The Zen of ML is a set of community-accepted design principles for responsible ML practice. We have developed draft design principles over the months leading up to MozFest. In the hackathon we will expand, improve on, review and evaluate the design principles through iterative, interactive sessions. The end goal is to have a set of working design principles so that we can launch The Zen of ML v0.1 in the weeks that follow.
Join us for fun, engaging and productive dialogue, and share your insights! We are looking for contributors with practice in using machine learning (beginner to advanced), programmers, designers, ethicists and linguists. After the hackathon we will continue to invite practitioners to review and give feedback on the design principles, and test the principles in real life educational contexts.
Register here to attend: http://mzl.la/taihackathon.
The Zen of ML is a project of the Mozilla Trustworthy AI Working Group. We have developed draft design principles over the months leading up to MozFest. At the AIIRL hackathon we will review, evaluate and build on the design principles. The questions we seek to answer are:
- Do the design principles cover the key aspects of responsible ML?
- Are the design principles useful for new entrants into ML?
- Are the design principles useful for educators?
- Are the design principles well formulated?
- Do the design principles fulfill the requirements that we set out for them?
The hackathon will consist of interactive sessions where we evaluate the draft principles and improve them where necessary. At the end of the hackathon we hope to have a set of working design principles so that we can launch The Zen of ML 1.0 in the weeks that follow.
How will you deal with varying numbers of participants in your session?:The session is intended to break into small group discussion across 15-20 draft statements provided by facilitators. Should there be a small number of participants, we will combine into a single discussion group covering 1-2 statements. For large numbers of participants, the number of small groups can increase with the same statements being discussed in multiple groups.
We're hoping that many efforts and discussions will continue after Mozfest. Share any ideas you already have for how to continue the work from your session.:The end goal is to publish The Zen of ML as a set of community-accepted design principles. We are looking for contributors with practice in using machine learning (beginner to advanced), designers, and linguists. After the session we will continue to collect suggestions from the public in the form of aphorisms, description text, and references. We plan to collect this information virtually via our website and periodically incorporate such feedback into the published result. We will also invite practitioners to review and give feedback on the design principles.
Participants who want to get involved more actively have an option to join us and shape the final Zen of ML principles through the Trustworthy AI working group.
As an ethicist, I build frameworks that empower responsible and trustworthy approaches to ethical uncertainty. I hold a Ph.D. in philosophy.
I am an engineer and designer, who serendipitously evolved into a computer scientist. I am now doing my PhD on deeply personal, completely private voice assistants at the TU Delft.
Bernease is a data scientist at University of Washington and WhyLabs. At WhyLabs, she builds data monitoring tools using approximate statistics. At UW, human-centered evaluation metrics with synthetic data.