Trustworthy AI is crucial to the widespread adoption of AI in high-stakes
applications with fairness, robustness, and accuracy being some of the key
trustworthiness metrics. In this work, we propose a controllable framework for
data-centric trustworthy AI (DCTAI)- VTruST, that allows users to control the
trade-offs between the different trustworthiness metrics of the constructed
training datasets. A key challenge in implementing an efficient DCTAI framework
is to design an online value-function-based training data subset selection
algorithm. We pose the training data valuation and subset selection problem as
an online sparse approximation formulation. We propose a novel online version
of the Orthogonal Matching Pursuit (OMP) algorithm for solving this problem.
Experimental results show that VTruST outperforms the state-of-the-art
baselines on social, image, and scientific datasets. We also show that the data
values generated by VTruST can provide effective data-centric explanations for
different trustworthiness metrics.

提出了一种基于数据的可控信任 AI（DCTAI）框架 VTruST，该框架允许用户控制构建训练数据集时信任度指标之间的权衡，通过提出一种在线值函数为基础的训练数据子集选择算法来实现高效的 DCTAI 框架。实验结果表明，VTruST 在社交、图像和科学数据集上优于现有基线算法，并且 VTruST 生成的数据值可为不同信任度指标提供有效的数据中心解释。

VTruST: 基于可控价值函数的数据中心可信 AI 的子集选择

VTruST: Controllable value function based subset selection for  Data-Centric Trustworthy AI

A salient characteristic of large pre-trained language models (PTLMs) is a
remarkable improvement in their generalization capability and emergence of new
capabilities with increasing model capacity and pre-training dataset size.
Consequently, we are witnessing the development of enormous models pushing the
state-of-the-art. It is, however, imperative to realize that this inevitably
leads to prohibitively long training times, extortionate computing costs, and a
detrimental environmental impact. Significant efforts are underway to make PTLM
training more efficient through innovations in model architectures, training
pipelines, and loss function design, with scant attention being paid to
optimizing the utility of training data. The key question that we ask is
whether it is possible to train PTLMs by employing only highly informative
subsets of the training data while maintaining downstream performance? Building
upon the recent progress in informative data subset selection, we show how we
can employ submodular optimization to select highly representative subsets of
the training corpora. Our results demonstrate that the proposed framework can
be applied to efficiently train multiple PTLMs (BERT, BioBERT, GPT-2) using
only a fraction of data while retaining up to $\sim99\%$ of the performance of
the fully-trained models.

通过使用具有高度信息量的训练数据子集训练预训练语言模型，同时保持下游性能，我们展示了如何利用子模块优化来选择高度代表性的训练语料库子集，以有效地训练多个预训练语言模型。