Welcome to Litsy!

This is an interesting look at data science and how it can be used and misused, based on your perspective. Fascinating. But not entirely surprising.

#BookSpinBingo @TheAromaofBooks

Awesome intro to how data models can inadvertently (or advertently) screw individuals and society over. Was going to be a great prelude to a data training but then the rona hit.

Leapt to the top of my TBR list as soon as I saw it.

I'm currently in the section of the book covering the us news and world report impact on college admissions. I want to dig more into the industry that rose around this, anyone know of further reading on this? This feels similarly systemic as the tax prep market and the inefficiencies in health care.

Great documentary and the author of this book is featured!

⭐ ⭐This book is a disappointing missed opportunity. I understand and share some of the author's concerns. We need discussions about how to manage these models, how to update them more frequently, how to build self correcting & self identifying attributes that help us identify & manage the downsides. This book should have been that discussion. It was not.
Full review: https://beta.thestorygraph.com/book_reviews/2170a799-10f1-44a8-95a4-3fbc732e4005

Frightening, but not surprising. Imagine how much better the world would be if Big Data was used for good, rather than for profit.....

In other news, I don‘t have a great deal to do today and am really tired, so it‘s my intention to read, drink tea and nap after having a bath. I‘m looking forward to this a lot! 🛁 📚 ☕️

This is the way they measure student progress in the U.K.: how well students achieve in comparison to how well they *should* achieve. I have such issue with this for many reasons, but am happy that I don‘t get given a ‘score‘ as a result!

I‘ve been really into the Dubner/Levitt Freakonomics series, so I thought this one might be interesting.

In other news, Alan has made a friend who keeps coming to call for him. When the door is on the latch, they bump noses through the gap and softly pat each other with their stumpy hands 😍😍😍

Awesome intro to how data models can inadvertently (or advertently) screw individuals and society over. Was going to be a great prelude to a data training but then the rona hit.

I enjoyed this book but thought it was a lot of information to take in at one time. It was challenging to read in some cases because I work as a data scientist and I have to wonder if anything I have created in the last three years has contributed to these new weapons. This is an important read for anyone conducting research, or data collection. #NFNov

A repetitive look into the dangers of blindly trusting algorithms. For the full review, please visit http://benjamin-m-weilert.com/index.php/2019/11/20/book-weapons-of-math-destruct...

Points have been counted.

Please Note: I have to work two jobs tomorrow so I won't count points tomorrow, so I'll count them up on Thursday after work.

#NFNov

So @Clwojick and I have switched for this week! So she will count points for this week and I'll be here to answer questions and tell you all, how cool your Non Fiction Reads are!

I am working my way through the tagged book. Trying hard to understand WMDs as they apply to finance.

#NFNov

#NFNov Hey Y'all. I am very sorry but I won't be counting points tonight. My partner is leaving for IN and tonight is our last night together. But I'll count all points for the two days tomorrow. Sorry!

“Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that‘s something only humans can provide.”

For a while now I have been struggling with the global trend of data-driven decision making. Rather than relying on rhetoric and power, in many aspects of our lives, we now put our faith in algorithms and supposed neutrality of models. This book gives me hope that I am not alone in my worries.

Over half-way, this book is getting more and more relevant in the data-driven economy and politics.

Learned some stuff I didn't know, and just a good reminder about how data can be used for good and evil, algorithms can have unintended terrible consequences, and always check the methodology whenever possible (and ideally, it should always be possible).

#nonfiction #datascience

It‘s interesting, but since my grad degree is in information science, and I‘m familiar w/ most of the topics discussed, a lot of it wasn‘t very surprising or new to me. I think the author was really trying too hard with the whole WMD, but I appreciated how she made things accessible and easy to read. I think the main takeaway is that algorithms are only as good as their creators and need constant updates and oversight. 2/5 stars

Just have to point out that it‘s funny she claims that we shouldn‘t place all the blame on the ranking, but then does. I want to see more data, and actual references, indicating the role the US News WMD, as she calls it, had on the rise of college as a necessity to life. Give me data!

