A much more accurate definition of an algorithm is that it’s an opinion embedded in math.
So, we do that every time we build algorithms — we curate our data, we define success, we embed our own values into algorithms.
So when people tell you algorithms make thing objective, you say “no, algorithms make things work for the builders of the algorithms.”
In general, we have a situation where algorithms are extremely powerful in our daily lives but there is a barrier between us and the people building them, and those people are typically coming from a kind of homogenous group of people who have their particular incentives — if it’s in a corporate setting, usually profit and not usually a question of fairness for the people who are subject to their algorithms.
So we always have to penetrate this fortress. We have to be able to question the algorithms themselves.
We live in the age of the algorithm – mathematical models are sorting our job applications, curating our online worlds, influencing our elections, and even deciding whether or not we should go to prison. But how much do we really know about them? Former Wall St quant, Cathy O’Neil, exposes the reality behind the AI, and explains how algorithms are just as prone to bias and discrimination as the humans who program them.
An algorithm is not a fact. It's an opinion embedded in math. Watch the new RSA Short with @mathbabedotorg explaining the truth about algorithms. https://t.co/363pWAf5ut
And then I made a big change. I quit my job and went to work as a quant for D. E. Shaw, a leading hedge fund. In leaving academia for finance, I carried mathematics from abstract theory into practice. The operations we performed on numbers translated into trillions of dollars sloshing from one account to another. At first I was excited and amazed by working in this new laboratory, the global economy. But in the autumn of 2008, after I’d been there for a bit more than a year, it came crashing down.
The crash made it all too clear that mathematics, once my refuge, was not only deeply entangled in the world’s problems but also fueling many of them. The housing crisis, the collapse of major financial institutions, the rise of unemployment- all had been aided and abetted by mathematicians wielding magic formulas. What’s more, thanks to the extraordinary powers that I loved so much, math was able to combine with technology to multiply the chaos and misfortune, adding efficiency and scale to systems that I now recognized as flawed.
If we had been clear-headed, we all would have taken a step back at this point to figure out how math had been misused and how we could prevent a similar catastrophe in the future. But instead, in the wake of the crisis, new mathematical techniques were hotter than ever, and expanding into still more domains. They churned 24/ 7 through petabytes of information, much of it scraped from social media or e-commerce websites. And increasingly they focused not on the movements of global financial markets but on human beings, on us. Mathematicians and statisticians were studying our desires, movements, and spending power. They were predicting our trustworthiness and calculating our potential as students, workers, lovers, criminals.
This was the Big Data economy, and it promised spectacular gains. A computer program could speed through thousands of résumés or loan applications in a second or two and sort them into neat lists, with the most promising candidates on top. This not only saved time but also was marketed as fair and objective.
Yet I saw trouble. The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.
I came up with a name for these harmful kinds of models: Weapons of Math Destruction, or WMDs for short.
Equally important, statistical systems require feedback- something to tell them when they’re off track. Statisticians use errors to train their models and make them smarter. If Amazon. com, through a faulty correlation, started recommending lawn care books to teenage girls, the clicks would plummet, and the algorithm would be tweaked until it got it right. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes.
Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive- and very common.
In WMDs, many poisonous assumptions are camouflaged by math and go largely untested and unquestioned.
This underscores another common feature of WMDs. They tend to punish the poor. This is, in part, because they are engineered to evaluate large numbers of people. They specialize in bulk, and they’re cheap. That’s part of their appeal. The wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. The privileged, we’ll see time and again, are processed more by people, the masses by machines.
Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.
Indeed. These three great books provide a systems view of higher education and its intersections with tech and algorithms. Below, I excerpt from their introductions and book blurbs, provide chapter lists, and select a handful of tweets from authors Tressie McMillan Cottom, Sara Goldrick-Rab, and Cathy O’Neil. They are all active on Twitter and well worth a follow.
