Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias

แนวคิดหลัก

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.

บทคัดย่อ

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.

สถิติ

"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."

คำพูด

"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."

ข้อมูลเชิงลึกที่สำคัญจาก

Fairness Feedback Loops

by Sierra Wylli... ที่ arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07857.pdf

สอบถามเพิ่มเติม

How can algorithmic reparation be practically implemented in real-world machine learning systems

Algorithmic reparation can be practically implemented in real-world machine learning systems by incorporating intentional interventions to address historical discrimination and promote equity. This can involve adjusting the training data to better represent marginalized groups, using biased sampling techniques like quota sampling based on intersectional identities, or modifying the model's decision-making process to prioritize fairness over accuracy. Additionally, algorithmic reparation may involve collaborating with experts from diverse fields such as sociology, ethics, and law to ensure that the interventions are effective and aligned with social justice goals.

What are some potential unintended consequences or drawbacks of relying on synthetic data for training models

Relying on synthetic data for training models can have several unintended consequences or drawbacks. One potential drawback is that synthetic data may not accurately reflect the complexities and nuances of real-world scenarios, leading to biases or inaccuracies in model predictions. Additionally, if the synthetic data is generated from biased sources or contains inherent biases itself, it can perpetuate existing inequalities and reinforce discriminatory practices in machine learning systems. Moreover, there is a risk of model collapse where successive generations of models trained on synthetic data lose fidelity to the original distribution and converge towards a single point estimate.

How might historical discrimination impact the effectiveness of algorithmic reparation initiatives

Historical discrimination can impact the effectiveness of algorithmic reparation initiatives by influencing how marginalized groups are represented in datasets used for training machine learning models. If historical discrimination has led to underrepresentation or misrepresentation of certain groups in past datasets, this bias will carry forward into future models trained on these datasets. As a result, algorithmic reparation efforts may struggle to address systemic inequities rooted in historical injustices unless deliberate steps are taken to mitigate these biases through careful curation of training data and thoughtful design of intervention strategies that account for intersectional identities affected by discrimination.