Feature attribution, the ability to localize regions of the input data that
are relevant for classification, is an important capability for machine
learning models in scientific and biomedical domains. Current methods for
feature attribution, which rely on "explaining" the predictions of end-to-end
classifiers, suffer from imprecise feature localization and are inadequate for
use with small sample sizes and high-dimensional datasets due to computational
challenges. We introduce prospector heads, an efficient and interpretable
alternative to explanation-based methods for feature attribution that can be
applied to any encoder and any data modality. Prospector heads generalize
across modalities through experiments on sequences (text), images (pathology),
and graphs (protein structures), outperforming baseline attribution methods by
up to 49 points in mean localization AUPRC. We also demonstrate how prospector
heads enable improved interpretation and discovery of class-specific patterns
in the input data. Through their high performance, flexibility, and
generalizability, prospectors provide a framework for improving trust and
transparency for machine learning models in complex domains.

提出了一种有效且可解释的特征归属方法 prospectors heads，能够推广到序列、图像和图谱数据领域，并在特征定位上具有优越性能，提供了一个改善复杂领域机器学习模型信任和透明度的框架。

探索者头部：大规模模型与数据的广义特征归因

Prospector Heads: Generalized Feature Attribution for Large Models &  Data

Machine Learning's proliferation in critical fields such as healthcare,
banking, and criminal justice has motivated the creation of tools which ensure
trust and transparency in ML models. One such tool is Actionable Recourse (AR)
for negatively impacted users. AR describes recommendations of cost-efficient
changes to a user's actionable features to help them obtain favorable outcomes.
Existing approaches for providing recourse optimize for properties such as
proximity, sparsity, validity, and distance-based costs. However, an
often-overlooked but crucial requirement for actionability is a consideration
of User Preference to guide the recourse generation process. In this work, we
attempt to capture user preferences via soft constraints in three simple forms:
i) scoring continuous features, ii) bounding feature values and iii) ranking
categorical features. Finally, we propose a gradient-based approach to identify
User Preferred Actionable Recourse (UP-AR). We carried out extensive
experiments to verify the effectiveness of our approach.

机器学习在诸如医疗保健、银行和刑事司法等关键领域的普及促使了确保 ML 模型的信任和透明性的工具的创建，其中一个工具是用于受负面影响的用户的可操作补救措施（AR），AR 描述了对用户的可行性特征的成本效益改变的建议，以帮助他们获得有利的结果；基于现有方法的补救方式优化了接近性、稀疏性、有效性和基于距离的成本等属性，然而，行动性的一个常被忽视但至关重要的要求是考虑用户偏好来指导补救生成过程，本研究尝试通过三种简单形式（i）评分连续特征，（ii）限制特征值范围和（iii）对分类特征进行排序）来捕捉用户偏好，最后，我们提出了一种基于梯度的方法来识别用户优选的可操作补救措施（UP-AR），我们进行了大量实验证实我们方法的有效性。