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Artificial Unintelligence
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248 pages, Hardcover
First published January 1, 2018
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May 2, 2018
This book may easily win the prize for the best book that no one wants to read. And that is precisely why it is the one book that everyone must read.
Meredith Broussard is a coder, educator, and a computational journalist that specializes in algorithmic accountability reporting. Which is to say that she is a very tech-savvy investigative journalist that emphasizes statistical analysis. (The algorithmic and computational side of it is method more than purpose, to my way of thinking.)
She is most definitely not a Luddite. “My goal in this book is to empower people around technology.” She embraces technology and the power of algorithms, but with a caveat. She is wary of the autonomous school of computing who wants to turn it all over to the machines. And she makes a very strong case that doing so is both impractical, in the foreseeable future, and inappropriate. “We need to stop fetishizing tech.”
It’s an important message. In my own words, machines will never think in the human sense because thought is relative. Even humans have difficulty interpreting reality, which is why so much scientific discovery is ultimately proven to be wrong. All reality exists in context, which means that reality is defined by far more variables than we can comprehend, much less measure and compute. The outer limit may well be infinity.
As a result, any attempt to interpret reality and to use that “data” to think is reliant upon convention and limited representation. And convention is, by definition, imperfect.
Pyrrho of Elis was a not-so-famous Greek philosopher who introduced what ultimately became known as the philosophical school of skepticism. It has been resiliently unpopular for reasons that psychologists can easily explain. Who wants to hang out with skeptics? And connection is ultimately what we all crave.
Pyrrho’s skepticism related to dogma. A dogma is a rule or law or defined procedure or process. A convention is dogma as well. And Pyrrho’s issue with dogma was that whenever you lay it out you have opened the door for a duality—a truth and its exception. There are, quite literally, exceptions to every rule because reality is ultimately defined by an infinite number of variables that can’t be known by either a person or a machine. And that means that the exceptions can never be fully articulated no matter how much computational power you have at your disposal.
Algorithms are ultimately nothing more than mathematical dogma. They can, by definition, never be complete. They will always be limited by probabilities, which is why they work at playing Go and translating language at a superficial level, but will never be the “black box” of human sentient consciousness that we all dream of. Never.
I have witnessed the debilitating over-confidence in tech that Broussard speaks to repeatedly throughout my career in business. Technology is, in many ways, destroying modern business and, in particular, the social contract that employers used to recognize between employer, employee, and community.
Business is consumed with reducing costs, which typically gets falsely interpreted as eliminating bodies. As a result, businesses typically want to automate everything, which, as Broussard explains, means that all of its processes need to be conceptually digitized. They must be restructured to accommodate the very real limitations of mathematics. Which ultimately means that they are often compromised and shaded by the very real personal biases of the person who made the digital conversion.
That works some of the time in some circumstances. But not always. No company will ever be successful in fully automating processes like customer service, sales, quality, and innovation. To the extent they try, moreover, they risk disengaging the people they need to actually make those processes, perhaps assisted by technology, effective.
Broussard has a strong political perspective. We all do. And there are portions of the book where she falls back on her investigative journalism and strays from the overall objective of the book. It’s always to make a relevant point, and she never quite abandons that objective, but the lapses are notable and just a tad distracting.
In part, however, Broussard is admittedly trying to contrast the potential of tech socially and politically with the non-conformist, male-dominated libertarianism of the current tech industry. And that needs to change.
The bigger irony, however, is that a culture and industry built on non-conformity is now ultimately turning back on itself, and is not just promoting, but mandating, the ultimate in conformity. And the hidden risk is that unlike the conformity of things like organized religion, technology is forcing us to conform to norms and standards that we aren’t even aware of because the algorithms that drive our decisions and are filled with the human biases of the people who created them, are largely hidden from view.
And that is ultimately where, I suppose, algorithmic accountability reporting comes in. And I say, “bring it on.” It’s exactly what we need.
In the meantime, however, we need to understand the overriding conceptual paradox of technology. Hal is a myth. The black box is a myth. The autonomous car is a myth (and if we give them broad access to our roads in your lifetime we will regret it). The potential of tech is not a myth. As long as we recognize that humanity is not obsolete, but it is biased.
Meredith Broussard is a coder, educator, and a computational journalist that specializes in algorithmic accountability reporting. Which is to say that she is a very tech-savvy investigative journalist that emphasizes statistical analysis. (The algorithmic and computational side of it is method more than purpose, to my way of thinking.)
She is most definitely not a Luddite. “My goal in this book is to empower people around technology.” She embraces technology and the power of algorithms, but with a caveat. She is wary of the autonomous school of computing who wants to turn it all over to the machines. And she makes a very strong case that doing so is both impractical, in the foreseeable future, and inappropriate. “We need to stop fetishizing tech.”
It’s an important message. In my own words, machines will never think in the human sense because thought is relative. Even humans have difficulty interpreting reality, which is why so much scientific discovery is ultimately proven to be wrong. All reality exists in context, which means that reality is defined by far more variables than we can comprehend, much less measure and compute. The outer limit may well be infinity.
As a result, any attempt to interpret reality and to use that “data” to think is reliant upon convention and limited representation. And convention is, by definition, imperfect.
Pyrrho of Elis was a not-so-famous Greek philosopher who introduced what ultimately became known as the philosophical school of skepticism. It has been resiliently unpopular for reasons that psychologists can easily explain. Who wants to hang out with skeptics? And connection is ultimately what we all crave.
Pyrrho’s skepticism related to dogma. A dogma is a rule or law or defined procedure or process. A convention is dogma as well. And Pyrrho’s issue with dogma was that whenever you lay it out you have opened the door for a duality—a truth and its exception. There are, quite literally, exceptions to every rule because reality is ultimately defined by an infinite number of variables that can’t be known by either a person or a machine. And that means that the exceptions can never be fully articulated no matter how much computational power you have at your disposal.