This book is about the power of algorithms in the age of neoliberalism and the ways those digital decisions reinforce oppressive social relationships and enact new modes of racial profiling, which I have termed technological redlining. By making visible the ways that capital, race, and gender are factors in creating unequal conditions, I am bringing light to various forms of technological redlining that are on the rise. The near-ubiquitous use of algorithmically driven software, both visible and invisible to everyday people, demands a closer inspection of what values are prioritized in such automated decision-making systems. Typically, the practice of redlining has been most often used in real estate and banking circles, creating and deepening inequalities by race, such that, for example, people of color are more likely to pay higher interest rates or premiums just because they are Black or Latino, especially if they live in low-income neighborhoods. On the Internet and in our everyday uses of technology, discrimination is also embedded in computer code and, increasingly, in artificial intelligence technologies that we are reliant on, by choice or not. I believe that artificial intelligence will become a major human rights issue in the twenty-first century. We are only beginning to understand the long-term consequences of these decision-making tools in both masking and deepening social inequality. This book is just the start of trying to make these consequences visible. There will be many more, by myself and others, who will try to make sense of the consequences of automated decision making through algorithms in society.
Part of the challenge of understanding algorithmic oppression is to understand that mathematical formulations to drive automated decisions are made by human beings. While we often think of terms such as “big data” and “algorithms” as being benign, neutral, or objective, they are anything but. The people who make these decisions hold all types of values, many of which openly promote racism, sexism, and false notions of meritocracy, which is well documented in studies of Silicon Valley and other tech corridors.
Indeed. These three great books provide a systems view of higher education and its intersections with tech and algorithms. Below, I excerpt from their introductions and book blurbs, provide chapter lists, and select a handful of tweets from authors Tressie McMillan Cottom, Sara Goldrick-Rab, and Cathy O’Neil. They are all active on Twitter and well worth a follow.
Lower Ed: The Troubling Rise of For-Profit Colleges in the New Economy
Years later, I would also realize how Jason could think that the Technical College was God’s will, as education gospels converge with our articles of faith in individual work ethic, self-sacrifice, and gendered norms about being the head of a household. A college education, whether it is a night class in auto mechanics or a graduate degree in physics, has become an individual good. This is in contrast to the way we once thought of higher (or post-secondary) education as a collective good, one that benefits society when people have the opportunity to develop their highest abilities through formal learning. Despite our shift to understanding higher education as a personal good, we have held on to the narrative of all education being inherently good and moral. Economists W. Norton Grubb and Marvin Lazerson call this the education gospel: our faith in education as moral, personally edifying, collectively beneficial, and a worthwhile investment no matter the cost, either individual or societal. Grubb and Lazerson aren’t the only ones to allude to education as a faith-based institution. All institutions require our collective faith in them for them to work. We call that legitimacy. But I like Grubb and Lazerson’s construction of the education gospel, in part because it speaks to the contradictions in that faith. The gospel was critical to higher education’s shift to its vocational promise. That is, the idea that higher education is a moral good is allowable only insofar as higher education serves market interests. Education is good because a good job is good. The faith breaks down when we divorce higher education from jobs. The contradiction is that we don’t like to talk about higher education in term of jobs, but rather in terms of citizenship and the public good, even when that isn’t the basis of our faith.
Based on the education gospel, we increasingly demand more personal sacrifice from those who would pursue higher education: more loans, fewer grants; more choices, fewer practical options; more possibilities, more risk of failing to attain any of them. We justify that demand by pointing to the significant return in higher wages that those with higher education credentials enjoy. And we imply that this wage premium will continue in the “knowledge economy,” where twenty-first-century jobs will require everyone to have some post-secondary education to do highly cognitive work. The gap between the education gospel and the real options available to people—those who need a priest but who instead get a televangelist—is how we end up with Lower Ed.
Lower Ed refers to credential expansion created by structural changes in how we work, unequal group access to favorable higher education schemes, and the risk shift of job training, from states and companies to individuals and families, exclusively for profit. Lower Ed is the subsector of high-risk post-secondary schools and colleges that are part of the same system as the most elite institutions. In fact, Lower Ed can exist precisely because elite Higher Ed does. The latter legitimizes the education gospel while the former absorbs all manner of vulnerable groups who believe in it: single mothers, downsized workers, veterans, people of color, and people transitioning from welfare to work. Lower Ed is, first and foremost, a set of institutions organized to commodify social inequalities (see Chapters 3 and 4) and make no social contributions beyond the assumed indirect effect of greater individual human capital. But Lower Ed is not just a collection of schools or set of institutional practices like profit taking and credential granting. Lower Ed encompasses all credential expansion that leverages our faith in education without challenging its market imperatives and that preserves the status quo of race, class, and gender inequalities in education and work. When we offer more credentials in lieu of a stronger social contract, it is Lower Ed. When we ask for social insurance and get workforce training, it is Lower Ed. When we ask for justice and get “opportunity,” it is Lower Ed.
