This page leads to a series of related about the human centred design contribution to automated decision systems that have ethical outcomes.
The audience for these articles was initially the Data61 User Experience Designers and Product Managers who have been tasked to provide assistance on development of products and systems using data (sensitive or public) and Machine Learning (algorithms that make predictions, assist with decision making, reveal insights from data, or act autonomously), because these products are expected to deliver information to a range of users and provide the basis for contextually supported decisions.
However it’s always been hope that a wider UX and Product audience will find it helpful.
Machine Learning computer scientists, software engineers, data scientists, anthropologists or other highly skilled technical or social science professionals are very welcome to read this guide in order to increase and enhance their understanding of user experience concerns and maybe even refer to it.
Crafting Ethical AI Products and Services Part 1: Purpose and Position
This article looks at the reasons why an ethical mindset and practise is key to technology production and positions the ownership as a multidisciplinary activity.
Crafting Ethical AI Products and Services Part 2: Proposed Methods
This article is a set of proposed methods for user experience designers and product managers working in businesses that are building new technologies specifically with machine learning AI.
Softwiring – The role of Human Centred Design in Ethical Artificial Intelligence
This is a keynote converted to an article and is more of a summary of the potential for UX to have meaningful contributions and impact in ethical technology (D61+Live 2018 keynote)
Reading List
This list that contributed to the articles above was compiled between 2017 – 2018.
Legal
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- Australian Government Privacy Laws
- CSIRO Ethics Resources for Researchers (including indigenous communities)
Papers and Reports
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- Ethically Aligned Design Version 2, by the IEEE
- Algorithmic Impact Assessments – framework for public agency accountability by AI Now
- AI Now Institute 2017 Report
- The Three Laws of Robotics in the Age of Big Data by Jack M. Balkin
- Mechanics of Trust by Jens Riegelsberger, M. Angela Sasse & John D. McCarthy
- Auditing Algorithms from the Outside
- Future of Life AI Principals
- Revealing Uncertainty for Information Visualisation
Articles
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- Ethics By Numbers How To Build Machine Learning That Cares by Lachlan McCalman
- Computer says no: why making AIs fair, accountable and transparent is crucial by Ian Sample Science editor, The Guardian
- Don Norman: Designing for People
- Why AI is still waiting for it’s ethics transplant by Scott Rosenberg, WIRED
- Ethical Machine Learning (from 8:00 to 19:20)
- Will Tech Companies Ever Take Ethics Seriously? by Evan Selinger
Emerging Practise
Tools
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- ODI Data Ethics Canvas
- Ten Usability Heuristics by NNGroup
- Humane AI Newsletter by Roya Pakzad
- Fairness Measures managed by Meike Zehlike