Centrala begrepp
The author argues that model-induced distribution shifts (MIDS) can lead to loss in performance, fairness, and minoritized group representation, even in initially unbiased datasets. They propose algorithmic reparation (AR) as a framework to counter the injustices of MIDS and promote equity.
Sammanfattning
Fairness feedback loops caused by model-induced distribution shifts (MIDS) can have detrimental effects on model performance, fairness, and representation of minoritized groups. The study introduces algorithmic reparation (AR) as a potential solution to address these issues and promote equity. Through empirical settings and experiments, the research highlights the importance of understanding and mitigating MIDS in machine learning systems.
Key points:
- Model-induced distribution shifts (MIDS) impact performance, fairness, and representation.
- Algorithmic reparation (AR) is proposed as a framework for addressing MIDS.
- Empirical settings and experiments demonstrate the significance of recognizing and mitigating MIDS in machine learning systems.
Statistik
"We find that chains of generative models eventually converge to the majority and amplify model mistakes that eventually come to dominate and degrade the data until little information from the original distribution remains."
"Our experiments on CelebA undergoing model collapse and performative prediction leads to a 15% drop in accuracy."
"For example, our experiments on CelebA undergoing model collapse and performative prediction leads to a 15% drop in accuracy."
Citat
"We introduce a framework that allows us to track multiple MIDS over many generations, finding that they can lead to loss in performance, fairness, and minoritized group representation."
"Despite these negative consequences, we identify how models might be used for positive, intentional interventions in their data ecosystems."
"Our work takes an important step towards identifying, mitigating, and taking accountability for the unfair feedback loops enabled by the idea that ML systems are inherently neutral and objective."