Reading list#
What is this Reading list?
The reading list contains all the suggestions for what to read or watch, contributed by members of the Data Ethics Club community. We choose from this list what to vote on to discuss for future meetings. We’d also love it if this reading list was useful for other people in starting their own journal clubs, or for any other purpose.
We would love suggestions for new reading materials, or better ways to categorise them (open an issue or email us).
Key
🎤 = audio only
🕰️ = waiting for paper to drop
✅ = we’ve discussed it
🔒 = Not Open access
📺 = Watching/Listening material
[1] = A longer piece of work, we’d need to choose a chapter or section.
[2] = A shorter piece of work, perhaps to combine with something else
Note: reading materials appear once in the category we felt they fit best.
What is data ethics?#
Introduction to Research Ethics - suggested by @leriomaggio
Algorithmic Injustices: Towards a relational ethics - suggested by @RobertArbon, ✅ 3rd March 2021
📺 21 Fairness Definitions and their politics - suggested by @NatalieThurlby
📺 AI, Ain’t I a Woman? - poem2 - suggested by Valentina Ragni
Ethics and Policy Class Materials by Casey Fiesler1 - suggested by @NatalieThurlby #100
Data and Society: A Critical Introduction - suggested by reviewer for DEC paper [1]
Moral philosophy for data science#
Engineers’ Moral Responsibility: A Confucian Perspective1 - suggested by @NatalieThurlby #101
Data Science, Meaning and Diversity - suggested (and written!) by Ismael Kherroubi Garcia
Forbidding Nasty Knowledge - On the Use of Ill-gotten Information - suggested by Mia Mace
The nature of data#
The Missing Datasets Project - #1 - suggested by @ninadicara
Dataism is our new God - suggested by @NatalieThurlby - ✅ 31st March 2021
Challenging racism in the use of health data - suggested by @Lextuga007
Living in the Hidden Realm of AI - suggested by @vairylein, see #54 ✅
Algorithmic decision making#
Review into bias in algorithmic decision making1 - suggested by @nataliethurlby
Suggested excerpt: executive summary - ✅ 20th January 2021
Related: Accountability for algorithms: a response to the CDEI review into bias in algorithmic decision-making - suggested by Tom Whittaker
Algorithms Of Oppression1 - suggested by @mtwest2718
Suggested excerpt: Social Inequality Will Not Be Solved By An App
Automating Inequality1 - suggested by Valentina Ragni and @mtwest2718
What does it mean to ‘solve’ the problem of discrimination in hiring? - suggested by @edwinrobots
Fairness and utilization in allocating resources with uncertain demand - suggested by @edwinrobots
Coded Bias film - suggested by @NatalieThurlby and Paula Andrea Martinez ✅
Measuring the predictability of life outcomes with a scientific mass collaboration - suggested by @ninadicara #3
🔒 Nudging Privacy: The Behavioural Economics of Personal Information - suggested by @ninadicara #102
Accountable Algorithms [1] - suggested by DEC paper reviewer
Algorithmic Transparency for the Smart City [1] - suggested by DEC paper reviewer
Assessing risk, automating racism. - suggested by DEC paper reviewer
The failures of algorithmic fairness - suggested by Paul Lee
History of data science#
Statistics, Eugenics and Me by Raphael Sonabend - suggested by @ninadicara #70
Environmental costs and considerations#
AI and Climate Change: How they’re connected, and what we can do about it - suggested by @JennyBrennan
Green AI - suggested by @JennyBrennan
Principles of Green Software Engineering - suggested by @JennyBrennan
Quantifying the Carbon Emissions of Machine Learning and the ML CO2 Impact calculator - suggested by @JennyBrennan
What an ancient lake in Nevada reveals about the future of tech - suggested by @NatalieThurlby - ✅11th August 2021
Privacy and surveillance#
“Participant” Perceptions of Twitter Research Ethics - suggested by @nataliethurlby - ✅25th Aug 2021
The Rise of Private Spies - suggested by @HDiscoDay - ✅28th July 2021
📺 A Question of Trust - Reith Lectures - Onora O’Neill
Transparency is Surveillance1 - suggested by Charles Radclyffe
Data ethics in the private and public sectors#
