Acceleration AI Ethics, the Debate between Innovation and Safety, and Stability AI's Diffusion versus OpenAI's Dall-E
This work addresses the problem of balancing innovation and safety in AI ethics for researchers and developers, presenting a novel framework that is incremental in redefining ethical approaches.
The paper tackles the perceived conflict between AI ethics and innovation by proposing a reconceptualization of ethics as an accelerator, using a comparative analysis of Stability AI's Diffusion and OpenAI's Dall-E to identify five key principles that frame ethics as a driver rather than a hindrance to AI development.
One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI's Diffusion and OpenAI's Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence.