FairLMs is a Python library designed to streamline fairness research in language models by unifying bias measurement, mitigation, and evaluation evidence. It offers 33 intrinsic and extrinsic metrics, 14 mitigation components across four intervention categories, 14 diagnostic tools, adapters for major Transformer architectures and hosted APIs, and benchmark loaders. The library enforces explicit declarations of model capabilities and input requirements, ensuring compatibility and reproducibility across components and datasets.
arXiv:2604. 27011v2 Announce Type: replace-cross Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation.
arXiv:2511. 00079v2 Announce Type: replace Abstract: flowengineR is an R package designed to provide a modular and extensible framework for building reproducible algorithmic workflows for general-purpose machine learning pipelines.
FairCompressAgent (FCA) is an agentic framework that unifies fairness-aware pruning, incremental quantization, and sparse low‑rank factorization for FPGA deployment. A language‑model planner selects compression configurations based on model profiles and measured outcomes, while an execution layer handles compression, fine‑tuning, evaluation, and constraint‑based selection. Experiments on Fitzpatrick‑17k with VGG‑11 show FCA can reduce inference tensor storage by 59.54% under accuracy constraints, improve validation average precision, and lower equalized opportunity, achieving similar results to one‑shot planning with fewer candidate evaluations.
arXiv:2609.16321v1 Announce Type: cross Abstract: Existing fairness analysis tools predominantly operate as post-training evaluation frameworks, requiring practitioners to complete the full model dev...
arXiv:2608. 08700v1 Announce Type: new Abstract: Reliable evaluation of tool routing is critical as Large Language Models increasingly operate as autonomous agents.
The paper argues that fairness failures in generative models arise mainly from inadequate evaluation practices, making fairness findings hard to compare or use for deployment. It diagnoses common empirical and conceptual shortcomings in current methods and calls for a move toward standardized, generative‑specific evaluation. The authors introduce Fairness Cards, a minimal reporting artifact that explicitly documents evaluation choices—such as prompt families, counterfactual protocols, metrics, and refusal handling—to improve reproducibility, comparability, and accountability.
arXiv:2607. 09424v1 Announce Type: cross Abstract: We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English.
arXiv:2601.03087v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit systematic biases across demographic groups. Auditing is proposed as an accountability tool for black-box LLM...
arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.
Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized groups, remain under-...
arXiv:2607. 21300v1 Announce Type: cross Abstract: Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations.
The paper introduces the AR Fairness Metamodel, a structured framework for representing, analyzing, and comparing fairness scenarios. It incorporates key elements such as agents, resources, and their attributes, and supports both discrete and continuous fairness measures—including equality, equity, group fairness, individual fairness, the Gini index, the Theil index, Jain's fairness index, and a specific measure for Australia's Child Care Subsidy. The metamodel builds on the Tiles framework, offering modular components that can be connected to capture diverse fairness definitions, and includes formal proofs of relationships among group fairness, individual fairness, and envy‑freeness. An open‑source implementation of the Tiles framework is provided to facilitate practical fairness modeling and evaluation across various applications.