Data Ethics Training, Learning and Upskilling

Managing data ethics well requires specific skills, methods and knowledge that should be developed in employees at different levels through professional training courses.

LOTI have also crowdsourced trainings, readings and resources to help guide individuals or organisations who are looking to learn about and upskill in data ethics.

Socio-technical skills

Data ethics can be described as a ‘socio-technical’ design discipline. This means that it requires technical knowledge around data collection and processing as well as social expertise to understand how data–based systems shape and function in society. 

On the technical side, data scientists and analysts should think critically about the data they use and the models they select as part of their collection, analysis and presentation techniques. An understanding of reporting, selection and systematic biases, overgeneralisation, unconscious bias and overfitting should be part of a practitioner’s toolkit. Good practice, such as conducting Exploratory Data Analysis at the beginning of the project and using a well-structured notebook, can help others to provide scrutiny and identify biases. Using the UK Statistics Authority’s Guidelines on Ethics Self-Assessment (Method and Quality) is a good way to rank and record ethical risks at the analysis stage.

Practitioners, especially those in management positions, should also understand how data technologies exist within the society in which they are implemented. This means having the skills and knowledge to know when and how to consult with residents on data, how to write about data projects in the right language for different audiences and, using design methods, being able to identify any problems that may emerge through the use of any tool or service developed with data.

Useful Resources and Tools

LOTI have crowdsourced the following training courses, introductory readings and other resources to help any organisation or individual educate themselves on data ethics. If you have any recommendations to add to this page, please email the LOTI Data Ethicist, Sam Nutt, at sam.nutt@loti.london.

Data Ethics Training Courses

• Awareness in Data Ethics – Office for National Statistics
• Introduction to Data Ethics and the Data Ethics Canvas – Open Data Institute
• Data Ethics Professional and Data Ethics Facilitator – Open Data Institute*
• Practical Data Ethics – fast.ai
• Ethics of AI – University of Helsinki
• Ethics for technology – Hattusia
• Data Ethics, AI and Responsible Innovation – edX

*This training was taken by LOTI staff.

Data Bias Courses

• Understanding Bias in Data – Coursera
• Fairness (machine learning crash course) – Google
• Research Design in Social Data Science – SAGE Campus

Data Ethics Tools & Resources for Practitioners

Core Tools:

• Data Ethics Canvas – Open Data Institute
• Data Ethics Framework – Government Digital Service
• Consequence Scanning – an agile practice for responsible innovators – doteveryone
• Ethics Self-Assessment Tool – UK Statistics Authority

Other Useful Tools:

• Data Ethics Maturity Model – Open Data Institute
• The Turing Commons – Alan Turing Institute
• The AI Ethics Living Dictionary – Montreal AI Ethics Institute
• The Digital Ethics Compass – Danish Design Council
• Algorithmic Impact Assessment Template – Ada Lovelace Institute

Data Ethics – Popular and Recommended Readings

• Technology is not Neutral, by Stephanie Hare
• Invisible Women, by Caroline Criado Perez
• Weapons of Maths Destruction, by Cathy O’ Neil
• The Smart Enough City, by Ben Green
• AI Ethics, by Mark Coeckelbergh
• Privacy is Power, by Carissa Veliz
• The Black Box Society, by Frank Pasquale
• Automating Inequality, by Virginia Eubanks
• Who Owns the Future? By Jaron Lanier
• Habeas Data Privacy vs. the Rise of Surveillance Tech, by Cyrus Farivar

Image source: Nacho Kamenov & Humans in the Loop / Better Images of AI / Data annotators discussing the correct labeling of a dataset / CC-BY 4.0

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