Chapter 14: The Future of Data Systems

Reference 1 in Chapter 14: What If Data Is a Bad Idea?

David Schmudde

2024

schmud.de

Reference 2 in Chapter 14: ACM Code of Ethics and Professional Conduct

Association for Computing Machinery

2018

acm.org

Reference 3 in Chapter 14: Making Hard Choices: The Quest for Ethics in Machine Learning

Igor Perisic

2016

linkedin.com

Reference 4 in Chapter 14: Algorithm Writers Need a Code of Conduct

John Naughton

2015

theguardian.com

Reference 5 in Chapter 14: Ethical AI Is Not About AI

Deborah G. Johnson and Mario Verdicchio

Communications of the ACM 2023

cacm.acm.org doi

Reference 6 in Chapter 14: 'Good' Isn’t Good Enough

Ben Green

NeurIPS Joint Workshop on AI for Social Good 2019

benzevgreen.com PDF

Reference 7 in Chapter 14: Ethics as a Participatory and Iterative Process

Marc Steen

Communications of the ACM 2023

cacm.acm.org doi

Reference 8 in Chapter 14: What Happens When Big Data Blunders?

Logan Kugler

Communications of the ACM 2016

cacm.acm.org doi

Reference 9 in Chapter 14: Algorithms and the Criminal Justice System: Promises and Challenges in Deployment and Research

Miri Zilka

University of Cambridge Security Seminar Series 2023

cl.cam.ac.uk

Reference 10 in Chapter 14: Welcome to Algorithmic Prison

Bill Davidow

2014

theatlantic.com

Reference 11 in Chapter 14: They're Watching You at Work

Don Peck

2013

theatlantic.com

Reference 12 in Chapter 14: Is an Algorithm Any Less Racist Than a Human?

Leigh Alexander

2016

theguardian.com

Reference 13 in Chapter 14: How a Machine Learns Prejudice

Jesse Emspak

2016

scientificamerican.com

Reference 14 in Chapter 14: Joint Statement on Enforcement Efforts Against Discrimination and Bias in Automated Systems

Rohit Chopra, Kristen Clarke, Charlotte A. Burrows and Lina M. Khan

2023

ftc.gov PDF

Reference 15 in Chapter 14: The Moral Economy of Tech

Maciej Cegłowski

2016

idlewords.com

Reference 16 in Chapter 14: Artificial Intelligence in Healthcare Is Racist

Greg Nichols

2020

zdnet.com

Reference 17 in Chapter 14: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Cathy O'Neil

2016

Reference 18 in Chapter 14: Make Algorithms Accountable

Julia Angwin

2016

nytimes.com

Reference 19 in Chapter 14: European Union Regulations on Algorithmic Decision-Making and a ‘Right to Explanation

Bryce Goodman and Seth Flaxman

ICML Workshop on Human Interpretability in Machine Learning 2016

arxiv.org

Reference 20 in Chapter 14: A Review of the Data Broker Industry: Collection, Use, and Sale of Consumer Data for Marketing Purposes

United States Senate Committee on Commerce, Science, Transportation and 3 others

2013

commerce.senate.gov PDF

Reference 21 in Chapter 14: Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market

Stephanie Assad, Robert Clark, Daniel Ershov and Lei Xu

Journal of Political Economy 2024

economics.yale.edu PDF doi

Reference 22 in Chapter 14: Thinking in Systems: A Primer

Donella H. Meadows and Diana Wright

2008

Reference 23 in Chapter 14: Listening to a 'big data’/‘data science' talk. Mentally translating 'data' to '‘surveillance': ‘…everything starts with surveillance…'

Daniel J. Bernstein

2015

x.com

Reference 24 in Chapter 14: Why Software Is Eating the World

Marc Andreessen

2011

a16z.com

Reference 25 in Chapter 14: Internet of Things’ Security Is Hilariously Broken and Getting Worse

J. M. Porup

2016

arstechnica.com

Reference 26 in Chapter 14: Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World

