Selective Data Expansion for Model Performance

arXiv:2412.03009 · cs.LG, cs.CY · Submitted 2024-12-04 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Selective Data Expansion for Model Performance".

Jane: Here's the gist of the paper. The gist The DATASIFT framework, based on data valuation and multi-armed bandits, efficiently acquires additional labeled data points to rapidly improve the fairness of a machine learning model.

Tom: Problem Definition and Context The research addresses the task of determining which additional data points must be acquired to enhance the fairness of a model learned on training data, given an acquisition budget.

Jane: The problem is formally defined as: "Given a model trained on Dtrain, fairness metric F, data pool D, and acquisition budget B, we address the problem of determining additional data points Dacq ⊂ D that must be acquired such that Dacq ≤ B and the model learned on Dtrain ∪ Dacq is substantially fairer than the original model learned on Dtrain alone (i.e., FDtrain∪Dacq < FDtrain).

Tom: Existing literature often focuses on traditional performance metrics like model accuracy or loss, but none directly apply to the metric of model fairness.

Paper summary: Tom: So we're looking at this paper, "Selective Data Expansion for Model Performance." It’s about how to quickly get better fairness in a machine learning model without just throwing random data at it.

Jane: Right. The main idea seems to be that not all the data points you have are equally useful when trying to fix unfairness in an AI model, and this paper proposes a system to figure out which ones are most valuable to grab next.

Lu: It focuses on acquiring additional labeled data points for training the downstream model because it explores earlier stages of pipelines for bias mitigation, which is something that hasn't been looked at much before.

Meng: So what’s the core mechanism here? How does this selective approach actually work instead of just picking a random batch?

Tom: Well, they present something called DATASIFT, which uses an idea based on data valuation and multi-armed bandits to decide the order in which to acquire data points.

Jane: That sounds like they are trying to be smart about their budget. They’re not just grabbing whatever is next; they’re valuing the data first.

Lu: Exactly, and it does this by partitioning the entire pool of data and then using a multi-armed bandit approach to balance exploration versus exploitation.

Meng: And how do they decide which partition is worth looking at? Is that based on some pre-existing metric, or is it dynamic?

Tom: They select a partition, randomly sample a batch from it, evaluate how much fairness improves by acquiring that batch, and then they update the utility of those partitions based on that benefit.

Jane: That sounds like an iterative process where the system learns what kind of data helps the most before spending more effort on it.

Lu: To speed up that evaluation part, they use something called influence functions to estimate how much acquiring a batch affects fairness without having to retrain the whole model every time.

Meng: Influence functions sound complicated. So, what’s the practical payoff for someone trying to deploy this at scale? Does it make training faster?

Tom: They actually show that this approach can significantly improve fairness even when you only acquire a few data points, which is pretty impressive given the complexity of fairness metrics.

Jane: It seems like the paper is pointing toward a way to be much more efficient with data acquisition than just randomly sampling from the pool.

Lu: The paper shows that by using this valuation method, they construct near-optimal batches instead of just picking whatever comes up next, and they mention an O(N times T r) complexity for finding the most valuable points.

Meng: That complexity sounds like it's manageable for testing, but how robust is this thing when you move from simulation to real traffic?

Tom: The experiments on real-world and synthetic datasets show that DATASIFT consistently outperforms other methods they tested, like Random or Entropy approaches, in actually improving the fairness metric.

Jane: So while the theory is complex with all the partitioning and valuation, they’re showing empirical evidence that this systematic selection process yields better results for fairness.

Lu: And they note that incorporating data valuation into the multi-armed bandit framework through DATASIFT-Inf leads to a much better outcome than just using influence estimation alone, because it adjusts its decision in every iteration to aim for zero bias.

Meng: What about the trade-off? The paper mentions tuning hyperparameters like the batch size and an exploration-exploitation parameter alpha. Does that mean there's a specific sweet spot we should aim for?

Tom: They found that a moderate value for alpha, around zero point one, demonstrated better performance while keeping things stable throughout the entire data acquisition process <ref:2412.03009#pg1>.

Jane: So it’s not about just picking the biggest batch or the smallest one; there seems to be a balance needed in how much you explore versus how much you exploit what you already think is good.

Lu: Also, they showed that DATASIFT-Inf is quite robust across different batch sizes and partitioning criteria, suggesting neither the smallest nor the largest batch size gives you the highest fairness result.

