Preserving Agency: Why AI Safety Needs Community, Not Corporate Control
The AI community faces a design challenge: how do we build safety mechanisms that protect users from harm while preserving their autonomy and decision-making capacity? This challenge becomes particularly pronounced when designing AI companions and conversational systems, where the line between responsible protection and overprotective control can blur quickly.
The Paternalistic Pattern
Traditional approaches to AI safety often follow a top-down model: experts identify risks, engineers build safeguards, and users receive a “protected” experience. While well-intentioned, such designs can end up feeling overly restrictive or out of step with real user needs.
Consider a simple example: an AI companion that automatically refuses to engage with any emotional content, redirecting users to “seek professional help” whenever they express feelings. While intended to prevent unhealthy dependency, such rigid boundaries can feel dismissive to users who are simply sharing everyday stress. A more thoughtful approach might involve the system explaining its design limitations (i.e., that it processes language patterns rather than truly understanding emotions) while still offering to continue the conversation within its actual capabilities. This allows users to make informed choices about what kind of interaction would be most helpful, rather than encountering unexplained barriers.
Such paternalism can backfire, undermining the very protections it seeks to provide. Users may come to feel that the system doesn’t trust their judgment, while uniform safeguards designed for the most vulnerable end up unnecessarily restricting others. Frequent interventions without clear explanation can erode confidence in the system’s reliability. Most importantly, broad-brush approaches cannot adapt to the diversity of human needs -- the same protocol that is essential for someone in crisis may feel intrusive to someone simply looking for routine assistance or casual conversation.
Why Open Source Changes Everything
Open source development offers a different approach to AI safety, one that embraces transparency, diversity, and collaboration instead of imposing solutions from above. Transparent development means safety mechanisms become educational resources; when people can examine how safety decisions are made, they develop an understanding of the technical and ethical considerations involved, leading to more informed interactions across all AI systems. Diverse participation ensures that safety measures reflect the needs of varied communities and not the assumptions of a single demographic. Developers from different backgrounds, user communities across cultures, and edge cases from real-world deployment all contribute to solutions that work for everyone, not just the original designers. Collaborative boundary-setting treats users as partners in determining appropriate protections; instead of predetermined rules, systems can engage users in ongoing conversations about what boundaries feel helpful versus intrusive. Iterative improvement allows safety systems to evolve based on community feedback, with modular architectures that let different communities combine components based on their specific needs and contexts.
This collaborative approach represents a philosophical reframing that treats users as capable adults who deserve both protection and agency in their AI interactions. But principles alone aren’t enough. To move beyond paternalism, we need to translate these values into design practices that communities can actually shape and use.
Community-Driven Safety
Moving beyond paternalistic approaches requires concrete changes in how we develop AI safety systems. Co-designing safety features means bringing user communities into the design process from the beginning -- teachers, students, and administrators collaborating on educational AI boundaries, or patients, caregivers, and medical professionals working together on healthcare AI protocols. Building adaptive systems involves creating AI that can adjust protective mechanisms based on context and user needs, recognizing when someone needs gentle (or more insistent) support versus when intervention becomes intrusive. Prioritizing education over restriction means helping users understand the implications of their choices instead of simply blocking interactions (explaining parasocial relationship dynamics instead of refusing engagement, for instance).
Creating knowledge-sharing infrastructure allows successful safety approaches to be documented, shared, and adapted across projects, building a cumulative knowledge base that benefits everyone rather than forcing each team to solve the same problems in isolation. This challenge of balancing oversight with community input has been researched. My colleague Avijit Ghosh and co-authors have proposed a "Dual Governance" framework that illustrates how regulatory agencies can systematically evaluate and certify community-developed safety tools, creating transparent processes for reviewing crowdsourced safety mechanisms and adding successful approaches to public registries. This bridges institutional accountability with grassroots innovation, suggesting we can have both oversight and distributed development.
Security Through Obscurity?
A common pushback against open source safety approaches is the worry that transparency creates vulnerabilities: if safety mechanisms are visible, won't malicious actors exploit them more easily? However, this logic gets the security model backwards. In cybersecurity, we've learned that systems relying on secrecy for their security are fundamentally brittle. The most robust security systems remain secure even when their workings are fully public, having been tested and hardened by diverse communities trying to break them.
The same principle applies to AI safety mechanisms. When safety measures are hidden, they can only be tested by the small team that built them. When they're open, they benefit from red-teaming by security researchers, stress-testing by diverse communities, and continuous improvement by developers who understand their limitations. Moreover, truly malicious actors already have access to sophisticated AI systems and can develop their own harmful tools. With the myths about secrecy addressed, the real challenge now becomes: how do we keep evolving safety practices without falling back into paternalism?
So, What's Next?
The tension between user protection and paternalism won’t disappear overnight. But shifting from closed, top-down safety to community-driven approaches offers a path forward: one where users are treated as partners rather than passive recipients of safeguards.
The open source ecosystem is uniquely positioned to make this real. By co-developing safety mechanisms in the open, documenting what works, and iterating together, we can create protections that evolve with the diversity of human needs rather than forcing everyone into the same mold. This open approach is about making them collaborative, explainable, and adaptable.
At Hugging Face, we see our role as building the infrastructure and communities where this kind of open safety innovation can thrive, ensuring AI systems are not only safe, but also respectful of human agency.