Dual Governance: The intersection of centralized regulation and
  crowdsourced safety mechanisms for Generative AI
Avijit Ghosh Dhanya Lakshmi
Generative Artificial Intelligence (AI) has seen mainstream adoption lately,
especially in the form of consumer-facing, open-ended, text and image
generating models. However, the use of such systems raises significant ethical
and safety concerns, including privacy violations, misinformation and
intellectual property theft. The potential for generative AI to displace human
creativity and livelihoods has also been under intense scrutiny. To mitigate
these risks, there is an urgent need of policies and regulations responsible
and ethical development in the field of generative AI. Existing and proposed
centralized regulations by governments to rein in AI face criticisms such as
not having sufficient clarity or uniformity, lack of interoperability across
lines of jurisdictions, restricting innovation, and hindering free market
competition. Decentralized protections via crowdsourced safety tools and
mechanisms are a potential alternative. However, they have clear deficiencies
in terms of lack of adequacy of oversight and difficulty of enforcement of
ethical and safety standards, and are thus not enough by themselves as a
regulation mechanism. We propose a marriage of these two strategies via a
framework we call Dual Governance. This framework proposes a cooperative
synergy between centralized government regulations in a U.S. specific context
and safety mechanisms developed by the community to protect stakeholders from
the harms of generative AI. By implementing the Dual Governance framework, we
posit that innovation and creativity can be promoted while ensuring safe and
ethical deployment of generative AI.