Summary of A Systems Thinking Approach to Algorithmic Fairness, by Chris Lam
A Systems Thinking Approach to Algorithmic Fairness
by Chris Lam
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper applies systems thinking to model algorithmic fairness by encoding prior knowledge about potential bias in data. The authors use causal graphs to link AI/ML systems to politics and law, combining techniques from machine learning, causal inference, and system dynamics. This enables capturing different emergent aspects of the fairness problem. The approach helps policymakers understand complex trade-offs between fairness policies, providing a sociotechnical foundation for designing AI policy aligned with political agendas and societal values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure artificial intelligence (AI) systems are fair and don’t discriminate against certain groups of people. The authors use a special way of thinking called “systems thinking” to figure out where bias might exist in the data that trains AI systems. They then use this information to design fairness policies that take into account different political views and societal values. |
Keywords
» Artificial intelligence » Inference » Machine learning