Algorithms are ultimately nothing more than mathematical dogma. They can, by definition, never be complete. They will always be limited by probabilities, which is why they work at playing Go and translating language at a superficial level, but will never be the “black box” of human sentient consciousness that we all dream of. Never.
I have witnessed the debilitating over-confidence in tech that Broussard speaks to repeatedly throughout my career in business. Technology is, in many ways, destroying modern business and, in particular, the social contract that employers used to recognize between employer, employee, and community.
Business is consumed with reducing costs, which typically gets falsely interpreted as eliminating bodies. As a result, businesses typically want to automate everything, which, as Broussard explains, means that all of its processes need to be conceptually digitized. They must be restructured to accommodate the very real limitations of mathematics. Which ultimately means that they are often compromised and shaded by the very real personal biases of the person who made the digital conversion.
That works some of the time in some circumstances. But not always. No company will ever be successful in fully automating processes like customer service, sales, quality, and innovation. To the extent they try, moreover, they risk disengaging the people they need to actually make those processes, perhaps assisted by technology, effective.
Broussard has a strong political perspective. We all do. And there are portions of the book where she falls back on her investigative journalism and strays from the overall objective of the book. It’s always to make a relevant point, and she never quite abandons that objective, but the lapses are notable and just a tad distracting.
In part, however, Broussard is admittedly trying to contrast the potential of tech socially and politically with the non-conformist, male-dominated libertarianism of the current tech industry. And that needs to change.
The bigger irony, however, is that a culture and industry built on non-conformity is now ultimately turning back on itself, and is not just promoting, but mandating, the ultimate in conformity. And the hidden risk is that unlike the conformity of things like organized religion, technology is forcing us to conform to norms and standards that we aren’t even aware of because the algorithms that drive our decisions and are filled with the human biases of the people who created them, are largely hidden from view.
And that is ultimately where, I suppose, algorithmic accountability reporting comes in. And I say, “bring it on.” It’s exactly what we need.
In the meantime, however, we need to understand the overriding conceptual paradox of technology. Hal is a myth. The black box is a myth. The autonomous car is a myth (and if we give them broad access to our roads in your lifetime we will regret it). The potential of tech is not a myth. As long as we recognize that humanity is not obsolete, but it is biased.
June 9, 2018
A long, meandering read in the Malcolm Gladwell style that takes a long time to make it's point and then doesn't make the point strongly enough. Needs a good edit.
November 7, 2019
I came to this very sympathetic to her side (that AI is not really the solution to everything), but I think she’s too skeptical about all of it. I love her explanations about what code can and can’t do, but I think she gives humans too much credit. Like self-driving cars? I’m not entirely sure that humans are better at driving than a good self driving car could be. She used the same three news stories that always go around about car accidents with these cars, but car accidents by humans are like...not rare so I think maybe I might be ok letting cars drive. And a few other things that computers can do better
November 21, 2018
While the book title and premise offers a compelling contrarian angle on an overwhelmingly positive view of AI and the blind adoption of the idea it will be the saviour of our times, the story actually told seems at the very least confused, or at worst, short-sighted.
The author's tone is one filled with condescension and distracts from an otherwise powerful point - adoption of "AI" has to be aligned with the capabilities (and limitations) of the technology, and the realities of where this technology fits in society. Irrelevant tangents and incessant moralizing weave a common thread through entire chapters. The story of the feebleness of our current technical capabilities somehow morphs into a tale about social virtues, systemic misogyny, racism, and the author's bitterness of not being included and venerated in some social circles.
On the bright side, if you have the patience to get past the style, the book is full of interesting examples where AI, machine learning, and other similar technologies fall flat or at least short of the idyllic potential.
In Chapter 6, a section reads:
It's unfortunate that the author's reference wasn't applied more introspectively.
The author's tone is one filled with condescension and distracts from an otherwise powerful point - adoption of "AI" has to be aligned with the capabilities (and limitations) of the technology, and the realities of where this technology fits in society. Irrelevant tangents and incessant moralizing weave a common thread through entire chapters. The story of the feebleness of our current technical capabilities somehow morphs into a tale about social virtues, systemic misogyny, racism, and the author's bitterness of not being included and venerated in some social circles.
On the bright side, if you have the patience to get past the style, the book is full of interesting examples where AI, machine learning, and other similar technologies fall flat or at least short of the idyllic potential.
In Chapter 6, a section reads:
"Paul Slovic, an expert in risk assessment, writes that we have cognitive fallacies related to expertise. We tend to assume that when people are experts at one thing, their expertise extends to other areas as well."
It's unfortunate that the author's reference wasn't applied more introspectively.
July 10, 2020
Lots of thoughts on this book, but, since I don't have the time, here's a lovely term Meredith Broussard introduces:
Technochauvinism is often accompanied by fellow-traveler beliefs such as Ayn Randian meritocracy; technolibertarian political values; celebrating free speech to the extent of denying that online harassment is a problem; the notion that computers are more “objective” or “unbiased” because they distill questions and answers down to mathematical evaluation; and an unwavering faith that if the world just used more computers, and used them properly, social problems would disappear and we’d create a digitally enabled utopia.Oh, and another one I liked so much, I made it a little visual:
August 13, 2023
An interesting book, but too centred on the author's perspective, which uses much of the current ideological argumentation to attack the world of technology and computing. I tend to take this argument well, but the discourse becomes poor without a view from the other side.
Still, Broussard does a relevant job of drawing attention to issues that cannot be brushed aside.
Still, Broussard does a relevant job of drawing attention to issues that cannot be brushed aside.
July 17, 2018
A shallow mind preaching a primitivist sermon. Is Broussard right? Sometimes. Only by the middle of this book I started to doubt Broussard's reason and assume this is actually a collage of Internet gathered arguments.
