A feminist-inspired multi-stakeholder engagement framework and tools for evolving and enacting social, computational, and legal agreements that govern the lifecycle of an AI system.
  1. Co-constitution

  2. Addressing friction

  3. Informed refusal

  4. Contestability and complaint

  5. Disclosure-centered mediation

Scenarios (coming soon)
  • Gender-Equity
  • Healthcare
  • Education
  • Content Moderation

Learn more about the framework and contact us at: bogdana@mozillafoundation.org

In service
of you and a vision for improved transparency and human agency in the interactions between people and algorithmic systems:
  • Bogdana Rakova, Mozilla Foundation, Senior Trustworthy AI Fellow
  • Megan Ma, Assistant Director, Stanford Center for Legal Informatics
  • Renee Shelby, Sociology Department and Legal Studies Program, Georgia Institute of Technology

Graphic design by Yan Li.

With the kind support of Mozilla Foundation.


Informed Refusal

TwSw interventions see refusal as a generative practice - given the opportunity to opt out, individuals are inspired to provide technology companies their critical feedback about what needs to change for them to opt in. The concept of informed refusal comes from Ruha Benjamin’s work where she compares it to the concept of informed consent in medical law, arguing for a justice-oriented approach to constructing more reciprocal relationships between institutions and communities.  

We recognize that people hold a multiplicity of dynamic human identities. Inspired by the temporal dynamics in law - writing in the present, reflecting on the past, and encoding the future, we challenge the dominant temporal design of algorithmic systems which have been critically framed as self-fulfilling prophecies and stochastic parrots. We argue for the need to incorporate multifaceted mechanisms to refuse harmful algorithms within the lifecycle of AI including its design, the user interface, as well as contractual agreements such as terms of use and content policies. 

We encourage practitioners to ask: What are the assumptions and problem formulations that underpin a particular AI model or system? Who gets to define the default conditions? What does consent mean and what forms does it take? What is the temporal dynamics of consent and its refusal?