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Found 11 papers, 2 papers with code
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals.
AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI
As AI technology advances rapidly, concerns over the risks of bigness in digital markets are also growing.
Optimization's Neglected Normative Commitments
Optimization is offered as an objective approach to resolving complex, real-world decisions involving uncertainty and conflicting interests.
Dynamic Documentation for AI Systems
In this paper, we show the limits of present documentation protocols, and argue for dynamic documentation as a new paradigm for understanding and evaluating AI systems.
Beyond Bias and Compliance: Towards Individual Agency and Plurality of Ethics in AI
AI ethics is an emerging field with multiple, competing narratives about how to best solve the problem of building human values into machines.
Reward Reports for Reinforcement Learning
Building systems that are good for society in the face of complex societal effects requires a dynamic approach.
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems
In the long term, reinforcement learning (RL) is considered by many AI theorists to be the most promising path to artificial general intelligence.
Hard Choices in Artificial Intelligence
In this paper, we examine the vagueness in debates about the safety and ethical behavior of AI systems.
AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks
Despite interest in communicating ethical problems and social contexts within the undergraduate curriculum to advance Public Interest Technology (PIT) goals, interventions at the graduate level remain largely unexplored.
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical Commitments
As AI systems become prevalent in high stakes domains such as surveillance and healthcare, researchers now examine how to design and implement them in a safe manner.