February 10, 2025
In the current debate about artificial intelligence (AI), ‘big tech’, and data, Meredith Broussard stands out as a remarkably clear and eloquent voice. In Artificial Unintelligence (2018), she aims to empower people by explaining what computers and algorithms do, who designs them, and who stands to benefit from the rise of technology.
Maths rather than intelligence
Broussard’s recurring message is that technologies – both hardware and software – are created by humans. Simple as this observation may seem, in practice, there is a great deal of misunderstanding about what we can realistically expect from digital technology. The misconceptions are fuelled by promises from the industry. Our language for computers is also misleading. Since computers neither know nor think as sentient beings do, ‘intelligence’ is an inaccurate term. Instead, they consist of multiple layers operating on mathematical principles. The same applies to machine ‘learning’, which essentially means that a machine can improve at its programmed tasks, not that it acquires knowledge, wisdom, or agency. This is not genuine ‘learning’.
Statistics on steroids
When we talk about AI, we mean narrow AI – ‘statistics on steroids’, as Broussard calls it. Narrow AI is designed to perform specific tasks or a set of tasks. General AI, on the other hand, exists only in Hollywood movies. The industry continues, however, to dangle the illusion of a future that was never possible to begin with.
Human-in-the-loop systems
Broussard draws on her coding background to illustrate the risks of treating data – again, generated by people – as immutable truth. While AI can detect patterns too complex for humans, it cannot account for everything. This makes fully automated decision-making undesirable; instead, the author argues that human-in-the-loop systems, which combine AI with human intelligence, are a more realistic path forward. She also highlights that most engineers today are not creating new technologies, but maintaining existing ones.
Big tech
Towards the end, Broussard shifts focus to the people behind the big tech industry, criticising their homogeneous backgrounds and indifference to social issues.
Artificial Unintelligence is often cited, and I understand why: Broussard conveys her expertise in an accessible way and takes a stance. Some sections are so detailed that I skimmed through them, but in the end, I learned a lot. I will continue my reading on digital technologies by revisiting Weapons of Math Destruction by Cathy O’Neil.
Maths rather than intelligence
Broussard’s recurring message is that technologies – both hardware and software – are created by humans. Simple as this observation may seem, in practice, there is a great deal of misunderstanding about what we can realistically expect from digital technology. The misconceptions are fuelled by promises from the industry. Our language for computers is also misleading. Since computers neither know nor think as sentient beings do, ‘intelligence’ is an inaccurate term. Instead, they consist of multiple layers operating on mathematical principles. The same applies to machine ‘learning’, which essentially means that a machine can improve at its programmed tasks, not that it acquires knowledge, wisdom, or agency. This is not genuine ‘learning’.
Statistics on steroids
When we talk about AI, we mean narrow AI – ‘statistics on steroids’, as Broussard calls it. Narrow AI is designed to perform specific tasks or a set of tasks. General AI, on the other hand, exists only in Hollywood movies. The industry continues, however, to dangle the illusion of a future that was never possible to begin with.
Technochauvinism is often accompanied by fellow-traveler beliefs such as Ayn Randian meritocracy; technolibertarian political values; celebrating free speech to the extent of denying that online harassment is a problem; the notion that computers are more “objective” or “unbiased” because they distill questions and answers down to mathematical evaluation; and an unwavering faith that if the world just used more computers, and used them properly, social problems would disappear and we’d create a digitally enabled utopia.
Human-in-the-loop systems
Broussard draws on her coding background to illustrate the risks of treating data – again, generated by people – as immutable truth. While AI can detect patterns too complex for humans, it cannot account for everything. This makes fully automated decision-making undesirable; instead, the author argues that human-in-the-loop systems, which combine AI with human intelligence, are a more realistic path forward. She also highlights that most engineers today are not creating new technologies, but maintaining existing ones.
Big tech
Towards the end, Broussard shifts focus to the people behind the big tech industry, criticising their homogeneous backgrounds and indifference to social issues.
we have a small, elite group of men who tend to overestimate their mathematical abilities, who have systematically excluded women and people of color in favor of machines for centuries, who tend to want to make science fiction real, who have little regard for social convention, who don’t believe that social norms or rules apply to them, who have unused piles of government money sitting around, and who have adopted the ideological rhetoric of far-right libertarian anarcho-capitalists.
What could possibly go wrong?
Artificial Unintelligence is often cited, and I understand why: Broussard conveys her expertise in an accessible way and takes a stance. Some sections are so detailed that I skimmed through them, but in the end, I learned a lot. I will continue my reading on digital technologies by revisiting Weapons of Math Destruction by Cathy O’Neil.
We need to stop fetishizing tech. We need to audit algorithms, watch out for inequality, and reduce bias in computational systems, as well as in the tech industry.
January 10, 2021
note: this review is from a reading of the uncorrected page proof of this book
A solid primer into tech criticism composed through the author's experiences doing data and tech journalism, a historical account of Marvin Minsky's boy's club at MIT and an explanation of basic coding and machine learning to laymen (like me).
Her primary argument is to discredit "technochauvinism" — a belief that tech can solve all, often held by white male libertarian-minded tech bros. Tech criticism has evolved since this book's publication in 2018 making it slightly out of date. It's fundamental critiques are still valid but feel incomplete reading at the end of 2020 — what about questions on the private ownership of tech companies? How much tech actually exists in the companies we call "tech companies"? And are their innovations and growth primarily a function of exploiting regulatory gaps? *cough uber, doordash*
The problems that Broussard identifies in tech, framed through technochauvinsim, revolve around the white male identities of our tech overlords. The solution that she prescribes then is to diversify the tech workforce. It was the solution in the past when white scientists of the 1800s and 1900s refused to educate women and black people to contribute to research as "human computers" and diversity is the solution now as Apple watches were first released without being able to track menstruation.
