arXiv:2209.15448v3 Announce Type: replace Abstract: As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this paper, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach utilizes the observed action, either from AI or humans, as input for achieving a stronger oracle in policy learning for the decision maker (humans or AI). In the decision process wi
The increasing prevalence of sophisticated AI systems necessitates advanced methods for integrating human and artificial intelligence to optimize performance and mitigate risks.
This research introduces a novel approach to policy learning that leverages human-AI interaction for more robust and effective sequential decision-making, elevating the potential of AI systems in complex environments.
The proposed 'super policy learning' paradigm allows observed human or AI actions to directly enhance the decision-making capabilities of both humans and AI, creating a more powerful, integrated oracle.
- · AI developers
- · Organizations implementing AI
- · Human-AI collaboration platforms
- · AI systems without human feedback loops
More capable and robust AI systems capable of operating in confounded environments through human-AI interaction.
Accelerated adoption of AI in critical sectors where human oversight and adaptability are paramount.
The development of new regulatory and ethical frameworks specifically designed for highly integrated human-AI decision-making systems.
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Read at arXiv cs.LG