Meng: From a deployment standpoint, if we use this, does it mean less overall computation compared to brute-force methods? I’m worried that "carefully selecting batches" might just be slow if the selection process itself is heavy.

Tom: They found that DATASIFT-Inf requires significantly less computation than standard DATASIFT because they stop evaluating early once they see a significant improvement in fairness, which saves you time.

Jane: It seems like the main message here is that data valuation isn't just academic; it’s crucial for actually discovering useful data points when your goal is specifically model fairness.

Lu: The paper concludes by establishing DATASIFT as a principled framework that combines reinforcement learning with data valuation to determine the most valuable data points for improving model fairness.

Meng: So, if I had to sum up what this means practically, it’s that we can be much more surgical and efficient when we are trying to patch bias into an existing AI system.

Tom: It shows that focusing on earlier stages of pipeline mitigation by strategically acquiring data—rather than just accepting whatever data we get—is a viable path to fairer decisions in healthcare or finance.

Jane: That's the big picture, moving away from just hoping for the best with data toward actively curating it for specific outcomes like fairness.

Conclusion: Tom: So we’re wrapping up this look at "Selective Data Expansion for Model Performance." The authors are focusing on how to strategically pick which data points to grab when you need to fix a model's bias.

Jane: Right, so they’re not just taking a random chunk of data; they’ve got this whole framework, DATASIFT, that uses valuation and multi-armed bandits to decide what's worth grabbing next.

Lu: It really gets at the core idea that not all data points in your pool are equally useful when you're trying to improve fairness.

Meng: So when you look at the title, "Selective Data Expansion," it sounds like they’re talking about being surgical with your data acquisition budget.

Tom: Exactly. They’re showing that even acquiring just a few specific data points can make a big difference in how fair an AI model is performing compared to training on the original set alone.

Jane: It seems like the authors are arguing that focusing on earlier stages of pipeline mitigation, rather than just tweaking the final model, is where this kind of data acquisition strategy really matters.

Lu: I think what’s interesting is how they use influence functions to estimate those effects without having to retrain the entire model every single time you sample a batch.

Tom: That’s smart for deployment because retraining takes so much time and compute; estimating the effect first saves a ton of work down the line.

Jane: And what this means for us is that instead of just blindly adding data, we can use valuation to build an acquisition order that actually steers the model toward fairness more directly.

Lu: The paper’s conclusion is pretty solid, showing that this combination of reinforcement learning and data valuation is a principled way to find the most useful data points for specific fairness goals.

Meng: It suggests that if you're running into bias issues, you don't need to just throw more raw data at it; you need a smart plan for getting the right labeled examples.

Tom: Yeah, it’s about being intentional with your data budget when the goal is something as specific as fairness.

Jane: And that leads us next to how this selective acquisition fits into the bigger picture of deploying these models in real-world applications.

Jahid Hasan, Romila Pradhan

Purdue University

cs.LG, cs.CY

Submitted: 2024-12-04

Updated: 2026-10-16

Comments: 19 pages, 9 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 84/100

The gist: The gist The DATASIFT framework, based on data valuation and multi-armed bandits, efficiently acquires additional labeled data points to rapidly improve the fairness of a machine learning model.

Key concepts

Data Valuation: This technique assigns a value to each data point based on its potential contribution to reducing model unfairness. It uses first-order influence functions to estimate how much changing or adding a specific data point will affect the final fairness metric of the trained model.
Multi-Armed Bandits (MAB): The problem is framed as an MAB, where each partition of the data pool is an 'arm' to pull. The algorithm balances exploration (trying new partitions) and exploitation (sampling batches from currently high-utility partitions) to find the best sequence for acquiring data points.
Influence Functions: These functions estimate the local effect of a single training data point on the model's fairness. By calculating these influences, DATASIFT can construct batches by selecting data points predicted to have the highest positive impact on achieving fairness goals.
Upper Confidence Bound (UCB): This is an algorithm used within DATASIFT to decide which partition or batch to sample next. It uses a reward score that combines both model fairness and accuracy, balancing the need to exploit known good data with exploring potentially better, unknown data points.

Terminology

Influence Functions — 1× in this paper · explained in 3 episodes

Summary

The gist The DATASIFT framework, based on data valuation and multi-armed bandits, efficiently acquires additional labeled data points to rapidly improve the fairness of a machine learning model.