The "overlord" problem is not so much as explored as the "white male" part. Taking Broussard's view in this book, a technological utopia is around the corner with the help of progressive hiring managers and some diversity and inclusion consultants. But girl bosses are still bosses. Diverse capitalist technocrats will still extract value through exploitation. Yes, diversify the tech industry but that can only improve so much without democratization.
Broussard's individual accounts of her data reporting are fascinating and she does much to clarify to possibilities and limits of machine learning and artificial intelligence. Unfortunately, her broader criticism of the tech industry falls flat.
A solid primer into tech criticism composed through the author's experiences doing data and tech journalism, a historical account of Marvin Minsky's boy's club at MIT and an explanation of basic coding and machine learning to laymen (like me).
Her primary argument is to discredit "technochauvinism" — a belief that tech can solve all, often held by white male libertarian-minded tech bros. Tech criticism has evolved since this book's publication in 2018 making it slightly out of date. It's fundamental critiques are still valid but feel incomplete reading at the end of 2020 — what about questions on the private ownership of tech companies? How much tech actually exists in the companies we call "tech companies"? And are their innovations and growth primarily a function of exploiting regulatory gaps? *cough uber, doordash*
The problems that Broussard identifies in tech, framed through technochauvinsim, revolve around the white male identities of our tech overlords. The solution that she prescribes then is to diversify the tech workforce. It was the solution in the past when white scientists of the 1800s and 1900s refused to educate women and black people to contribute to research as "human computers" and diversity is the solution now as Apple watches were first released without being able to track menstruation.
The "overlord" problem is not so much as explored as the "white male" part. Taking Broussard's view in this book, a technological utopia is around the corner with the help of progressive hiring managers and some diversity and inclusion consultants. But girl bosses are still bosses. Diverse capitalist technocrats will still extract value through exploitation. Yes, diversify the tech industry but that can only improve so much without democratization.
Broussard's individual accounts of her data reporting are fascinating and she does much to clarify to possibilities and limits of machine learning and artificial intelligence. Unfortunately, her broader criticism of the tech industry falls flat.
July 29, 2019
A reasonable argument for scepticism about the grand claims and presumed neutrality of digital technology.
Broussard is a data journalist, and the book is at its best when covering the first part of her title. She does really well breaking down how computers work (I largely know, but a refresher is always useful) and the way bad data in can lead to bad data out. It's nice hearing from someone who really understands the tech, and who does a good job explaining how it translates into real world problems or simply isn't at the level tech futurists like to proclaim. I also really appreciated hearing how she uses programming to help support her journalism work, such as scanning records for irregularities in campaign financing. Having the curtains pulled back into those process, and what could and couldn't be achieved was really interesting.
The book is a little less persuasive in the more generically journalist parts. Long descriptions of buggy self-driving cars in 2009, or poor, tired nerds on hackathon bus trips add colour but little to the argument. There's also a wiff of moral panic at times. The sale of illegal drugs sold online is described as if it wasn't a vastly offline phenomena as well. The risk of 'smart' cars being dumb and killing people is treated as an unacceptable risk. (When the bigger ethical question is not if they will do some harm -every machine ultimately does- but if it will be far less than what we humans do to ourselves when at the wheel).
Broussard's term for the attitudes she doesn't like is 'technological chauvinism' which is similar to Evgeny Morozov's 'Technological solutionism' (from his excellent 2013 book, To Save Everything Click Here). The shift highlights Broussard's desire to link the question of how computers misunderstand the world, to how a certain segment of rich tech bros are sexist, sometimes racist and possibly worst of all in her eyes, libertarian. Algorithmic discrimination is a very real and important issue, so the argument is fine, but at times it felt more like a complaint about the people rather than the way they do or often do-not think about how their machines will act. It wasn't a big issue, but i've seen the argument made better elsewhere, and at times it seemed to distract from the book's main theme.
Still, this is an easy and well written book, from someone who is good at explaining how computers understand and often misunderstand the world.
Broussard is a data journalist, and the book is at its best when covering the first part of her title. She does really well breaking down how computers work (I largely know, but a refresher is always useful) and the way bad data in can lead to bad data out. It's nice hearing from someone who really understands the tech, and who does a good job explaining how it translates into real world problems or simply isn't at the level tech futurists like to proclaim. I also really appreciated hearing how she uses programming to help support her journalism work, such as scanning records for irregularities in campaign financing. Having the curtains pulled back into those process, and what could and couldn't be achieved was really interesting.
The book is a little less persuasive in the more generically journalist parts. Long descriptions of buggy self-driving cars in 2009, or poor, tired nerds on hackathon bus trips add colour but little to the argument. There's also a wiff of moral panic at times. The sale of illegal drugs sold online is described as if it wasn't a vastly offline phenomena as well. The risk of 'smart' cars being dumb and killing people is treated as an unacceptable risk. (When the bigger ethical question is not if they will do some harm -every machine ultimately does- but if it will be far less than what we humans do to ourselves when at the wheel).
Broussard's term for the attitudes she doesn't like is 'technological chauvinism' which is similar to Evgeny Morozov's 'Technological solutionism' (from his excellent 2013 book, To Save Everything Click Here). The shift highlights Broussard's desire to link the question of how computers misunderstand the world, to how a certain segment of rich tech bros are sexist, sometimes racist and possibly worst of all in her eyes, libertarian. Algorithmic discrimination is a very real and important issue, so the argument is fine, but at times it felt more like a complaint about the people rather than the way they do or often do-not think about how their machines will act. It wasn't a big issue, but i've seen the argument made better elsewhere, and at times it seemed to distract from the book's main theme.
Still, this is an easy and well written book, from someone who is good at explaining how computers understand and often misunderstand the world.
July 12, 2019
It was quite interesting how this technochauvinistic approach bleeds into other industries as well. As they say, not everything needs to be an app.
