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mandyc627's review against another edition
4.0
Well written. I took many notes as I read related to the injustices created or enhanced by WMDs, especially those in education. This book serves as a great reminder that we need to remain critical and always keep our eyes wide open when big data systems are in play (e.g., value-added systems of evaluation) and not take any information produced by them out of context.
isering's review against another edition
3.0
This was interesting, but it mainly covered the downsides and not the upsides. She also fell very much into the framing trap - recommending algorithms only be used for good - but discounts are the flip side to surcharges, and who should assess what counts as 'good'? Price discrimination for example can be seen as beneficial by some.
legodetective's review against another edition
5.0
We are all being manipulated by social media and the internet. Algorithms are unfair and not transparent. They reinforce the systematic oppression that is plagueing our country. Read this book!!
arodriguez28's review against another edition
3.0
Informative but really repetitive, sad how so little has changed positively in 8 years
cpope9's review against another edition
3.0
I think that the thesis of this book is becoming increasingly more relevant as time moves forward. I think that the ideas in this book need to be considered as society and democracy move throughout the digital age. Without consideration, institutions and society will be increasingly tested by grosser inequalities and human rights abuses. Very intriguing premise and topic underpinning the darker and flawed practical aspects and applications of big data analytics.
2024: 3.5 stars. oh how things have only gotten worse. This book is still eye opening and relevant but things have only been further devolved since I first read this. With part of my career leading a data team, I see first hand how small details and assumptions in modeling can dramatically paint different pictures about reality. When used out of context, those models can inadvertently create or perpetuate systemic inequity, disadvantage, and discrimination. For anyone who doesn’t think “systemic racism” isn’t real, read this or talk to any corporate quant about their models’ assumptions about race. This book does a decent job at explaining key areas of companies or institutions using data models in ignorance, negligence, or aggression to cause harm for personal benefit without regard to broader social or system impacts. This still happens everywhere and I’m not sure it’s avoidable or regulatable at this point. But the author does a great job to paint a clear picture of the issue at hand.
However, what I really wish was here was some synthesis of the research/ case studies that created a theory/checklist/definition of attributes of the WMDs she discusses. This book is mostly “data can be used badly. Here’s 30 examples from 15 different fields. We need to fix this.” I appreciate the examples, but have each example tie into a grander “this is how you can tell your data model is harmful” or “this is how to know if your data is being used against you” type of summary. It would serve as a much more useful and testable conclusion than just “big data…bad!!!!!?”
Also would’ve loved some cases of big data being used well or productively as a counterexample to the broader theory that should have been posited here. But mostly just the bad stuff…which is still interesting but there’s so much more good intentions that turn into ignorantly or negligently problematic models fueling than initial or intentional malice behind the days…and that needs to be further explored (but isn’t).
2024: 3.5 stars. oh how things have only gotten worse. This book is still eye opening and relevant but things have only been further devolved since I first read this. With part of my career leading a data team, I see first hand how small details and assumptions in modeling can dramatically paint different pictures about reality. When used out of context, those models can inadvertently create or perpetuate systemic inequity, disadvantage, and discrimination. For anyone who doesn’t think “systemic racism” isn’t real, read this or talk to any corporate quant about their models’ assumptions about race. This book does a decent job at explaining key areas of companies or institutions using data models in ignorance, negligence, or aggression to cause harm for personal benefit without regard to broader social or system impacts. This still happens everywhere and I’m not sure it’s avoidable or regulatable at this point. But the author does a great job to paint a clear picture of the issue at hand.
However, what I really wish was here was some synthesis of the research/ case studies that created a theory/checklist/definition of attributes of the WMDs she discusses. This book is mostly “data can be used badly. Here’s 30 examples from 15 different fields. We need to fix this.” I appreciate the examples, but have each example tie into a grander “this is how you can tell your data model is harmful” or “this is how to know if your data is being used against you” type of summary. It would serve as a much more useful and testable conclusion than just “big data…bad!!!!!?”
Also would’ve loved some cases of big data being used well or productively as a counterexample to the broader theory that should have been posited here. But mostly just the bad stuff…which is still interesting but there’s so much more good intentions that turn into ignorantly or negligently problematic models fueling than initial or intentional malice behind the days…and that needs to be further explored (but isn’t).
jegutman's review against another edition
5.0
This book deserves a lot of credit for discussing an important issue that doesn't get enough attention. I don't agree with many of the points made in the book, especially some of the recommended solutions, but it's still an important set of issues to discuss.
If you're interested in the world of big data, data science, or any form of data modeling, this book is a valuable tool to understand how the use of data models have consequences that are easy to miss but that are important to consider.
If you're interested in the world of big data, data science, or any form of data modeling, this book is a valuable tool to understand how the use of data models have consequences that are easy to miss but that are important to consider.
laraph's review against another edition
5.0
Excellent coverage of a selection of algorithms that codify racism and discrimination against the poor and mentally ill, obscure accountability, and endanger democracy. She insists on an ethical exclusion of all proxies for race, poverty, health etc in model building and training. I'd push further for mandatory testing of all results for correlations with discriminatory markers and, should they exist, models would have to retrain while enforcing that such correlations _vanish_. Yes, this reduces "accuracy" but if we are to dismantle our reality of discrimination, we must lose that bit of "accuracy" that encodes all too real discrimination.
Only complaint: the book was too short!
Only complaint: the book was too short!