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.

通过实施 Dual Governance 框架，我们认为可以促进创新和创造力，同时确保生成 AI 的安全和道德部署。

双重治理：中央监管与众包安全机制在生成式人工智能中的交叉

Dual Governance: The intersection of centralized regulation and  crowdsourced safety mechanisms for Generative AI

Integrating ethical practices into the AI development process for artificial
intelligence (AI) is essential to ensure safe, fair, and responsible operation.
AI ethics involves applying ethical principles to the entire life cycle of AI
systems. This is essential to mitigate potential risks and harms associated
with AI, such as algorithm biases. To achieve this goal, responsible design
patterns (RDPs) are critical for Machine Learning (ML) pipelines to guarantee
ethical and fair outcomes. In this paper, we propose a comprehensive framework
incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical
development of AI systems. Our framework comprises new responsible AI design
patterns for ML pipelines identified through a survey of AI ethics and data
management experts and validated through real-world scenarios with expert
feedback. The framework guides AI developers, data scientists, and
policy-makers to implement ethical practices in AI development and deploy
responsible AI systems in production.

本文提出了一个包含新的负责的 AI 设计模式的全面框架，以将 RDPs 整合到 ML 管道中，以减轻 AI 可能存在的风险和危害，以确保 AI 系统的道德发展。

机器学习管道的负责任设计模式

Responsible Design Patterns for Machine Learning Pipelines

Contextual utility theory integrates context-sensitive factors into
utility-based decision-making models. It stresses the importance of
understanding individual decision-makers' preferences, values, and beliefs and
the situational factors that affect them. Contextual utility theory benefits
explainable AI. First, it can improve transparency and understanding of how AI
systems affect decision-making. It can reveal AI model biases and limitations
by considering personal preferences and context. Second, contextual utility
theory can make AI systems more personalized and adaptable to users and
stakeholders. AI systems can better meet user needs and values by incorporating
demographic and cultural data. Finally, contextual utility theory promotes
ethical AI development and social responsibility. AI developers can create
ethical systems that benefit society by considering contextual factors like
societal norms and values. This work, demonstrates how contextual utility
theory can improve AI system transparency, personalization, and ethics,
benefiting both users and developers.

本研究探讨了将情境型效用理论融入基于效用的决策模型中，应用此理论可以提高人工智能系统的透明度、个性化和道德发展，更好地服务于用户和开发人员。