PyData Amsterdam 2024

Sweet Summer Child Score
09-18, 15:30–17:00 (Europe/Amsterdam), Amstel Room - OBA Oosterdok

Sweet Summer Child Score (SSCS) is an open source library to identify potential AI harms. In this tutorial we'll break into small groups and take the quiz online using a motivating scenario. Participants will practice mapping the risks of an AI system in a structured way, helping to formalise their instincts, identify potential harms, and plan next steps to better understand or reduce the risks of these harms.


A truism in tech is that we're good at asking "can" we do it but not "should" we do it. Attempting to tackle the latter, this library offers a system scan to quickly identify potential harms, and build the capability of relative risk assessment.

This tutorial is designed for Data Scientists and ML devs at an entry level - participants will need a laptop and internet access. A basic understanding of statistics is assumed.

The project and GitHub repos are online at https://summerchild.dev

We'll spend the first hour completing the quiz in small groups, and the remaining time prioritising the risks we identified and planning hypothetical next steps. We'll also touch on running it locally in the terminal, and forking the lib to configure the questions to be more specific for a vertical or use-case.

SSCS does not explore the specifics of your stack or technical implementation -- instead it takes a step back to look at the ecosystem your technology will be deployed in, and the implementation choices which define the seam between your system and the broader world. Put simply, this is an attempt to see the forest, not the trees.

Join this tutorial to ask some sticky questions and take an unsympathetic look at our chances of building fair and responsible tech. We won't take ourselves too seriously while we practice having challenging and constructive discussions with peers and colleagues!

Laura is a very technical designer™️. She recently joined Pydantic as Lead Design Engineer. Her side projects include Debias AI, (debias.ai), Sweet Summer Child Score (summerchild.dev), Ethics Litmus Tests (ethical-litmus.site), fairXiv (fairxiv.org), the Melbourne Fair ML reading group (groups.io/g/fair-ml). Laura is passionate about feminism, digital rights and designing for privacy. She speaks, writes and runs workshops at the intersection of design and technology.