BiasQD-DT: Creating Interpretable Trade-Offs in Classifier Bias with Quality-Diversity Optimization via Decision Trees

Interpretable and fair generalized additive neural networks via multi-objective learning.

Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve interpretability. This paper focuses on neural network (NN)-based generalized additive models (GAMs), a class of self-interpretable models. While most existing research has prioritized improving the accuracy of NN-based GAMs, their interpretability remains largely underexplored. To address this gap, this paper introduces explicit quantitative metrics for evaluating the interpretability of NN-based GAMs, empirically examines their effectiveness, and explores strategies for improving interpretability within these models. In addition, the simultaneous and explicit optimization of both interpretability and fairness, along with their trade-offs and the underlying reasons, remains underexplored. To address this, we propose a multi-objective neural basis model (MONBM) framework based on multi-objective evolutionary learning to consider accuracy, interpretability, and fairness simultaneously. A partial retraining strategy is further developed to facilitate the practical application of evolutionary multi-objective optimization to deep model architectures. Based on MONBM, this paper reveals the complex relationships between these dimensions and the reasons behind these intricate relationships. This analysis demonstrates how multi-objective optimization can be combined with self-interpretable models to reveal relationships among trustworthiness objectives. In addition, MONBM obtains a set of models with different trade-offs between dimensions, and the competitiveness of the approach is validated by comparing it with state-of-the-art methods.

DECODE: Decision Tree Capturing Opaque Decisions via Coverage-Driven Synthetic Sampling

In the context of smart manufacturing, Explainable AI has emerged as an essential solution to ensure trust in complex Machine Learning model decisions. However, most employed methods are limited to feature relevance scoring, lacking in providing a human-interpretable description of model behaviour. Surrogate models, however, address this gap by approximating a complex predictor through an interpretable model, with treebased surrogates striking a great balance between performance, interpretability, and deployability. In this paper, we propose DECODE, a tree-based surrogate framework combined with an Empty Space Search (ESS) synthetic sampling strategy. Unlike commonly employed synthetic sampling approaches that are either contrained to a specific data distribution or focused on local decision-boundary neighbourhoods, ESS aims to maximise data coverage across a dataset's feature range, enabling the surrogate to characterise model behaviour in low-density regions that reflect rare or abnormal conditions. Experiments on multiple low-dimensional datasets and with multiple classifiers show that an ESS-trained DECODE model substantially improves model behaviour explainability, measured through fidelity and rule overlap metrics, while maintaining satisfactory predictive performance with its teacher model. These results highlight the importance of coverage-centred synthetic training to achieve faithful surrogate explanations that go beyond high density data regimes.

FIFT: Feature Importance-Guided Fairness Testing for machine learning software

Results indicate that global feature importance, used as an active search signal rather than a post-hoc diagnostic, improves both the effectiveness and the efficiency of individual fairness testing.

Principled Bias Detection and Mitigation across the ML Data Lifecycle

Too Much of the Same: From Algorithmic to Human Bias in Learning to Defer

It is demonstrated that standard LtD strategies show class-dependent sampling bias in classification tasks in practice, and thus may disproportionately defer the minority classes when applied to imbalanced datasets, and that such asymmetries in task delegation may trigger human biases, ultimately leading to poorer downstream decision making.

Generative AI bias against scientific novelty: a cautionary tale from a small-sample evaluation of research proposals

The results raise concerns that LLM-assisted evaluation may under-select proposals that human reviewers identify as highly novel, potentially reflecting the statistical logic of next-token prediction trained on past scientific outputs.