More than two million students are enrolled in for-profit colleges, from the small family-run operations to the behemoths brandished on billboards, subway ads, and late-night commercials. These schools have been around just as long as their bucolic not-for-profit counterparts, yet shockingly little is known about why they have expanded so rapidly in recent years-during the so-called Wall Street era of for-profit colleges.
In Lower Ed Tressie McMillan Cottom-a bold and rising public scholar, herself once a recruiter at two for-profit colleges-expertly parses the fraught dynamics of this big-money industry to show precisely how it is part and parcel of the growing inequality plaguing the country today. McMillan Cottom discloses the shrewd recruitment and marketing strategies that these schools deploy and explains how, despite the well-documented predatory practices of some and the campus closings of others, ending for-profit colleges won’t end the vulnerabilities that made them the fastest growing sector of higher education at the turn of the twenty-first century. And she doesn’t stop there.
With sharp insight and deliberate acumen, McMillan Cottom delivers a comprehensive view of postsecondary for-profit education by illuminating the experiences of the everyday people behind the shareholder earnings, congressional battles, and student debt disasters. The relatable human stories in Lower Ed-from mothers struggling to pay for beauty school to working class guys seeking “good jobs” to accomplished professionals pursuing doctoral degrees-illustrate that the growth of for-profit colleges is inextricably linked to larger questions of race, gender, work, and the promise of opportunity in America.
Drawing on more than one hundred interviews with students, employees, executives, and activists, Lower Ed tells the story of the benefits, pitfalls, and real costs of a for-profit education. It is a story about broken social contracts; about education transforming from a public interest to a private gain; and about all Americans and the challenges we face in our divided, unequal society.
Paying the Price: College Costs, Financial Aid, and the Betrayal of the American Dream
There is a new economics of college in America. In the past, students and families who worked hard stood a real chance of attaining a college degree, a ticket to the good life. But then the world shifted. Today, the promise of a college degree in exchange for hard work and dedication no longer holds true. Instead, students encounter a price so high that it has changed what it means to attend college.
Unfortunately, many people don’t know this. Millions enroll in higher education with plans to work, borrow, and save, only to find that their funds still fall short. Even living on ramen, doubling up with roommates, and working a part-time job isn’t enough to make ends meet. Many who start college can’t afford to complete their degrees. Others take on huge debt that either they cannot repay or limits their future opportunities. And this is occurring at a time when diplomas matter more than ever.
What happened? Just as Americans decided that college was essential, states began spending less on public higher education and the price of college rose. At the same time, the financial aid system, long intended to make college affordable, failed to keep up with growing student and family need. Student loans became the stopgap. And, to make matters worse, for nearly 80 percent of the public, family income declined.
What does this mean for students facing the new economics in public colleges and universities? How are they managing to make it through higher education today, and where are they falling short? This book is the result of my six-year-long effort to find out. As you will see, the statistics and stories make one thing quite clear: college students are paying a hefty price.
If you are a young person, and you work hard enough, you can get a college degree and set yourself on the path to a good life, right?
Not necessarily, says Sara Goldrick-Rab, and with Paying the Price, she shows in damning detail exactly why. Quite simply, college is far too expensive for many people today, and the confusing mix of federal, state, institutional, and private financial aid leaves countless students without the resources they need to pay for it.
Weapons of Math Destruction: How big data increases inequality and threatens democracy
And then I made a big change. I quit my job and went to work as a quant for D. E. Shaw, a leading hedge fund. In leaving academia for finance, I carried mathematics from abstract theory into practice. The operations we performed on numbers translated into trillions of dollars sloshing from one account to another. At first I was excited and amazed by working in this new laboratory, the global economy. But in the autumn of 2008, after I’d been there for a bit more than a year, it came crashing down.
The crash made it all too clear that mathematics, once my refuge, was not only deeply entangled in the world’s problems but also fueling many of them. The housing crisis, the collapse of major financial institutions, the rise of unemployment— all had been aided and abetted by mathematicians wielding magic formulas. What’s more, thanks to the extraordinary powers that I loved so much, math was able to combine with technology to multiply the chaos and misfortune, adding efficiency and scale to systems that I now recognized as flawed.
