Fairness in Large Language Models: A Taxonomic Survey

Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead to discriminatory outcomes against certain communities, particularly marginalized populations, prompting extensive study in fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in traditional machine learning, entails exclusive backgrounds, taxonomies, and fulfillment techniques. To this end, this survey presents a comprehensive overview of recent advances in the existing literature concerning fair LLMs. Specifically, a brief introduction to LLMs is provided, followed by an analysis of factors contributing to bias in LLMs. Additionally, the concept of fairness in LLMs is discussed categorically, summarizing metrics for evaluating bias in LLMs and existing algorithms for promoting fairness. Furthermore, resources for evaluating bias in LLMs, including toolkits and datasets, are summarized. Finally, existing research challenges and open questions are discussed

A Mixing-Accelerated Primal-Dual Proximal Algorithm for Distributed Nonconvex Optimization

In this paper, we develop a distributed mixing-accelerated primal-dual proximal algorithm, referred to as MAP-Pro, which enables nodes in multi-agent networks to cooperatively minimize the sum of their nonconvex, smooth local cost functions in a decentralized fashion. The proposed algorithm is constructed upon minimizing a computationally inexpensive augmented-Lagrangian-like function and incorporating a time-varying mixing polynomial to expedite information fusion across the network. The convergence results derived for MAP-Pro include a sublinear rate of convergence to a stationary solution and, under the Polyak-{\L}ojasiewics (P-{\L}) condition, a linear rate of convergence to the global optimal solution. Additionally, we may embed the well-noted Chebyshev acceleration scheme in MAP-Pro, which generates a specific sequence of mixing polynomials with given degrees and enhances the convergence performance based on MAP-Pro. Finally, we illustrate the competitive convergence speed and communication efficiency of MAP-Pro via a numerical example.Comment: 8 pages, 2 figures, accepted by ACC202

Individual Fairness Guarantee in Learning with Censorship

Algorithmic fairness, studying how to make machine learning (ML) algorithms fair, is an established area of ML. As ML technologies expand their application domains, including ones with high societal impact, it becomes essential to take fairness into consideration when building ML systems. Yet, despite its wide range of socially sensitive applications, most work treats the issue of algorithmic bias as an intrinsic property of supervised learning, i.e., the class label is given as a precondition. Unlike prior fairness work, we study individual fairness in learning with censorship where the assumption of availability of the class label does not hold, while still requiring that similar individuals are treated similarly. We argue that this perspective represents a more realistic model of fairness research for real-world application deployment, and show how learning with such a relaxed precondition draws new insights that better explain algorithmic fairness. We also thoroughly evaluate the performance of the proposed methodology on three real-world datasets, and validate its superior performance in minimizing discrimination while maintaining predictive performance

Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking

The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern. Addressing these fairness bugs often involves sacrificing ML performance, such as accuracy. To address this issue, we present a novel counterfactual approach that uses counterfactual thinking to tackle the root causes of bias in ML software. In addition, our approach combines models optimized for both performance and fairness, resulting in an optimal solution in both aspects. We conducted a thorough evaluation of our approach on 10 benchmark tasks using a combination of 5 performance metrics, 3 fairness metrics, and 15 measurement scenarios, all applied to 8 real-world datasets. The conducted extensive evaluations show that the proposed method significantly improves the fairness of ML software while maintaining competitive performance, outperforming state-of-the-art solutions in 84.6% of overall cases based on a recent benchmarking tool

Preventing Discriminatory Decision-making in Evolving Data Streams

Bias in machine learning has rightly received significant attention over the last decade. However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting. Despite the wide prevalence of online systems in the real world, work on identifying and correcting bias in the online setting is severely lacking. The unique challenges of the online environment make addressing bias more difficult than in the offline setting. First, Streaming Machine Learning (SML) algorithms must deal with the constantly evolving real-time data stream. Second, they need to adapt to changing data distributions (concept drift) to make accurate predictions on new incoming data. Adding fairness constraints to this already complicated task is not straightforward. In this work, we focus on the challenges of achieving fairness in biased data streams while accounting for the presence of concept drift, accessing one sample at a time. We present Fair Sampling over Stream ($FS^2$), a novel fair rebalancing approach capable of being integrated with SML classification algorithms. Furthermore, we devise the first unified performance-fairness metric, Fairness Bonded Utility (FBU), to evaluate and compare the trade-off between performance and fairness of different bias mitigation methods efficiently. FBU simplifies the comparison of fairness-performance trade-offs of multiple techniques through one unified and intuitive evaluation, allowing model designers to easily choose a technique. Overall, extensive evaluations show our measures surpass those of other fair online techniques previously reported in the literature

