Meetup Manifesto for Data Practices

Christine Chung
Making Meetup
Published in
4 min readJun 27, 2018


At Meetup, our mission is to bring people together in real life. By paying a monthly subscription fee, Meetup organizers allow us to pursue that mission wholeheartedly — without influence from ads. It’s a privilege we don’t take for granted. Instead of optimizing for clicks or ad dollars, we’re optimizing for real people showing up in real life to do what matters to them.

To that end, we use data throughout our product to help people find, create, and discover the best Meetups for them — and we’re just getting started. We’re using machine learning to recommend the highest quality Meetups to members. We’re getting smarter about sending better and fewer notifications. We’re using natural language processing to determine which Meetups are most relevant.

As our use of data grows in scale and sophistication, we’ve decided to put some guard rails in place. Our current business model helps us align our values with our practices, but isn’t enough on its own. We do some things already, like empowering organizers to protect the privacy of their members and monitoring gender bias in our recommendations. But we need to do more to ensure that these practices are deliberate, intentional and prioritized — not ad hoc or subject to convenience.

That’s why we have adopted the Manifesto for Data Practices. We’ve adapted it to reflect Meetup’s particular values and principles, but followed the core structure and intent laid forward in the framework of the manifesto. We’re using it to align and guide data practitioners in creating effective solutions and making ethical decisions when using data to build Meetup. By codifying our values and principles, we empower individuals in a way that allows us to hold one another accountable and ensure that our choices are intentional, ethical, and consistent with our mission — that we provide the best experience for Meetup’s members and organizers.

This adapted version of the manifesto lays out the practices that we aspire to — what we think ethical data practice looks like, and a vision for how to get there.

Principles & Practices

As data practitioners at Meetup, we aim to achieve:


by owning the impact of our work, celebrating our wins, and fixing our mistakes.

This looks like:

  • Proactively monitoring the impact of models on an ongoing basis and iterate on our methodology in response to new information
  • Inviting and acting on fair criticism of our work by others at Meetup, and when appropriate, outside of the company
  • Providing a clear channel for communication and reconciliation to those impacted by our use of data

Fairness & Inclusion

by embracing and serving diversity to create a space where all feel welcome.

This looks like:

  • Building in checkpoints for bias — in our assumptions, the data we use, and the models we produce — in every project, with particular attention to underrepresented or vulnerable populations
  • Collaborating with other Meetup teams to make sure we are addressing real needs and creating a better experience for all Meetup members, directly or indirectly
  • Building teams with diverse ideas, backgrounds, and strengths


by creating reusable solutions that maximize social benefit and minimize harm.

This looks like:

  • Defining measurable, timely indicators of success for every project we take on
  • Using iterative A/B testing and analysis to test our hypotheses
  • Producing reproducible, documented, and extensible work
  • Quantifying the short and long term impacts of our work on individuals and society

Privacy & Security

by protecting the privacy and security of individuals represented in our data.

This looks like:

  • Encrypting data wherever it is stored at rest and in transit
  • Storing, accessing, and using personal data only when we have a reason that clearly benefits our members, and delete otherwise
  • Establishing clear and manageable restrictions on access to data
  • Complying with all applicable external policies and fill in the gaps where we find them lacking


by responsibly communicating how and when we collect data and for what purposes.

This looks like:

  • Sharing data only when it benefits our members, and only with parties that share our values and principles
  • Communicating clearly how and when member data will be collected, stored, used, or shared
  • Understanding and communicating the impact of the algorithms and models that we use


by collecting and making available data & insights to benefit members and improve Meetup.

This looks like:

  • Facilitating the continuous collection and availability of data for internal use
  • Liberating data by making it accessible and interpretable to non-technical users
  • Presenting our work in ways that empower others to make better-informed decisions
  • Helping others understand the most useful and appropriate applications of data to solve real-world problems

Our team is using data and machine learning to help humans be more human: bringing people together in real life to create community for everyone. Join us!



Christine Chung
Making Meetup

Data Scientist at Meetup