Problem Definition and Context

The research addresses the task of determining which additional data points must be acquired to enhance the fairness of a model learned on training data, given an acquisition budget. The problem is formally defined as: "Given a model trained on Dtrain, fairness metric F, data pool D, and acquisition budget B, we address the problem of determining additional data points Dacq ⊂ D that must be acquired such that Dacq ≤ B and the model learned on Dtrain ∪ Dacq is substantially fairer than the original model learned on Dtrain alone (i.e., FDtrain∪Dacq < FDtrain). Existing literature often focuses on traditional performance metrics like model accuracy or loss, but none directly apply to the metric of model fairness.

DATASIFT Framework Overview

DATASIFT presents a framework that casts the data acquisition task for model fairness as a multi-armed bandit (MAB) problem. The core idea is to split the data pool into smaller partitions and decide the order of acquiring data points by balancing exploration and exploitation. The process involves selecting a partition, sampling a batch, evaluating its benefit on model fairness, and updating the utility of partitions based on that benefit. To speed up evaluation, DATASIFT leverages influence functions to estimate the effect of acquiring a batch without retraining the model. The algorithm uses an Upper Confidence Bound (UCB) algorithm built on a reward score that incorporates both fairness and accuracy of the learned model.

Data Valuation and Influence Functions

To construct a batch for acquisition, DATASIFT-Inf incorporates data valuation using first-order influence functions to quantify the contribution of training data points toward model fairness. The influence of up-weighting a data point d in the training data by epsilon is computed as Iθ(d) = -H−1θ ∇θL(d, θ∗) (Equation 4). Using the chain rule of differentiation, this allows for the estimation of the first-order influence of a single training data point d on model fairness by approximating dF∗ϵ ≈ 1/n IF (d). This estimated influence is then used to construct a batch by selecting the top-K data points with the highest predicted influence on fairness as determined by a regression model R.

Comparison with Competing Methods and Results

Experiments evaluate DATASIFT against several competing methods, including Random, Entropy, Inf., and AutoData. DATASIFT consistently outperforms the other methods in improving fairness for most of the datasets. The paper demonstrates that incorporating data valuation into the MAB framework via DATASIFT-Inf significantly improves effectiveness and efficiency compared to standard DATASIFT by constructing near-optimal batches. While Inf solely relies on influence estimation, which can introduce bias in the other direction, DATASIFT-Inf adjusts its decision in each iteration to lead the model toward zero bias.

Effect of Hyperparameters and Scalability

The study investigates the sensitivity of DATASIFT to hyperparameters such as batch size and the exploration-exploitation trade-off parameter α. The results show that a moderate value for α, such as 0.1, demonstrates better performance maintaining stability throughout the data acquisition process. Furthermore, DATASIFT-Inf is shown to be robust across different batch sizes and partitioning criteria, suggesting that neither the smallest nor largest batch sizes yield the highest fairness. Scalability analysis indicates that DATASIFT-Inf requires significantly less computation compared to DATASIFT because it carefully selects batches providing a significant improvement in fairness, leading to an early termination of the evaluation process.

Conclusion

The paper presents DATASIFT as a principled framework that integrates reinforcement learning with data valuation to determine the most valuable data points for model fairness. The findings demonstrate that data valuation is crucial in the effective discovery of useful data points and that DATASIFT-Inf is both effective and efficient in identifying data points valuable for model fairness. Future work plans include expanding the data valuation module to efficiently cater to non-parametric ML algorithms. The study concludes that DATASIFT is both effective and efficient in identifying data points valuable for model fairness.

Improvements for AI systems

Data Acquisition for Improving Fairness Using Reinforcement Learning can be used to determine an optimal sequence for acquiring data points from a pool D to maximize model fairness within a budget B by framing it as a multi-armed bandit problem. This system selects partitions based on an upper confidence bound score that balances exploration and exploitation of data acquisition strategies.
DATASIFT-Inf can be used to construct batches by leveraging first-order influence functions to estimate the effect of training data points on model fairness, which speeds up the process compared to retraining a model for every batch evaluation. This allows the system to select batches that are the top-K data points with the highest influence on fairness as predicted by R.
The improved system can rapidly improve a machine learning model's fairness by acquiring only a fraction of the total data pool, achieving fairness levels comparable to or even better than when the entire dataset is acquired. This is demonstrated by DATASIFT-Inf exhibiting the most improvement in fairness with the least amount of additional data acquired.