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Aligning AI Optimization to Community Well-Being.
1Partnership on AI, San Francisco, CA USA.
International Journal of Community Well-Being
|November 1, 2021
Summary
This study explores integrating community well-being metrics into AI optimization. It proposes a process for commercial AI systems to prioritize societal impact alongside performance.
Area of Science:
- Computer Science
- Social Science
- AI Ethics
Background:
- Large tech platforms like Facebook and YouTube have begun incorporating well-being and user satisfaction metrics into their systems.
- Existing optimization algorithms often prioritize narrow performance indicators over broader community well-being.
- There is a growing need to align artificial intelligence (AI) development with societal values and ethical considerations.
Purpose of the Study:
- To investigate the incorporation of community well-being metrics into AI optimization objectives.
- To propose a practical framework for integrating community well-being into commercial AI systems.
- To examine the potential applications of well-being metrics in diverse AI systems, such as news and product recommendations.
Main Methods:
- Documenting case studies of platforms like Facebook and YouTube that have integrated well-being metrics.
- Synthesizing insights from participatory design and AI governance principles.
- Proposing a generalized process for integrating community well-being into AI.
Main Results:
- Identified Facebook's incorporation of "well-being" metrics (2017) and YouTube's "user satisfaction" metrics (2015).
- Proposed a four-step process: community involvement, metric selection, integration into objectives, and evaluation.
- Highlighted potential applications in news and product recommendation systems.
Conclusions:
- Community well-being metrics can be effectively integrated into AI optimization objectives.
- A participatory and adaptive approach is crucial for successful implementation.
- Further research is needed on optimal community engagement and business impact assessment.
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