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

• What is Data Ethics?¹

• Ethical data science¹

• Algorithmic Injustices: Towards a relational ethics - suggested by @RobertArbon, ✅ 3rd March 2021

• Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

• Ethics can’t be a side hustle - : ✅ 3rd Feb 2021

• Reflections on trusting trust

• 📺 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

• Why data is never raw

• 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

  • We created poverty, algorithms won’t make that go away ✅

  • Algorithms designed to fight poverty can actually make it worse

• What does it mean to ‘solve’ the problem of discrimination in hiring? - suggested by @edwinrobots

• Machine decisions and human consequences

• Fairness and utilization in allocating resources with uncertain demand - suggested by @edwinrobots

• Hacking the cis-tem

• 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

History of data science

• Statistics, Eugenics and Me by Raphael Sonabend - suggested by @ninadicara #70

Environmental costs and considerations

• Which programming languages use the least electricity?

• 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

• Privacy is power

• Paths to Social License for Tracking-data Analytics

• 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

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

• A feature not a bug

• A manifesto for team science

• Whiteness In Statistics - suggested by @NatalieThurlby

• Why Open Source misses the point of Free Software

• Ethics Creep🔒 - suggested by @NatalieThurlby

• Economies of Virtue: The Circulation of ‘Ethics’ in Big Tech - suggested by @RShkunov

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

• Data Feminism Book Club¹

  • 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

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