This is something I try to teach my students every class. Algorithms, which control the databases we use in our research, aren‘t perfect because they are made by flawed humans. The faith people put into algorithms is so scary

Thank you so much @AmyG for the wonderful #thankfulforbooks gifts. I am so excited for these and the chocolate was super thoughtful. Thank you! @JamieLou this was a lovely swap, thank you 😊

And with that I'm halfway there! WMD was a must read. None of it was exactly new info to me, but it was always more of a feeling and general cultural awareness. Now I have data backing it up. I finished American War but it was far more grim than I'd imagined. Very dark. I've realized that a lot of the speculative fiction I read ends on a high note. I think I like it that way.

Yes!!!

I've got this one on audio and as an ebook to help me focus on what promises to be a fascinating and worrying look at big data

According to a watchdog group, the Consumer Federation of America, Allstate analyzes consumer and demographic data to determine the likelihood that consumers will shop for lower prices. If they aren't likely to, it makes sense to charge them more. And that's just what Allstate does.

The algorithms would make sure that those deemed losers would remain that way. A lucky minority would gain even more control over the data economy, raking in outrageous fortunes and convincing themselves all the while that they deserved it.

Highly recommended if you want to better understand how firms target you in their advertising, lending, and hiring efforts.

I‘m always leery of statistics if I don‘t know how the numbers were derived. O‘Neil makes it easy to understand how stats and algorithms can mislead and be used for all kinds of dishonest practices that hurt large groups of society. In the era of fake news, this is a must-have for any beginner‘s BS Detection Kit.

What do you think?

O‘Neil explains how algorithms perpetuate the class divide, racist policies, and contribute to the cycle of poverty. I knew for-profit colleges were bad, but was shocked by how outrageous their tactics were in targeting those who were vulnerable. An eye-opening look on how reliance on data has unintended consequences. Covers topics from stop and frisk to college loans to Facebook. Good choice for #readharder #nonfictionabouttech #litsyatoz #O

It was because of hearing Cathy O‘Neil on the 99% Invisible podcast that I sought out her book. You can listen to the episode here: https://99percentinvisible.org/episode/the-age-of-the-algorithm/

O‘Neil, a mathematician passionate about fairness, accountability & democracy, gets riled up about the misuse of data & predictive models & that makes for good #audiobook listening. Big data gets excellent teachers fired, makes physically fit people pay higher health premiums, a driver with drunk driving convictions pay lower premiums than a driver with a clean record but a poor credit rating & credit ratings are inaccurate anyway. 😠

I‘m surprised by one thing after another in this audiobook. Just learned that the BMI is a bogus measurement.

A few skippable parts but overall a pretty interesting listen.

Paraphrased: If you look at a student's grade level test scores from year to year, they are as random as M&Ms thrown on a graph, yet analyses on these student test scores are used to score teachers and fire them. #nonfiction #datascience #monitoring #surveillance #algorithm #code #program #math #education

I already got the gist of this book before I started listening, but I listened to it twice anyway so I could give it lots of thought. Food for thought: most job applicants are filtered out based on zip codes, social media, personality tests, and credit scores before they ever see a live person. It's illegal to filter by race/religion/disability, but what's next? Health scores? The talented exceptions to the rule are hurt the most by algorithms.

I finally figured out how to borrow audiobooks from the library. Listening to this now. #datascience #programming

Whew! It feels like I have been away from Litsy forever, but it's really only been a week or so. This is my last week of my graduate program, so I'm scrambling to read all the books and write all the papers. Oh, and I'm moving this weekend. And working tomorrow.

Anyway, I really enjoyed this book and wanted to spend more time with it, but someone else is waiting for it at the library. I'll have to ask for it again... later 😆

I thought this cover was an interesting take on the usual #SkullAndCrossedBones. #WhatAWayToLive #PiratesLife

2017 #61 nonfiction audio read this. Understand why we are being lied to even by people who believe they are telling the truth. The most brutal accounting of the economic system. The end got a little to preachy for me but the first 3/4 was super insightful and solid.

Really interesting look at how data metrics can cause further marginalization and harm. It's a pretty easy read, and she brings together facts in interesting and new ways.

Why I read it: NBA nonfiction longlist 2016

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 hard wired. It generated 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.

Just stating this and it's already grabbed me.