Non-Hermitian topological whispering gallery

In 1878, Lord Rayleigh observed the highly celebrated phenomenon of sound waves that creep around the curved gallery of St Paul's Cathedral in London1,2. These whispering-gallery waves scatter efficiently with little diffraction around an enclosure and have since found applications in ultrasonic fatigue and crack testing, and in the optical sensing of nanoparticles or molecules using silica microscale toroids. Recently, intense research efforts have focused on exploring non-Hermitian systems with cleverly matched gain and loss, facilitating unidirectional invisibility and exotic characteristics of exceptional points3,4. Likewise, the surge in physics using topological insulators comprising non-trivial symmetry-protected phases has laid the groundwork in reshaping highly unconventional avenues for robust and reflection-free guiding and steering of both sound and light5,6. Here we construct a topological gallery insulator using sonic crystals made of thermoplastic rods that are decorated with carbon nanotube films, which act as a sonic gain medium by virtue of electro-thermoacoustic coupling. By engineering specific non-Hermiticity textures to the activated rods, we are able to break the chiral symmetry of the whispering-gallery modes, which enables the out-coupling of topological "audio lasing" modes with the desired handedness. We foresee that these findings will stimulate progress in non-destructive testing and acoustic sensing.This work was supported by the National Basic Research Program of China (2017YFA0303702), NSFC (12074183, 11922407, 11904035, 11834008, 11874215 and 12104226) and the Fundamental Research Funds for the Central Universities (020414380181). Z.Z. acknowledges the support from the China National Postdoctoral Program for Innovative Talents (BX20200165) and the China Postdoctoral Science Foundation (2020M681541). L.Z. acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement 801538. J.C. acknowledges support from the European Research Council (ERC) through the Starting Grant 714577 PHONOMETA and from the MINECO through a Ramón y Cajal grant (grant number RYC-2015-17156)

The interaction between changes of muscle activation and cortical network dynamics during isometric elbow contraction: a sEMG and fNIRS study

Objective: The relationship between muscle activation during motor tasks and cerebral cortical activity remains poorly understood. The aim of this study was to investigate the correlation between brain network connectivity and the non-linear characteristics of muscle activation changes during different levels of isometric contractions.Methods: Twenty-one healthy subjects were recruited and were asked to perform isometric elbow contractions in both dominant and non-dominant sides. Blood oxygen concentrations in brain from functional Near-infrared Spectroscopy (fNIRS) and surface electromyography (sEMG) signals in the biceps brachii (BIC) and triceps brachii (TRI) muscles were recorded simultaneously and compared during 80% and 20% of maximum voluntary contraction (MVC). Functional connectivity, effective connectivity, and graph theory indicators were used to measure information interaction in brain activity during motor tasks. The non-linear characteristics of sEMG signals, fuzzy approximate entropy (fApEn), were used to evaluate the signal complexity changes in motor tasks. Pearson correlation analysis was used to examine the correlation between brain network characteristic values and sEMG parameters under different task conditions.Results: The effective connectivity between brain regions in motor tasks in dominant side was significantly higher than that in non-dominant side under different contractions (p < 0.05). The results of graph theory analysis showed that the clustering coefficient and node-local efficiency of the contralateral motor cortex were significantly varied under different contractions (p < 0.01). fApEn and co-contraction index (CCI) of sEMG under 80% MVC condition were significantly higher than that under 20% MVC condition (p < 0.05). There was a significant positive correlation between the fApEn and the blood oxygen value in the contralateral brain regions in both dominant or non-dominant sides (p < 0.001). The node-local efficiency of the contralateral motor cortex in the dominant side was positively correlated with the fApEn of the EMG signals (p < 0.05).Conclusion: In this study, the mapping relationship between brain network related indicators and non-linear characteristic of sEMG in different motor tasks was verified. These findings provide evidence for further exploration of the interaction between the brain activity and the execution of motor tasks, and the parameters might be useful in evaluation of rehabilitation intervention

