and another of a different shape. They put all the triangles in the middle of the room. Then she asks them to see if they can cluster them by similarities.


“Students need to use their own language” rather than only hearing the teacher talk. This is how they learn to understand.


[My notes about the last few minutes, and the questions, go cut off via brain-computer glitch. Sorry.]

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Categories: education, liveblog Tagged with: liveblog • math • science • teaching Date: December 17th, 2017 dw

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[liveblogging] SMART education

I’m at the STEAM ed Finland conference in Jyväskylä. Maria Kankaanranta, Leena Hiltunen, Kati Clements and Tiina Mäkelä are on the faculty of the School of Education at the University of Jyväskylä The are going to talk about SMART education.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.


SMART means self-directed, motivated, adaptive, reseource enriched, and technology-embedded learning. (They credit South Korean researchers for this.) This is a paradigm shift: From education a specific times to any time. From lectures to motivated ed methods. From teaching the 3Rs to epanding the ed capacity. From traditional textbooks to enriched resources. From a physical space to anywhere there is the enabling tech.


One project (Horizon 2020) works across disciplines to connect students, parents, teachers, and companies. Companies expect universities to develop the skills they need, but you really have to begin with primary school. The aim of the project is to create a pedagogical framework and design principles for attractive and engaging STEM learning environments. She presents a long list of pedagogical design principles that guide the design of this kind of hybrid learning enviroments. It includes adaptive learning, self-regulation, project-based learning, novelty, but also conventionality: “you don’t have to abandon everything.”


What beyond MOODLE can we do? The EU has funded instruments for procurement of innovation. The presenters have worked on IMAILE & LEA (LearnTech Accelerator). IMAILE ran for 48 months in four countries. To address problems, the project pointed to two existing solutions: YipTree and AMIGO (e-books publisher from Spain). YipTree provides individual personalized learning paths (adaptive materials), student motivation by a virtual tutor and by other students, gamificiation, quick assessment tools, and notifications when a student is having difficulties. They tested this in two schools per country. YipTree did well.


They have been training teachers in computational thinking, programming, and robotics. They use online, mobile apps to make it available and free for all teachers and students. They’re using different training models to motivate and encourage teachers to adopt these apps. E.g., they’re “hijacking” schools and workplaces to train them where they are. Teachers really want human engagement.


Schools have access to tech resources but they’re under-used because the teachers don’t know what’s available and possible. This presentation’s project is helping teachers with this.


Conclusion: Smart ed is not easy. It takes time. It requires getting out of your comfort zone. It requires training, tools, research, and a human touch.


Q&A


Q: Does your model take into account students with disabilities?

A: Yes. Part of this is “access for all.” Also, IMAILE does. Imperfectly. They collaborate with a local school for the impaired.

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Categories: education, liveblog Tagged with: education • liveblog Date: December 17th, 2017 dw

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[liveblog] Ulla Richardson on a game that teaches reading

I’m at the STEAM ed Finland conference in Jyväskylä where Ulla Richardson is going to talk about GraphoLearn, an adaptive learning method for learning to read.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.


Ulla has been working on the Jyväskylä< Longitudinal Study of Dyslexia (JLD). Globally, one third of people can’t read or have poor reading skills. One fifth of Europe also. About 15% of children have learning disabilities.


One Issue: knowing which sound goes with which letters. GraphoLearn is a game to help students with this, developed by a multidisciplinary team. You learn a word by connecting a sound to a written letter. Then you can move to syllables and words. The game teaches by trial and error. If you get it wrong, it immediately tells you the correct sound. It uses a simple adaptive approach to select the wrong choices that are presented. The game aims at being entertaining, and motivates also with points and rewards. It’s a multi-modal system: visual and audio. It helps dyslexics by training them on the distinctions between sounds. Unlike human beings, it never displays any impatience.

It adapts to the user’s skill level, automatically assessing performance and aiming at at 80% accuracy so that it’s challenging but not too challenging.


13,000 players have played in Finland, and more in other languages. Ulla displays data that shows positive results among students who use GraphoLearn, including when teaching English where every letter has multiple pronunciations.


There are some difficulties analyzing the logs: there’s great variability in how kids play the game, how long they play, etc. There’s no background info on the students. [I missed some of this.] There’s an opportunity to come up with new ways to understand and analyze this data.


Q&A


Q: Your work is amazing. When I was learning English I could already read Finnish, so I made natural mispronunciations of ape, anarchist, etc. How do you cope with this?


A: Spoken and written English are like separate languages, especially if Finnish is your first language where each letter has only one pronunciation. You need a bigger unit to teach a language like English. That’s why we have the Rime approach where we show the letters in more context. [I may have gotten this wrong.]


Q: How hard is it to adapt the game to each language’s logic?


A: It’s hard.

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Categories: ai, education, games, liveblog, machine learning Tagged with: education • games • language • machine learning Date: December 17th, 2017 dw

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December 16, 2017

[liveblog] Mirka Saarela and Sanna Juutinen on analyzing education data

I’m at the STEAM ed Finland conference in Jyväskylä. Mirka Saarela and Sanna Juutinen are talking about their analysis of education data.

NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.


There’s a triennial worldwide study by the OECD to assess students. Usually, people are only interested in its ranking of education by country. Finland does extremely well at this. This is surprising because Finland does not do particularly well in the factors that are taken to produce high quality educational systems. So Finnish ed has been studied extensively. PISA augments this analysis using learning analytics. (The US does at best average in the OECD ranking.)


Traditional research usually starts with the literature, develops a hypothesis, collects the data, and checks the result. PISA’s data mining approach starts with the data. “We want to find a needle in the haystack, but we don’t know what the needle looks like.” That is, they don’t know what type of pattern to look for.


Results of 2012 PISA: If you cluster all 24M students with their characteristics and attitudes without regard to their country you get clusters for Asia, developing world, Islamic, western countries. So, that maps well.


For Finland, the most salient factor seems to be its comprehensive school system that promotes equality and equity.

In 2015 for the first time there was a computerized test environment available. Most students used it. The logfile recorded how long students spent on a task and the number of activities (mouse clicks, etc.) as well as the score. They examined the Finnish log file to find student profiles, related to student’s strategies and knowledge. Their analysis found five different clusters. [I can’t read the slide from here. Sorry.] They are still studying what this tells us. (They purposefully have not yet factored in gender.)


Nov. 2017 results showed that girls did far better than boys. The test was done in a chat environment which might have been more familiar for the girls? Is the computerization of the tests affecting the results? Is the computerization of education affecting the results? More research is needed.


Q&A


Q: Does the clustering suggest interventions? E.g., “Slow down. Less clicking.”

A: [I couldn’t quite hear the answer, but I think the answer is that it needs more analysis. I think.]


Q: I work for ETS. Are the slides available?


A: Yes, but the research isn’t public yet.

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Categories: ai, education, liveblog, machine learning Tagged with: ai • education • liveblog • machine learning Date: December 16th, 2017 dw

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