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

• URL: http://arxiv.org/abs/2405.08027v4
• Date: Fri, 11 Oct 2024 00:37:14 GMT
• Title: Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions
• Authors: Tian Xie, Xueru Zhang,
• Abstract summary: This paper investigates the long-term impacts when machine learning models are retrained with model-annotated samples. We find that agents are increasingly likely to receive positive decisions as the model gets retrained. We propose a refined retraining process to stabilize the dynamics.
• Score: 10.448052192725168
• License:
• 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.

Related papers

• How Aligned are Generative Models to Humans in High-Stakes Decision-Making? [10.225573060836478]
  Large generative models (LMs) are increasingly being considered for high-stakes decision-making. This work considers how such models compare to humans and predictive AI models on a specific case of recidivism prediction.
  arXiv  Detail & Related papers  (2024-10-20T19:00:59Z)
• Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models [115.501751261878]
  Fine-tuning language models(LMs) on human-generated data remains a prevalent practice. We investigate whether we can go beyond human data on tasks where we have access to scalar feedback. We find that ReST$EM$ scales favorably with model size and significantly surpasses fine-tuning only on human data.
  arXiv  Detail & Related papers  (2023-12-11T18:17:43Z)
• Improving Generalization of Alignment with Human Preferences through Group Invariant Learning [56.19242260613749]
  Reinforcement Learning from Human Feedback (RLHF) enables the generation of responses more aligned with human preferences. Previous work shows that Reinforcement Learning (RL) often exploits shortcuts to attain high rewards and overlooks challenging samples. We propose a novel approach that can learn a consistent policy via RL across various data groups or domains.
  arXiv  Detail & Related papers  (2023-10-18T13:54:15Z)
• Scaling Laws Do Not Scale [54.72120385955072]
  Recent work has argued that as the size of a dataset increases, the performance of a model trained on that dataset will increase. We argue that this scaling law relationship depends on metrics used to measure performance that may not correspond with how different groups of people perceive the quality of models' output. Different communities may also have values in tension with each other, leading to difficult, potentially irreconcilable choices about metrics used for model evaluations.
  arXiv  Detail & Related papers  (2023-07-05T15:32:21Z)
• Non-Invasive Fairness in Learning through the Lens of Data Drift [88.37640805363317]
  We show how to improve the fairness of Machine Learning models without altering the data or the learning algorithm. We use a simple but key insight: the divergence of trends between different populations, and, consecutively, between a learned model and minority populations, is analogous to data drift. We explore two strategies (model-splitting and reweighing) to resolve this drift, aiming to improve the overall conformance of models to the underlying data.
  arXiv  Detail & Related papers  (2023-03-30T17:30:42Z)
• Bias-inducing geometries: an exactly solvable data model with fairness implications [13.690313475721094]
  We introduce an exactly solvable high-dimensional model of data imbalance. We analytically unpack the typical properties of learning models trained in this synthetic framework. We obtain exact predictions for the observables that are commonly employed for fairness assessment.
  arXiv  Detail & Related papers  (2022-05-31T16:27:57Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses [16.874125120501944]
  We show that we can use strategic responses effectively to recover causal relationships between the observable features and outcomes we wish to predict. Our work establishes a novel connection between strategic responses to machine learning models and instrumental variable (IV) regression.
  arXiv  Detail & Related papers  (2021-07-12T22:12:56Z)
• On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study [65.17429512679695]
  In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Despite ADC's intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models.
  arXiv  Detail & Related papers  (2021-06-02T00:48:33Z)
• Data-driven model reduction of agent-based systems using the Koopman generator [0.3867363075280544]
  We show how Koopman operator theory can be used to derive reduced models of agent-based systems. Our goal is to learn coarse-grained models and to represent the reduced dynamics by ordinary or differential equations.
  arXiv  Detail & Related papers  (2020-12-14T17:12:54Z)
• Learning Opinion Dynamics From Social Traces [25.161493874783584]
  We propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces. We showcase our proposal by translating a classical agent-based model of opinion dynamics into its generative counterpart. We apply our model to real-world data from Reddit to explore the long-standing question about the impact of backfire effect.
  arXiv  Detail & Related papers  (2020-06-02T14:48:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.