The association between HLA-B variants and amoxicillin-induced severe cutaneous adverse reactions in Chinese han population

BackgroundAmoxicillin (AMX) is among the most prescribed and the best tolerated antimicrobials worldwide. However, it can occasionally trigger severe cutaneous adverse reactions (SCAR) with a significant morbidity and mortality. The genetic factors that may be relevant to AMX-induced SCAR (AMX-SCAR) remain unclear. Identification of the genetic risk factor may prevent patients from the risk of AMX exposure and resume therapy with other falsely implicated drugs.MethodologyFour patients with AMX-SCAR, 1,000 population control and 100 AMX-tolerant individuals were enrolled in this study. Both exome-wide and HLA-based association studies were conducted. Molecular docking analysis was employed to simulate the interactions between AMX and risk HLA proteins.ResultsCompared with AMX-tolerant controls, a significant association of HLA-B*15:01 with AMX-SCAR was validated [odds ratio (OR) = 22.9, 95% confidence interval (CI): 1.68–1275.67; p = 7.34 × 10−3]. Moreover, 75% carriers of HLA-B*15:01 in four patients with AMX-SCAR, and the carrier frequency of 10.7% in 1,000 control individuals and 11.0% in 100 AMX-tolerant controls, respectively. Within HLA-B protein, the S140 present in all cases and demonstrated the strongest association with AMX-SCAR [OR = 53.5, p = 5.18 × 10−4]. Molecular docking results also confirmed the interaction between AMX and S140 of the HLA-B protein, thus eliminating the false-positive results during in association analysis.ConclusionOur findings suggest that genetic susceptibility may be involved in the development of AMX-SCAR in Han Chinese. However, whether the HLA-B variants observed in this study can be used as an effective genetic marker of AMX-induced SCAR still needs to be further explored in larger cohort studies and other ethnic populations

FG2 AN: Fairness-Aware Graph Generative Adversarial Networks

Graph generation models have gained increasing popularity and success across various domains. However, most research in this area has concentrated on enhancing performance, with the issue of fairness remaining largely unexplored. Existing graph generation models prioritize minimizing graph reconstruction’s expected loss, which can result in representational disparities in the generated graphs that unfairly impact marginalized groups. This paper addresses this socially sensitive issue by conducting the first comprehensive investigation of fair graph generation models by identifying the root causes of representational disparities, and proposing a novel framework that ensures consistent and equitable representation across all groups. Additionally, a suite of fairness metrics has been developed to evaluate bias in graph generation models, standardizing fair graph generation research. Through extensive experiments on five real-world datasets, the proposed framework is demonstrated to outperform existing benchmarks in terms of graph fairness while maintaining competitive prediction performance

Detecting Camouflaged Objects via Multi-Stage Coarse-to-Fine Refinement

Camouflaged objects are typically assimilated into their surroundings. Consequently, in contrast to generic object detection/segmentation, camouflaged object detection proves to be considerably more intricate due to the indistinct boundaries and heightened intrinsic similarities between foreground targets and the surrounding environment. Despite the proposition of numerous algorithms that have demonstrated commendable performance across various scenarios, these approaches may still grapple with blurred boundaries, leading to the inadvertent omission of camouflaged targets in challenging scenes. In this paper, we introduce a multi-stage framework tailored for segmenting camouflaged objects through a process of coarse-to-fine refinement. Specifically, our network encompasses three distinct decoders, each fulfilling a unique role in the model. In the initial decoder, we introduce the Bi-directional Locating Module to excavate foreground and background cues, enhancing target localization. The second decoder focuses on leveraging boundary information to augment overall performance, incorporating the Multi-level Feature Fusion Module to generate prediction maps with finer boundaries. Subsequently, the third decoder introduces the Mask-guided Fusion Module, designed to process high-resolution features under the guidance of the second decoder’s results. This approach enables the preservation of structural details and the generation of fine-grained prediction maps. Through the integration of the three decoders, our model effectively identifies and segments camouflaged targets. Extensive experiments are conducted on three commonly used benchmark datasets. The results of these experiments demonstrate that, even without the application of pre-processing or post-processing techniques, our model outperforms 14 state-of-the-art algorithms