Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913568528531456 |
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| author | Harland, Hadassah Dazeley, Richard Vamplew, Peter Senaratne, Hashini Nakisa, Bahareh Cruz, Francisco |
| author_facet | Harland, Hadassah Dazeley, Richard Vamplew, Peter Senaratne, Hashini Nakisa, Bahareh Cruz, Francisco |
| contents | Emerging research in Pluralistic Artificial Intelligence (AI) alignment seeks to address how intelligent systems can be designed and deployed in accordance with diverse human needs and values. We contribute to this pursuit with a dynamic approach for aligning AI with diverse and shifting user preferences through Multi Objective Reinforcement Learning (MORL), via post-learning policy selection adjustment. In this paper, we introduce the proposed framework for this approach, outline its anticipated advantages and assumptions, and discuss technical details about the implementation. We also examine the broader implications of adopting a retroactive alignment approach through the sociotechnical systems perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23630 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI Harland, Hadassah Dazeley, Richard Vamplew, Peter Senaratne, Hashini Nakisa, Bahareh Cruz, Francisco Machine Learning Artificial Intelligence Emerging research in Pluralistic Artificial Intelligence (AI) alignment seeks to address how intelligent systems can be designed and deployed in accordance with diverse human needs and values. We contribute to this pursuit with a dynamic approach for aligning AI with diverse and shifting user preferences through Multi Objective Reinforcement Learning (MORL), via post-learning policy selection adjustment. In this paper, we introduce the proposed framework for this approach, outline its anticipated advantages and assumptions, and discuss technical details about the implementation. We also examine the broader implications of adopting a retroactive alignment approach through the sociotechnical systems perspective. |
| title | Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.23630 |