If we had been clear-headed, we all would have taken a step back at this point to figure out how math had been misused and how we could prevent a similar catastrophe in the future. But instead, in the wake of the crisis, new mathematical techniques were hotter than ever, and expanding into still more domains. They churned 24/ 7 through petabytes of information, much of it scraped from social media or e-commerce websites. And increasingly they focused not on the movements of global financial markets but on human beings, on us. Mathematicians and statisticians were studying our desires, movements, and spending power. They were predicting our trustworthiness and calculating our potential as students, workers, lovers, criminals.
This was the Big Data economy, and it promised spectacular gains. A computer program could speed through thousands of résumés or loan applications in a second or two and sort them into neat lists, with the most promising candidates on top. This not only saved time but also was marketed as fair and objective.
Yet I saw trouble. The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.
I came up with a name for these harmful kinds of models: Weapons of Math Destruction, or WMDs for short.
Equally important, statistical systems require feedback— something to tell them when they’re off track. Statisticians use errors to train their models and make them smarter. If Amazon. com, through a faulty correlation, started recommending lawn care books to teenage girls, the clicks would plummet, and the algorithm would be tweaked until it got it right. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes.
Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive— and very common.
In WMDs, many poisonous assumptions are camouflaged by math and go largely untested and unquestioned.
This underscores another common feature of WMDs. They tend to punish the poor. This is, in part, because they are engineered to evaluate large numbers of people. They specialize in bulk, and they’re cheap. That’s part of their appeal. The wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. The privileged, we’ll see time and again, are processed more by people, the masses by machines.
A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life – and threaten to rip apart our social fabric
We live in the age of the algorithm. Increasingly, the decisions that affect our lives-where we go to school, whether we get a car loan, how much we pay for health insurance-are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.
But as Cathy O’Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.” Welcome to the dark side of Big Data.
Tracing the arc of a person’s life, O’Neil exposes the black box models that shape our future, both as individuals and as a society. These “weapons of math destruction” score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health.
O’Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it’s up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
I’m a big fan of Design for Real Life. Author Eric Meyer now has a Design for Humanity course.
Designing for humans is tough. We design for millions, but every interaction between our work and a user is personal, and we aren’t taught to take care with those interactions. I created this course because I want everything we design to meet the real needs and wants of real people.
If you want a set of tools for stress-testing your work to make sure it’s as human-centered, compassionate, and inclusive as possible, this is the course for you. I’ll show you how to approach designing for your users, and I’ll provide a set of tools and processes to stress-test your design work to make sure it’s as human-centered, compassionate, and inclusive as possible.
I’m autistic with chronic, neuropathic pain. Cannabis and harm reduction are important and very personal topics to me.
The drug war’s perverse notions of addiction, addicts, and coping limit our vocabulary, stifle our empathy, and harm us all-especially those with neurodivergent operating systems and those enduring poverty and structural racism and ableism.
Why is harassment an automatic career hazard for a woman receiving any amount of professional attention? Question for @jack & also society! https://t.co/ULI570zV9n
Survivorship bias, or survival bias, is the logical error of concentrating on the people or things that “survived” some process and inadvertently overlooking those that did not because of their lack of visibility.
But I’ve found that a potentially “safe” way to broach the topic is by way of bringing up “sensory sensitivity” and “sensory processing” terminology first. After all, people who don’t know they’re on the spectrum assume they’re neurotypical, and certain terminology is more palatable for them than other terminology. Sensory issues tend to be much more agreeable terms in the neurotypical world.
I then began to list characteristics, asking them in the form of questions, to all of which the person nodded, with increasing vigor with each subsequent question. Eventually, the person and their partner actually laughed and high-fived each other as if to say, “finally! Somebody gets it!” (And this person is not one who had been likely to laugh or high-five anyone, especially in front of a healthcare professional.)
I played a tennis match of yes-no in my brain. I hadn’t yet mentioned the terms Asperger’s or autism yet; should I, or shouldn’t I? After much internal back-and-forth I finally thought, “screw it; bring it up.”
So then came the magical question: “what do you know about Asperger’s/autism?”