December 4, 2021
i did not actually finish this book as it made me want to dramatically end my life every time i glanced at it
December 4, 2020
Some great points about technochauvinism (didn't know that term before but it's perfect!) and unconscious biases, really points out how white, cis techbros basically dictate how algorithms work and then claim that they're objective, because "after all a computer has produced the data". It is also written in an accessible way, despite explaining a lot of difficult subject. Would recommend to everyone
May 18, 2023
Sobering look behind the (computer)screen of what you can and can’t expect from tech.
January 13, 2019
The author is skeptical that technology will save the world. It’s not the eschaton. She quit to become a journalist because there was no one else in computer science that looked like her? I’m skeptical of her reasons for why more women aren’t in STEM, but that’s a different topic. I also note she didn’t really explain why in the Titanic disaster more women survived because of the “women and children first” ethic that existed back then, which is gone today (why is it gone?, is my question). I’m also very skeptical of her notions of “social justice,” a term she never really defines, and is usually a pretense for more government and less liberty.
She writes, “Ultimately, everything we do with computers comes down to math, and there are limits to what we can (and should) do with it.” This is true. Humanity is more than math, and we can’t escape human nature with technology. She calls the flawed assumption that technology is always the solution technochauvinism. Then she equates them to Ayn Randian meritocracy, which misses how many people of leftist persuasion are in Silicon Valley and other tech areas.
Her discussion on general AI and narrow AI is good (narrow AI is what we have, general is the dream of Hollywood and science fiction). I appreciated this: “When we rely exclusively on computation for answers to complex social issues, we are relying on artificial intelligence. …It has no sentience, and it has no soul.” Also, all data comes down to people, since it is socially constructed, and made by people. Even data made by computers, because people make computers. A computer is not like a brain. If you take a piece out of a brain it will reroute pathways to compensate, and has an ability, under specific circumstances, to repair itself. A computer doesn’t. She pointed out that all the hype surrounding the algorithms in the AlphaGo victory ignored the humans worked over many years to create the training data. Is AlphaGo smart? Its designers are.
The discussion on technology is public schools, though one can differ with how to deal with this issue, she wants public education to succeed—but is this a dream or reality. There’s an interesting history on Marvin Minsky, considered the father of AI. But the gap in this work is there’s no in-depth exploration of Kurt Godel, John von Neumann, Claude Shannon, among others, who proved that AI needed an “oracle” outside of itself to prove certain axioms within the system. I hope the author reads George Gilder’s Life After Google for the real reason AI is a false eschaton. Also, she discusses price discrimination as a boogeyman, not noticing—and for a journalist this is an egregious oversight—that price discrimination is ubiquitous, and has many social welfare benefits, such as coupons, senior discounts, children’s prices, lower drug prices in lower-income countries, etc. She’s also very skeptical about autonomous cars, and recounts her experiences with them. Fair enough. But does that mean government shouldn’t allow them? I am all for permissionless innovation, as government is as flawed as we humans in foreseeing the future and “fixing problems.” I share her views on popular vs. good and how today’s society is mad for measurement. And I certainly agree with her that humans + machines outperform humans alone or machines alone, so-called human-in-the-loop systems. It’s why I’m not worried about AI “taking our jobs.” “No man is better than a machine, and no machine is better than a man with a machine,” according to Paul Tudor Jones, head of Tudor Investment Corp. I do believe George Gilder's book, Life After Google, is a far better explanation of why AI won't "eat the world," but there are some good points in this one.
She writes, “Ultimately, everything we do with computers comes down to math, and there are limits to what we can (and should) do with it.” This is true. Humanity is more than math, and we can’t escape human nature with technology. She calls the flawed assumption that technology is always the solution technochauvinism. Then she equates them to Ayn Randian meritocracy, which misses how many people of leftist persuasion are in Silicon Valley and other tech areas.
Her discussion on general AI and narrow AI is good (narrow AI is what we have, general is the dream of Hollywood and science fiction). I appreciated this: “When we rely exclusively on computation for answers to complex social issues, we are relying on artificial intelligence. …It has no sentience, and it has no soul.” Also, all data comes down to people, since it is socially constructed, and made by people. Even data made by computers, because people make computers. A computer is not like a brain. If you take a piece out of a brain it will reroute pathways to compensate, and has an ability, under specific circumstances, to repair itself. A computer doesn’t. She pointed out that all the hype surrounding the algorithms in the AlphaGo victory ignored the humans worked over many years to create the training data. Is AlphaGo smart? Its designers are.
The discussion on technology is public schools, though one can differ with how to deal with this issue, she wants public education to succeed—but is this a dream or reality. There’s an interesting history on Marvin Minsky, considered the father of AI. But the gap in this work is there’s no in-depth exploration of Kurt Godel, John von Neumann, Claude Shannon, among others, who proved that AI needed an “oracle” outside of itself to prove certain axioms within the system. I hope the author reads George Gilder’s Life After Google for the real reason AI is a false eschaton. Also, she discusses price discrimination as a boogeyman, not noticing—and for a journalist this is an egregious oversight—that price discrimination is ubiquitous, and has many social welfare benefits, such as coupons, senior discounts, children’s prices, lower drug prices in lower-income countries, etc. She’s also very skeptical about autonomous cars, and recounts her experiences with them. Fair enough. But does that mean government shouldn’t allow them? I am all for permissionless innovation, as government is as flawed as we humans in foreseeing the future and “fixing problems.” I share her views on popular vs. good and how today’s society is mad for measurement. And I certainly agree with her that humans + machines outperform humans alone or machines alone, so-called human-in-the-loop systems. It’s why I’m not worried about AI “taking our jobs.” “No man is better than a machine, and no machine is better than a man with a machine,” according to Paul Tudor Jones, head of Tudor Investment Corp. I do believe George Gilder's book, Life After Google, is a far better explanation of why AI won't "eat the world," but there are some good points in this one.