Algorithmic Accountability for the Public Sector1 - suggested by Tom Whittaker
Suggested excerpt: Executive Summary
Google’s AI principles - suggested by @milliams
The Financial Modelers Manifesto - #2 - suggested by @ninadicara
Related: A hippocratic oath for data science - suggested by @ninadicara
Artificial intelligence and transparency in the public sector - suggested by Tom Whittaker
Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics
GCHQ - Pioneering a New National Security: The Ethics of Artificial Intelligence - suggested by @ninadicara
UK Statistics Authority Landscape Review of Applied Data Ethics - :white_check_mark: 14th Apr 2021
📺[The mathematics of crime and terrorism - suggested by @hdiscoday - ✅ 14th July 2021
UK AI Strategy (2021) - suggested by Paul Lee
Learning from the stochastic parrots: finding fairness in AI - suggested by Paul Lee
Data science and research culture#
Towards Decolonising Computional Science - suggested by @nataliethurlby
📺Algorithmic Colonialisation suggested by @ninadicara
#bropenscience is Broken Science - suggested by @nataliethurlby - ✅ 17th March 2021
Collectors, Allies, and Nightlights, Oh My - suggested by @nataliethurlby
The Tyranny of Structurelessness - suggested by @nataliethurlby
Whiteness In Statistics - suggested by @NatalieThurlby
Ethics Creep🔒 - suggested by @NatalieThurlby
Economies of Virtue: The Circulation of ‘Ethics’ in Big Tech - suggested by @RShkunov
Math Washing [2] - suggested by Kamilla Wells
Ethics in action (the good and the not so good)#
ESR: Ethics and Society Review of Artificial Intelligence Research - suggested by @nataliethurlby - ✅ 8th Sept 2021
A toolkit for centering racial equity throughout data integration1
Structural injustice and individual responsibility 🎤 - suggested by Kamilla Wells - ✅ 6th Oct 2021
Design Justice: Towards an Intersectional Feminist Framework for Design Theory and Practice
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Chapter 7: “Show your work”
The description of “co-liberatory” projects in chapter 5: “Unicorns, janitors, ninjas, wizards and rock stars”
People With Disabilities Say This AI Tool Is Making the Web Worse for Them - suggested by @NatalieThurlby
Sharing learnings about our image cropping algorithm (Twitter) - suggested by @ninadicara, see #64
I can text you a pile of poo, but I can’t write my name - suggested by @nataliethurlby
Building Compassion into Your Modelling Variables - WeAllCount articles suggested by a reviewer for DEC paper
The Priviledge Embedded in You Unit of Analysis - WeAllCount articles suggested by a reviewer for DEC paper
Participatory Data Stewardship - Ada Lovelace Institute work suggested by a reviewer for DEC paper
Data sheets for data sets - suggested by @ninadicara
Field Specific#
Natural Language Processing (NLP)#
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? - suggested by @ninadicara - ✅17th February 2021
Mitigating Gender Bias in Natural Language Processing: Literature Review - suggested by @NatalieThurlby
Social Biases in NLP Models as Barriers for Persons with Disabilities
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings - suggested by @leriomaggio
Follow-up paper: Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them - suggested by @NatalieThurlby
Semantics derived automatically from language corpora contain human-like biases - suggested by@alicesaunders
Gender Bias and Sexism in Language - suggested by @leriomaggio
HONEST: Measuring Hurtful Sentence Completion in Language Models - suggested by @leriomaggio
Computer vision#
📺 How normal am I? - interactive - suggested by Valentina Ragni
📺 Critical perspectives on computer vision - suggested by @NatalieThurlby ✅
Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices - found through the CIVIC Data Library of Context suggested by a reviewer for DEC paper
Explainable AI/ML#
Explainable machine learning: how can you determine what a party knew or intended when a decision was made by machine-learning? - suggested by Tom Whittaker
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI - suggested by @NatalieThurlby