And the dialogue proceeded from there, as I listened carefully to their answer and clarified the truths, gently adjusting their previously-held notions (which weren’t entirely negative). Then I listed off more characteristics in plain, everyday language, also to which they nodded enthusiastically.
I mentioned that a lot of us get misdiagnosed with what I call the “Usual 5” (and there may be others): depression, anxiety, ADHD, bipolar, and OCD, before we realize that we’re actually Aspergian/autistic, and that the spectrum condition actually explains all of the others, negating and replacing those other labels, which had been reluctantly accepted but never fully embraced, because they didn’t seem to fit.
I stated clearly that my “theory” about what was going on with this person was not, in fact, a diagnosis, but simply the former: a theory, an idea. Nothing more. And I added that if they wanted to investigate this further, that I recommended the following steps:
Start with the self-quizzes; they’re not diagnostic, but they ask simple yes/no questions, and provide a quick, easy, no-cost starting point to see if the subject was even worth pursuing further
Based on the score, then they can do several things, one of which is to search for blogs written by Aspergian/autistic adults, especially ones of their same gender, to see if what they say resonates with them
Either instead of or immediately after reading the blogs, they can search for the positive attributes of Asperger’s/autism; knowing this person, going straight for the diagnostic criteria first might cause anxiety or depression, with all of the “impairments” and “difficulties” and “restricted” this and “insufficient” that. If interested, go for the happy stuff first.
At any point, if they’d like to pursue an official evaluation, I know just the right specialists, and I’m more than happy to make the referrals.
To blame trans identities on autism is to say that autistic people cannot understand or be aware of their own gender. If an autistic person cannot know they are trans, how can they know they aren’t? How can they know anything about themselves?
When a person’s gender is doubted because they are autistic, this paves the way for removing autistic people’s agency in all kinds of other ways. If we can’t know this central aspect of our identity, we surely can’t know how we feel, what we like, or who we are. In short, it implies that we are not truly people, and that our existence, experiences, and identities are for other people to define. This is just another facet of dehumanising autistic people, and gender is certainly not the only area in which this happens.
In itself, the very urge to find a ‘reason’ that someone is transgender is a result of believing that being transgender is a problem, and that it would always be better not to be. The fact that clinicians like Zucker are focused on why someone is transgender, instead of focusing on what kind of help they need and how to best provide it, demonstrates clearly the belief that it is fundamentally bad to be transgender.
Not only that, but the belief that it’s even theoretically possible for anyone besides the individual in question to know what someone’s gender is. That’s just not how gender works! No-one really understand what gender is, or what it means, or where it comes from. The only thing we know for sure is that it’s internal, subjective, and personal. You can’t prove or test someone else’s gender any more than you can prove or test their favourite colour. The idea that it can be tested is constantly used to invalidate trans people. Our genders are doubted or disbelieved if we fail to adequately ‘prove’ ourselves to everyone else – if we express too many or too few gender stereotypes, if we are too old or too young, if we claim to be nonbinary or our description of our identity is too complicated or confusing.
The best option is to allow someone to explore their feelings, support them in gaining self-understanding, and accept their identity whatever it turns out to be. It is not complicated, and it’s only scary if you are still holding onto the belief that being either autistic or transgender – or, perish the thought, both – is a terrible thing to be. Which it’s not. I am, along with countless others like me, living proof of that.
Companies and government institutions that use data need to pay attention to the unconscious and institutional biases that seep into their results. It doesn’t take active prejudice to produce skewed results in web searches, data-driven home loan decisions, or photo-recognition software. It just takes distorted data that no one notices and corrects for. Thus, as we begin to create artificial intelligence, we risk inserting racism and other prejudices into the code that will make decisions for years to come. As Laura Weidman Powers, founder of Code2040, which brings more African Americans and Latinos into tech, told me, “We are running the risk of seeding self-teaching AI with the discriminatory undertones of our society in ways that will be hard to rein in because of the often self-reinforcing nature of machine learning.”
Many people seem to believe that decisions made by computers are inherently neutral, but when Tay screeched “race war now!!!” into the Twitterverse, it should have illustrated to everyone the threat of algorithmic prejudice. Without careful consideration of the data, the code, the coders, and how we monitor what emerges from “deep learning,” our technology can be just as racist, sexist, and xenophobic as we are.
For more on algorithmic bias, algorithmic exclusion, and data ethics, see this collection of links.