May 4, 2019
This book started out great but then quickly turned into a sermon bent on demonizing technology, men and especially the mixture of the two. I first heard about it on a podcast episode and decided to pick it up because I got the impression it would present a fresh perspective on the promise of tech.
In this relation, the book did deliver some reasonable criticism as opposed to the usual glorification of technology. The author raises some important issues mostly related to how technology reflects the flaws of its creators and how in spite of its potential to alleviate some of the pressing global problems it has contributed to a rise in inequality, harassment and unethical profiteering on behalf of a rather small share of people. Computers’ highly mathematical way of reasoning and their failure to accurately reflect on some of the less quantifiable aspects of human nature and the intricate social relationships it gives rise to are also discussed – as are some of the more obscure and less ethical episodes of the development of the tech industry as we know it.
All in all, the book gives some food for thought. It has plenty of valid arguments but it fails to sufficiently elaborate on any of them which made it feel somewhat unconvincing. This as well as some of the rhetoric which was borderline antagonistic are my main gripes with it.
In this relation, the book did deliver some reasonable criticism as opposed to the usual glorification of technology. The author raises some important issues mostly related to how technology reflects the flaws of its creators and how in spite of its potential to alleviate some of the pressing global problems it has contributed to a rise in inequality, harassment and unethical profiteering on behalf of a rather small share of people. Computers’ highly mathematical way of reasoning and their failure to accurately reflect on some of the less quantifiable aspects of human nature and the intricate social relationships it gives rise to are also discussed – as are some of the more obscure and less ethical episodes of the development of the tech industry as we know it.
All in all, the book gives some food for thought. It has plenty of valid arguments but it fails to sufficiently elaborate on any of them which made it feel somewhat unconvincing. This as well as some of the rhetoric which was borderline antagonistic are my main gripes with it.
July 4, 2019
“We need to stop fetishizing tech. We need to audit algorithms, watch out for inequality, and reduce bias in computational systems, as well as the tech industry.”
A tech enthusiast and coder, Broussard takes us on a number of adventures through modern tech, examining 1. standardized testing problems in Philadelphia & tech’s unsuccessful efforts with it 2. self-driving car developments over the course of a decade, and 3. a “startup bus” national hack-a-thon (which she ends up winning). She’s part data-journalist, part developer, part reporter, part historian, and most of all, a skeptic.
As an employee in an education-oriented Silicon Valley-funded organization, that’s what I appreciate most of all. Technochauvinism (as she calls it) runs rampant today; her attempts to bring tech down to Earth (as something humans built) is something I hope others in Silicon Valley will eventually heed.
A tech enthusiast and coder, Broussard takes us on a number of adventures through modern tech, examining 1. standardized testing problems in Philadelphia & tech’s unsuccessful efforts with it 2. self-driving car developments over the course of a decade, and 3. a “startup bus” national hack-a-thon (which she ends up winning). She’s part data-journalist, part developer, part reporter, part historian, and most of all, a skeptic.
As an employee in an education-oriented Silicon Valley-funded organization, that’s what I appreciate most of all. Technochauvinism (as she calls it) runs rampant today; her attempts to bring tech down to Earth (as something humans built) is something I hope others in Silicon Valley will eventually heed.
February 14, 2021
Overall, I really enjoyed this book. I thought it was well-written, with a good range of examples and clear language that just explained the jargon rather than avoiding it or skating past it. The rating has less to do with the quality of the book and moreso a lack of trust in my judgement - I already agreed with the author's main premise. Of course, as someone who already works in tech and someone that ticks boxes for multiple underrepresented groups - what she wrote isn't that surprising to me. If anything, it just encourages me to be more skeptical when hearing about some technology "that's going to change everything."
On a brighter note, this book does have me thinking about ways in which I can help demystify how some of the tech around us works for people in general.
On a brighter note, this book does have me thinking about ways in which I can help demystify how some of the tech around us works for people in general.
October 26, 2019
I pretty much gave up on page 40 when the author declared GIA is impossible because we didn't have a reliable test to prove that consciousness can exist. The good news is that this is an easy book to gloss over quickly, so I did. Some of the following essays highlight interesting points, but the author does not convince us that they have thought about any issue in much more detail than they gave to GIA.
April 2, 2020
I would recommend Sara Wachter-Boettcher's Technically Wrong as a better book to read.
This book wavers back and forth between being a technical primer on how technology works, a summary of examples where computers get it wrong, and a personal memoir. Any of these books probably would have been fine, but I didn't enjoy the combined effort.
This book wavers back and forth between being a technical primer on how technology works, a summary of examples where computers get it wrong, and a personal memoir. Any of these books probably would have been fine, but I didn't enjoy the combined effort.
October 25, 2021
Awful from a technical perspective, did not finish the book. The person who proclaims herself as a technical journalist in this narrow and specific topic should be more attentive to the details illustrating her own thesis. Otherwise, the result will be the opposite.
March 21, 2021
If you have to go online for any reason, or communicate with someone, then algorithms and artificial intelligence (AI) are necessarily part of the encounter. If you're anything like me, then these encounters can be bereft of any logic, or sensemaking, possibly because you don't fit the presumptions of the person who's designed the program you're encountering, for any reason at all.
Meredith Broussard seeks to explain the world of AI, presumptions about intelligent computers and so on. She does this from the perspective of what's called a data journalist, a label I hadn't encountered before, as well as her personal experience as a teacher and also a programmer in a world she describes as adhering to technochauvinism.
What she means by that is not only that this field is dominated by males, but that people who might be different in some way – women obviously, but also minority groups, the poor and so on, can be and are excluded from consideration.In addition, she points out the unthinking acceptance of technology as a means of solving all problems.