Bruce Schneier

2015

schneier.com

Reference 27 in Chapter 14: Nothing to Hide

The Grugq

2016

grugq.tumblr.com

Reference 28 in Chapter 14: FTC Takes Action Against General Motors for Sharing Drivers’ Precise Location and Driving Behavior Data Without Consent

Federal Trade Commission

2025

ftc.gov

Reference 29 in Chapter 14: Deep-Spying: Spying Using Smartwatch and Deep Learning

Tony Beltramelli

2015

arxiv.org

Reference 30 in Chapter 14: Big Other: Surveillance Capitalism and the Prospects of an Information Civilization

Shoshana Zuboff

Journal of Information Technology 2015

papers.ssrn.com doi

Reference 31 in Chapter 14: Beyond Consent: Improving Data Protection Through Consumer Protection Law

Michiel Rhoen

Internet Policy Review 2016

policyreview.info doi

Reference 32 in Chapter 14: Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016

Official Journal of the European Union 2016

eur-lex.europa.eu

Reference 33 in Chapter 14: What Is the ‘Legitimate Interests’ Basis?

UK Information Commissioner's Office

ico.org.uk

Reference 34 in Chapter 14: How a Handful of Tech Companies Control Billions of Minds Every Day

Tristan Harris

TED2017 2017

ted.com

Reference 35 in Chapter 14: Consequences of an Insightful Algorithm

Carina C. Zona

GOTO Berlin 2016

youtube.com

Reference 36 in Chapter 14: Should We Treat Data as Labor? Moving Beyond ‘Free

Imanol Arrieta Ibarra, Leonard Goff, Diego Jiménez Hernández and 2 others

American Economic Association Papers Proceedings 2018

aeaweb.org PDF

Reference 37 in Chapter 14: Data Is a Toxic Asset, So Why Not Throw It Out?

Bruce Schneier

2016

schneier.com

Reference 38 in Chapter 14: Data is not toxic—which implies no benefit—but rather hazardous material, where we must balance need vs. want

Cory Scott

2016

x.com

Reference 39 in Chapter 14: Data Is The New Uranium—Incredibly Powerful And Amazingly Dangerous

Mark Pesce

2024

theregister.com

Reference 40 in Chapter 14: Mission Creep: When Everything Is Terrorism

Bruce Schneier

2013

schneier.com

Reference 41 in Chapter 14: Big Data: Big Power Shifts?

Lena Ulbricht and Maximilian von Grafenstein

Internet Policy Review 2016

policyreview.info doi

Reference 42 in Chapter 14: Facebook and Google: Most Powerful and Secretive Empires We've Ever Known

Ellen P. Goodman and Julia Powles

2016

theguardian.com

Reference 43 in Chapter 14: The World Is Choking on Digital Pollution

Judy Estrin and Sam Gill

2019

washingtonmonthly.com

Reference 44 in Chapter 14: Regulating Mass Surveillance as Privacy Pollution: Learning from Environmental Impact Statements

A. Michael Froomkin

University of Illinois Law Review 2015

repository.law.miami.edu

Reference 45 in Chapter 14: The Early Impact of GDPR Compliance on Display Advertising: The Case of an Ad Publisher

Pengyuan Wang, Li Jiang and Jian Yang

Journal of Marketing Research 2023

openreview.net doi

Reference 46 in Chapter 14: Don’t Be Fooled by Meta’s Fine for Data Breaches

Johnny Ryan

The Economist 2023

economist.com

Reference 47 in Chapter 14: Your Data Footprint Is Affecting Your Life in Ways You Can’t Even Imagine

Jessica Leber

2016

fastcompany.com

Reference 48 in Chapter 14: Haunted by Data

Maciej Cegłowski

2015

idlewords.com

Reference 49 in Chapter 14: You Are Not What You Read: Librarians Purge User Data to Protect Privacy

Sam Thielman

2016

theguardian.com

Reference 50 in Chapter 14: It's a cliché that people get into tech to 'change the world.' So then, you have to actually consider what the impact of your work is on the world. The idea that you can or should exclude societal and political discussions in tech is idiotic. It means you're not doing your job

Jez Humble

2021

x.com