Psychological Safety
Dig into project-based and self-directed learning, and you’ll find psychological safety. Dig into privilege, and find psychological safety. Dig into voice and choice, and find psychological safety. Dig into creative teams, Employee Resource Groups (ERGs), and Employee Networks, and find psychological safety.
Psychological safety is necessary to building creative, collaborative teams. We’re learning that in the industries I inhabit, and I see that same learning happening in the self-directed learning space. Students and workers don’t want to leave their real lives at home. They want to design for their real lives–in psychological safety.
I updated my post on Projects, Teams, and Psychological Safety with more resources, including a couple videos and quotes from a couple studies. Reflect on your career through the lens of psychological safety.
Performance terror. We’ve all known a classroom, meeting room or stage where we didn’t feel safe doing something we were quite capable of doing.
“As a college professor I encourage students to read their work aloud, but I never insist on it,” said Carey. “Sometimes those who are uncomfortable doing it will volunteer on their own because it’s their decision rather than mine.”
“I centered my instruction on the lives, histories and identities of my students. And I did all of this because I wanted my students to know that everyone around them was supporting them to be their best self,” said Simmons.
A supportive culture, sustained advisory relationships, and teaching strategies that create positive learning all promote psychological safety.
“Every child deserves an education that guarantees the safety to learn in the comfort of one’s own skin,” said Simmons.
Every child deserves an education that guarantees the safety to learn in the comfort of one’s own skin.
I centered my instruction on the lives, histories and identities of my students. And I did all of this because I wanted my students to know that everyone around them was supporting them to be their best self.
So while I could not control the instability of their homes, the uncertainty of their next meal, or the loud neighbors that kept them from sleep, I provided them with a loving classroom that made them feel proud of who they are, that made them know that they mattered.
There is a better way, one that doesn’t force kids of color into a double bind; a way for them to preserve their ties to their families, homes and communities; a way that teaches them to trust their instincts and to have faith in their own creative genius.
Further, Kahn argued that people are more likely to believe they will be given the benefit of the doubt—a defining characteristic of psychological safety—when relationships within a given group are characterized by trust and respect.
Here's a thing that was obvious to me from 1st grade on. It's extraordinary that people spend their whole lives in schools & don't see it. pic.twitter.com/mW4I1eRTFM
This fantastic talk is full of social model goodness. Highly recommended.
You don’t need somebody to fix you. You need somebody to fight for you, and with you.
The myth of normal is what’s broken.
Disability industrial complex is all about what people can’t do. We spend most of our time trying to fix what they can’t do. When all we do is fix people the message we give to them is that they are broken.
We have created a system that has you submit yourself, or your child, to patient hood to access the right to learn differently. The right to learn differently should be a universal human right that’s not mediated by a diagnosis.
When I saw @rboren tweeted this 4X, I knew it must be good. All y'all who think I'm too down on schools, watch this 4 times & let's talk. https://t.co/fBvJvi5jGV
ABC’s “Speechless,” a sitcom about a family with a son who has a disability, tackled why it’s often offensive to call people with disabilities “inspirational.” And it’s done so, so well.
“Inspiration porn” is a term used to describe a common tendency in which able-bodied people condescend to those with disabilities by suggesting they are brave or special just for living. Ray DiMeo, a character in “Speechless” who is the younger brother of a teen with cerebral palsy, explained it perfectly in Wednesday night’s episode:
“It’s a portrayal of people with disabilities as one-dimensional saints who only exist to warm the hearts and open the minds of able-bodied people,” he said.
To which his brother, JJ, who has cerebral palsy, hilariously adds: “I blame Tiny Tim.”
While these sorts of simplistic attitudes may seem harmless, if misguided, they can have real consequences in a world where disabilities are stigmatized. Research even shows stigma can lead to damaging health care consequences.
What’s more, these kinds of portrayals render the person who is disabled as a side character only revered for what they provide to others.
Creativity – as an expression of originality, experimentation, innovation – is not a viable product. It has been priced out into irrelevance – both by the professionalization of the industries that claim it, and the soaring cost of entry to those professions.
Today, creative industries are structured to minimize the diversity of their participants – economically, racially and ideologically. Credentialism, not creativity, is the passport to entry.