There are all kinds of issues and topics addressed in this book, from unthinking acceptance of the "trolley problem" (problematic anyway to me) to the improbability of self-driving cars, that reading a book is much more effective than reading from a screen, and is preferred by students [research at Deakin University pointed this out quite a few years ago] and how students from poor schools are discriminated against regarding resources electronic and otherwise (poor internal systems also being a reason).
A feature of this book is that Broussard both demonstrates and e3xplains what coding/programming is and how it works. I didn't follow all her instructions, as the detail on the page overwhelmed me a bit at times, but I enjoyed the explanations, as well as the openness of the way she gives information, including personal experiences, and also her easy to read writing style which made it seem as though she was in the room chatting with you.
Meredith Broussard seeks to explain the world of AI, presumptions about intelligent computers and so on. She does this from the perspective of what's called a data journalist, a label I hadn't encountered before, as well as her personal experience as a teacher and also a programmer in a world she describes as adhering to technochauvinism.
What she means by that is not only that this field is dominated by males, but that people who might be different in some way – women obviously, but also minority groups, the poor and so on, can be and are excluded from consideration.In addition, she points out the unthinking acceptance of technology as a means of solving all problems.
There are all kinds of issues and topics addressed in this book, from unthinking acceptance of the "trolley problem" (problematic anyway to me) to the improbability of self-driving cars, that reading a book is much more effective than reading from a screen, and is preferred by students [research at Deakin University pointed this out quite a few years ago] and how students from poor schools are discriminated against regarding resources electronic and otherwise (poor internal systems also being a reason).
A feature of this book is that Broussard both demonstrates and e3xplains what coding/programming is and how it works. I didn't follow all her instructions, as the detail on the page overwhelmed me a bit at times, but I enjoyed the explanations, as well as the openness of the way she gives information, including personal experiences, and also her easy to read writing style which made it seem as though she was in the room chatting with you.
March 19, 2019
A joyful read for the exciting moment where every field has taken a computational turn. Meredith Broussard warns us about the Techno-chauvinists that try to apply “digital” to every aspect of life resulting in a tremendous amount of poorly designed technology. As the vaunted “Artificial Intelligence” is nothing more than “narrow AI”, appropriate to speed the process up, but inadequate when replicating inequality and biases from its very-human developers. An interesting chapter describes how the last decades of IT Technology have been satelliting around a small non-diverse group of elites (very white and very male) much more interested in whacky creative ideas than gender politics, safety, or ethical usage of technology. Because we tend to assume that when people are expert in something, their expertise extends to other areas as well. But being good with computers is not the same than being good with people. And I entirely coincide with the author’s pitch of balancing genius and brilliance, with empathy and hard work. Other ideas related to why the autonomous car is not a very good idea, or how some “technocharlatans” are not honest about the debt of what it takes to keep technology working. Worth the book ride!
August 28, 2020
This was excellent. I learned a lot and got mad a lot and learned a very important word: technochauvanism-the belief that tech is always the answer. Of course this belief also has an impact on the people left out of tech-basically anyone who isn’t a white, straight cis male.
There are very important stories in here about classroom textbooks and standardized testing, the Titanic and predicting who would die in the disaster and why you can never predict something with 100% confidence, why self-driving cars will never work and why we won’t want to drive them anyway, and an interesting history of the tech movement and why we have the current white, straight cis male dominated field of tech.
Highly recommend this book. Not too technical for people who have never programmed anything-heck you can even do some beginner programming if you want because the author teaches you how.
Tech is not going to save the world, people are.
There are very important stories in here about classroom textbooks and standardized testing, the Titanic and predicting who would die in the disaster and why you can never predict something with 100% confidence, why self-driving cars will never work and why we won’t want to drive them anyway, and an interesting history of the tech movement and why we have the current white, straight cis male dominated field of tech.
Highly recommend this book. Not too technical for people who have never programmed anything-heck you can even do some beginner programming if you want because the author teaches you how.
Tech is not going to save the world, people are.
November 17, 2025
Frim a content perspective: A worthwhile read with all of the AI hype of today. Despite what it might fee like when typing out questions to ChatGPT, computers don’t, and can’t, think like people. Broussard does a good job of highlighting the shortcomings of computers and the danger of technochauvism (believing that technology always = better, fairer, etc). With computers it always comes back to 0s, 1s, and math. While AI might get answers right, it’s often for the wrong reasons because it’s not thinking and analyzing like a human, so its answers and use should be filtered with this understanding.
From a writing perspective: Each chapter had a personal anecdote that doesn’t connect to the previous chapter or rest of the book very directly so it felt a little disjointed at times.
3.5 would be a better rating.
January 21, 2026
Probably the best technical overview of AI I’ve found for people who don’t know how to code.
A little outdated now, but still useful for general concepts. Probably not useful for people already familiar with how AI works.
A little outdated now, but still useful for general concepts. Probably not useful for people already familiar with how AI works.
July 16, 2019
some thoughts to come, tomorrow
August 2, 2025
Although I'm basically in sympathy with the thrust of Broussard's arguments about how AI has been over-sold by a cadre of businessmen and technologists, collectively overly entranced with their supposed personal genius, the reality is that this book is now somewhat dated by the galloping effort to ram AI-technology down the throat of world society since 2017.
Also, the acts of Peter Thiel, Elon Musk, and their ilk of late have thoroughly reinforced the threat of their authoritarian attitudes that Broussard alludes to in this book. Which suggests to me that Broussard's "More than a Glitch" is now the salient reading choice.
Actual rating: 3.5.
Also, the acts of Peter Thiel, Elon Musk, and their ilk of late have thoroughly reinforced the threat of their authoritarian attitudes that Broussard alludes to in this book. Which suggests to me that Broussard's "More than a Glitch" is now the salient reading choice.
