<aside> 👋🏽

This is an overview of how Playlab approaches trust and safety. Have feedback or ideas? Reach out to [email protected].

</aside>

Table of Contents


Aligning our organizational incentives with impact and safety

We incorporated as a nonprofit in order to better align incentives so we’re accountable to impact and safety.

Responsible growth

Right now, Playlab is invite-only. The primary way to join Playlab is through professional opportunities offered by us or trusted partners like Teach For America / Teach For All, Relay GSE, Berkeley, and Leading Educators.

Prioritizing open source AI

We think that the AI models used in public education should be transparent and open to being interrogated for bias and interpretability. In the short-term, we use closed AI models so that our community can experience frontier technology and explore how that can be used to impact teaching and learning. In the long-term, we’re aiming to prioritize fully open-source models.

Moderation & making underlying AI models safer

Every app in Playlab benefits from additional bias and alignment guidance provided to the AI models. Most user inputs and all model outputs have automated moderating enabled. Outputs that fail moderation cause a conversation to end and users are able to flag outputs manually for issues related to bias, appropriateness, and hallucination. Every time someone creates a new app in Playlab, they start with a template that encourages them to consider bias.

Responsible product development

Our approach to product development includes red teaming; testing higher risk releases with a smaller subset of users that we co-design with; disclosures in product; and ongoing professional learning. We are also dedicating resources to developing improved age-appropriate and education-appropriate moderation models.

Supporting our community in creating the right guardrails for their context

Through professional learning, courses, content, and coaching, we support our community members in designing guardrails for their specific projects. App usage is reviewable and inspectable by the app’s creators, enabling teachers to understand how students are using the resources they are given.

Responsible piloting before use and scale

We’re very early to this AI wave. We encourage Playlab community members and partners to pilot and test how Playlab apps might drive impact in their context — so they can both test for and guard against harm and bias — and so they can prioritize projects that drive forward impact.

Benchmarking and evaluation

In collaboration with partners like Chan Zuckerberg Initiative and Leading Educators, we’re developing rubrics and evaluation tooling to evaluate the quality, impact, and safety of apps built on Playlab. Our community can use this to improve their projects and to gauge the quality and safety of projects created by others.