“What the artist was pretending he didn’t know is that money is the passport to success,” she writes. “We may be free beings, but we are constrained by an economic system rigged against us. What ladders we have, are being yanked away. Some of us will succeed. The possibility of success is used to call the majority of people failures.”
But creative people should not fear failure. Creative people should fear the prescribed path to success – its narrowness, its specificity, its reliance on wealth and elite approval. When success is a stranglehold, true freedom is failure. The freedom to fail is the freedom to innovate, to experiment, to challenge.
To “succeed” is to embody the definition of contemporary success: sanctioned, sanitized, solvent.
To which the 30-something, having spent their adult life in an economy of stagnant wages and eroding opportunities, takes the 20-something aside, and explains that this is a maxim they, too, were told, but from which they never benefitted. They tell the 20-something what they already know: It is hard to plan for what is already gone. We live in the tunnel at the end of the light.
If you are 35 or younger – and quite often, older – the advice of the old economy does not apply to you. You live in the post-employment economy, where corporations have decided not to pay people. Profits are still high. The money is still there. But not for you. You will work without a pay rise, benefits, or job security. Survival is now a laudable aspiration.
In the post-employment economy, jobs are privileges, and the privileged have jobs.
Unpaid internships lock out millions of talented young people based on class alone. They send the message that work is not labor to be compensated with a living wage, but an act of charity to the powerful, who reward the unpaid worker with “exposure” and “experience”. The promotion of unpaid labor has already eroded opportunity – and quality – in fields like journalism and politics. A false meritocracy breeds mediocrity.
Education is a luxury the minimum wage worker cannot afford. This message is passed on to their children.
Young Americans seeking full-time employment tend to find their options limited to two paths: one of low-status, low-paying temp jobs emblematic of poverty; another of high-status, low-paying temp jobs emblematic of wealth. America is not only a nation of temporary employees – the Walmart worker on a fixed-day contract, the immigrant struggling for a day’s pay in a makeshift “temp town” – but of temporary jobs: intern , adjunct , fellow.
Post-recession America runs on a contingency economy based on prestige and privation. The great commonality is that few are paid enough to live instead of simply survive.
In the post-employment economy, full-time jobs are parceled into low-wage contract labor, entry-level jobs turn into internships, salaries are paid in exposure, and dignity succumbs to desperation.
The problem in America is not that there are no jobs. It is that jobs are not paying. America is becoming a nation of zero-opportunity employers, in which certain occupations are locked into a terrible pay rate for no valid reason, and certain groups – minorities, the poor, and increasingly, the middle class – are locked out of professions because they cannot buy their way in.
During the recession, American companies found an effective new way to boost profits. It was called “not paying people”. “Not paying people” tends to be justified in two ways: a fake crisis (“Unfortunately, we can’t afford to pay you at this time…”) or a false promise (“Working for nearly nothing now will get you a good job later”).
In reality, profits are soaring and poorly compensated labor tends to lead to more poorly compensated labor. Zero opportunity employers are refusing to pay people because they can get away with it. The social contract does not apply to contract workers – and in 2013, that is increasingly what Americans are.
American ideology has long tilted between individualism and Calvinism. What happened to you was either supposed to be in your control – the “pull yourself up by your bootstraps” approach – or divinely arbitrated. You either jumped, or you were meant to fall. Claims you were pushed, or you were born so far down you could not climb up, were dismissed as excuses of the lazy. This is the way many saw their world before it collapsed.
They cut and blame us when we bleed.
When people are expected to work unpaid for the promise of work, the advantage goes to those immune from the hustle: the owners over the renters, the salaried over the contingent. Attempts to ensure stability and independence for citizens – such as affordable healthcare – are decried as government “charity” while corporate charity is proffered as a substitute for a living wage.
Faust’s is an inspiring tale – and one beyond the comprehension of most young graduates in America today. “Don’t trust the boomers!” warned Paul Campos in a 2012 article on the misguided advice the elder generation peddles to their underemployed, debt-ridden progeny – including gems like “higher education is always worth the price” and “internships lead to jobs” – and Faust’s rebuke proves him right. What is most remarkable about Faust’s career is not its culmination in the Harvard presidency, but the system of accessibility and opportunity that allowed her to pursue it. Her life story is a eulogy for an America long since past.