Actual rating: 3.5.
April 18, 2023
This book is a primer on ethical considerations that need to be addressed surrounding computation, AI, and machine learning. It focuses on areas where it has significant material impacts, and sometimes even to the extent of costing lives acting in seemingly banal everyday action. To this extent this book is great! It raises questions that every computer scientist, app developer, software designer, etc should consider.
Cars can kill. Automated cars kill differently. Journalists can incriminate; computer assisted journalists incriminate differently.
In many cases, Broussard is somewhat cynical of technology despite being a technologist herself. She's keenly aware of issues of technology in the now, and distrusts speaking ambiguously of the future. Perhaps this is mostly to get her stylistic point across (at a cost of getting to involved with the "science" of computer science) but in some ways I found this pedagogy leaving me wanting.
This book provides reasons to avoid these issues, but with no real skill set to avoid them. I'm provided questions of morality without grounded locations to investigate the ethics of these concerns more carefully. I'm told not to think of processes to avoid risks required design a better future, but rather to steep in the fear of present failures.
In some ways this book just misses its central goal: explaining "how computers misunderstand the world" because it mistakes computer science for software development. I feel as though I should have walked away with a better idea of what computers can do and what they can't. Instead I'm left thinking that optimistic technologists as "technochauvinists." That is, it doesn't address what computers do; it addresses problematic optimisms of technologists. There is no careful exploration of what it means for a computer to "think". She simply writes off "computational thinking" as not being "sentient" or "neurological", neither of which are her area of expertise it seems (e.g. cognitive psychology, neuroscience, philosophy of mind, etc). Having an entire chapter dedicated to justifying this in detail would have been immensely useful! Also there's no exploration of emergent properties of ecological networks of computers, such as in self-driving cars, which perhaps could be argued to have agencies in themselves even if they don't "think".
Largely her exploration of limitations are in classic reductionist properties from intuitions. There's no consideration of technological politics of interconnectedness of data such as one might see in _Empire_ or Terranova's book _Network Culture_. This is truly a different paradigm worthy of consideration from Broussard's reductionist explanations, which pose their own risks.
While certainly nobody should put all their investments in an uncertain technologized future, many of the places of criticism here are people investing a great deal of action into making technology avoid certain pitfalls despite the fact that they (as computer scientists and engineers are notoriously well known for) are terrible at expressing the legitimate concerns of technology in curated, ethically elegant speech. The fact that an automated car designer-in-training sees a huge risk as a "computer bug" is their training which is required. Certainly in thinking of the "bug", it is a "bug" because it risks life. It doesn't entirely dehumanize the problem. When we tell our children that having the cold is "just a bug" we aren't dehumanizing our children to the functional error of a "bug," are we?
In total, this book is a great primer for students who are already in route to do this kind of work. For those that aren't connected to this subject already, this book generates more irritation and fear on topics that are not fully explained enough for an introductory critical thinker to explore.
Cars can kill. Automated cars kill differently. Journalists can incriminate; computer assisted journalists incriminate differently.
In many cases, Broussard is somewhat cynical of technology despite being a technologist herself. She's keenly aware of issues of technology in the now, and distrusts speaking ambiguously of the future. Perhaps this is mostly to get her stylistic point across (at a cost of getting to involved with the "science" of computer science) but in some ways I found this pedagogy leaving me wanting.
This book provides reasons to avoid these issues, but with no real skill set to avoid them. I'm provided questions of morality without grounded locations to investigate the ethics of these concerns more carefully. I'm told not to think of processes to avoid risks required design a better future, but rather to steep in the fear of present failures.
In some ways this book just misses its central goal: explaining "how computers misunderstand the world" because it mistakes computer science for software development. I feel as though I should have walked away with a better idea of what computers can do and what they can't. Instead I'm left thinking that optimistic technologists as "technochauvinists." That is, it doesn't address what computers do; it addresses problematic optimisms of technologists. There is no careful exploration of what it means for a computer to "think". She simply writes off "computational thinking" as not being "sentient" or "neurological", neither of which are her area of expertise it seems (e.g. cognitive psychology, neuroscience, philosophy of mind, etc). Having an entire chapter dedicated to justifying this in detail would have been immensely useful! Also there's no exploration of emergent properties of ecological networks of computers, such as in self-driving cars, which perhaps could be argued to have agencies in themselves even if they don't "think".
Largely her exploration of limitations are in classic reductionist properties from intuitions. There's no consideration of technological politics of interconnectedness of data such as one might see in _Empire_ or Terranova's book _Network Culture_. This is truly a different paradigm worthy of consideration from Broussard's reductionist explanations, which pose their own risks.
While certainly nobody should put all their investments in an uncertain technologized future, many of the places of criticism here are people investing a great deal of action into making technology avoid certain pitfalls despite the fact that they (as computer scientists and engineers are notoriously well known for) are terrible at expressing the legitimate concerns of technology in curated, ethically elegant speech. The fact that an automated car designer-in-training sees a huge risk as a "computer bug" is their training which is required. Certainly in thinking of the "bug", it is a "bug" because it risks life. It doesn't entirely dehumanize the problem. When we tell our children that having the cold is "just a bug" we aren't dehumanizing our children to the functional error of a "bug," are we?
In total, this book is a great primer for students who are already in route to do this kind of work. For those that aren't connected to this subject already, this book generates more irritation and fear on topics that are not fully explained enough for an introductory critical thinker to explore.
July 20, 2024
I enjoyed this read, and I'm afraid I disagree with the sentiment of other reviewers that it's more focused on journalism than the technology itself and its impacts. Yes, the author is informed by this lens, but the book is about tech, not journalism. For anyone interested in a counter-narrative to technology as a solution for all (looking at you, accelerationism movement), this is a useful read from an experienced technology who loves tech but teaches us to maintain caution and a human-in-the-loop.
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