Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions

TL;DR

This paper explores the long-term effects of using ML models to annotate data in social domains with strategic human agents, highlighting risks to fairness and proposing solutions for stable retraining.

Contribution

It formalizes the interaction between strategic agents and models, analyzes dynamic evolution, and proposes a refined retraining process to mitigate instability and fairness issues.

Findings

Agents tend to receive more positive decisions over time.

The proportion of positively labeled agents may decrease in the long run.

Enforcing fairness constraints at each round may not benefit disadvantaged groups.

Abstract

As machine learning (ML) models are increasingly used in social domains to make consequential decisions about humans, they often have the power to reshape data distributions. Humans, as strategic agents, continuously adapt their behaviors in response to the learning system. As populations change dynamically, ML systems may need frequent updates to ensure high performance. However, acquiring high-quality human-annotated samples can be highly challenging and even infeasible in social domains. A common practice to address this issue is using the model itself to annotate unlabeled data samples. This paper investigates the long-term impacts when ML models are retrained with model-annotated samples when they incorporate human strategic responses. We first formalize the interactions between strategic agents and the model and then analyze how they evolve under such dynamic interactions. We find that agents are increasingly likely to receive positive decisions as the model gets retrained, whereas the proportion of agents with positive labels may decrease over time. We thus propose a refined retraining process to stabilize the dynamics. Last, we examine how algorithmic fairness can be affected by these retraining processes and find that enforcing common fairness constraints at every round may not benefit the disadvantaged group in the long run. Experiments on (semi-)synthetic and real data validate the theoretical findings.