Participation in these programs and internships is often dependent on personal wealth, resulting in a system of privilege that replicates itself over generations. McArdle compares America’s eroded meritocracy to imperial China, noting that “the people entering journalism, or finance, or consulting, or any other ‘elite’ profession, are increasingly the children of the children of those who rocketed to prosperity through the post-war education system. A window that opened is closing”.
Mobility is but a memory. “The life prospects of an American are more dependent on the income and education of his parents than in almost any other advanced country for which there is data,” writes economist Joseph E Stiglitz in an editorial aptly titled “Equal Opportunity, Our National Myth”.
This is not to say that hard-working elites do not deserve their success, but that the greatest barrier to entry in many professions is financial, not intellectual.
The “lifetime of citizenship, opportunity, growth and change” Drew Gilpin Faust extolled is something most Americans desire. But it is affordable only for a select few: the baby boomers who can buy their children opportunities as the system they created screws the rest.
While the start and end dates of the millennial generation are up for debate – and the idea of inherent generational traits is dubious – people of this age group share an important quality. They have no adult experience in a functional economy.
Millennials are chastised for leaning on elders, but the new rules of the economy demand it. Unpaid internships are often prerequisites to full-time jobs, and the ability to take them is based on money, not merit. Young adults who live off wealthy parents are the lucky few. They can envision a future because they can envision its purchase. Almost everyone else is locked out of the game.
It is one thing to discover, as an adult, that the rules have been rewritten, that the job market will not recover, that you will scramble to survive. It is another to raise a child knowing that no matter how hard they work, how talented they are, how big they dream, they will not have opportunities – because in the new economy, opportunities are bought, not earned. You know this, but you cannot tell this to a child. The millennial parent is always Santa, always a little bit of a liar.
The children of the millennials have been born into a United States of entrenched meritocracy – what Pierre Bourdieu called “the social alchemy that turns class privilege into merit”. Success is allegedly based on competition, not background, but one must be prepared to pay to play.
“This reliance on un- or underpaid labor is part of a broader move to a ‘privilege economy’ instead of a merit economy – where who you know and who pays your bills can be far more important than talent,” writes journalist Farai Chideya, noting that this system often locks out minorities.
By charging more for a year’s college tuition than the average median income, universities ensure that poor people stay poor while debt-ridden graduates join their ranks. By requiring unpaid internships, professions such as journalism ensure positions of influence will be filled only by those who can pay for them. The cycle of privilege and privation continues.
One after another, the occupations that shape American society are becoming impossible for all but the most elite to enter.
My father, the first person in his family to go to college, tries to tell me my degree has value. “Our family came here with nothing,” he says of my great-grandparents, who fled Poland a century ago. “Do you know how incredible it is that you did this, how proud they would be?” And my heart broke a little when he said that, because his illusion is so touching – so revealing of the values of his generation, and so alien to the experience of mine.
The assumed divide between mothers who work inside and outside the home is presented as a war of priorities. But in an economy of high debt and sinking wages, nearly all mothers live on the edge. Choices made out of fear are not really choices. The illusion of choice is a way to blame mothers for an economic system rigged against them. There are no “mommy wars”, only money wars – and almost everyone is losing.
For the average married mother of small children, it is often cheaper to stay home – even if she would prefer to be in the workforce. It is hard to “lean in” when you are priced out.
Corporate feminists like Sheryl Sandberg frame female success as a matter of attitude. But it is really a matter of money – or the lack thereof. For all but the fortunate few, American motherhood is making sure you have enough lifeboats for your sinking ship. American motherhood is a cost-cutting, debt-dodging scramble somehow interpreted as a series of purposeful moves. American mothers are not “leaning in”. American mothers are not “opting out”. American mothers are barely hanging on.
Careers in this economy are not about choices. They are about structural constraints masquerading as choice. Being a mother is a structural constraint regardless of your economic position. Mothers pay a higher price in a collapsed economy, but that does not mean they should not demand change – both in institutions and perceptions.
The irony of American motherhood is that the politicians and corporations who hold power do have a choice in how they treat mothers and their children. Yet they act as if they are held hostage to intractable policies and market forces, excusing the incompetence and corporate malfeasance that drain our households dry. Mothers can emulate them and treat “choice” as an individual burden – or we can work together and push for accountability and reform. This option is not easy. But we are used to that.
