arXiv subjects

Piotr Skalski

Publications and source records attributed to Piotr Skalski.

6 recordsLinked to original sources

arXiv2026

Fairness-Aware Low-Rank Representation Fine-Tuning

Pre-trained foundation models can be efficiently adapted for specific tasks using Low-Rank Adaptation (LoRA), but the fairness properties of these adapted classifiers remain underexplored. Existing fairness-aware fine-tuning methods assume that sensitive attribute labels are available alongside downstream task labels, which often fails in practice due to user consent limitations or privacy constraints. To address this gap, we investigate fairness-aware LoRA fine-tuning using separate datasets for downstream tasks and sensitive attributes. We introduce four fairness-aware LoRA strategies: sensitive unlearning, adversarial debiasing, orthogonality-based disentanglement, and entropy maximization. Through comprehensive experiments on standard algorithmic fairness datasets using an ImageNet pre-trained ViT-Base model, we evaluate these methods across multiple utility and fairness metrics. Our orthogonality-based disentanglement and entropy maximization approaches consistently outperform standard fine-tuning in both overall utility and fairness, while adversarial debiasing shows less consistent improvements and sensitive unlearning proves ineffective for classification tasks. However, fairness-aware methods underperform on certain metrics like subgroup-wise false-positive rate ratios, highlighting fundamental incompatibilities between fairness objectives. These findings demonstrate the potential of fairness-aware LoRA fine-tuning while revealing inherent challenges of simultaneously optimizing multiple fairness criteria in parameter-efficient adaptation.

cs.LG↗

arXiv2024

Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges

Ensuring fairness in transaction fraud detection models is vital due to the potential harms and legal implications of biased decision-making. Despite extensive research on algorithmic fairness, there is a notable gap in the study of bias in fraud detection models, mainly due to the field's unique challenges. These challenges include the need for fairness metrics that account for fraud data's imbalanced nature and the tradeoff between fraud protection and service quality. To address this gap, we present a comprehensive fairness evaluation of transaction fraud models using public synthetic datasets, marking the first algorithmic bias audit in this domain. Our findings reveal three critical insights: (1) Certain fairness metrics expose significant bias only after normalization, highlighting the impact of class imbalance. (2) Bias is significant in both service quality-related parity metrics and fraud protection-related parity metrics. (3) The fairness through unawareness approach, which involved removing sensitive attributes such as gender, does not improve bias mitigation within these datasets, likely due to the presence of correlated proxies. We also discuss socio-technical fairness-related challenges in transaction fraud models. These insights underscore the need for a nuanced approach to fairness in fraud detection, balancing protection and service quality, and moving beyond simple bias mitigation strategies. Future work must focus on refining fairness metrics and developing methods tailored to the unique complexities of the transaction fraud domain.

cs.LG↗

arXiv2024

Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences

Machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction. Most are based on supervised learning with hand-engineered features, which relies heavily on the availability of labelled data. Large self-supervised generative models have shown tremendous success in natural language processing and computer vision, yet so far they haven't been adapted to multivariate time series of financial transactions. In this paper, we present a generative pretraining method that can be used to obtain contextualised embeddings of financial transactions. Benchmarks on public datasets demonstrate that it outperforms state-of-the-art self-supervised methods on a range of downstream tasks. We additionally perform large-scale pretraining of an embedding model using a corpus of data from 180 issuing banks containing 5.1 billion transactions and apply it to the card fraud detection problem on hold-out datasets. The embedding model significantly improves value detection rate at high precision thresholds and transfers well to out-of-domain distributions.

cs.LG↗

arXiv2024

Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems

Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.

cs.CR↗

arXiv2022

Attribution of Predictive Uncertainties in Classification Models

Predictive uncertainties in classification tasks are often a consequence of model inadequacy or insufficient training data. In popular applications, such as image processing, we are often required to scrutinise these uncertainties by meaningfully attributing them to input features. This helps to improve interpretability assessments. However, there exist few effective frameworks for this purpose. Vanilla forms of popular methods for the provision of saliency masks, such as SHAP or integrated gradients, adapt poorly to target measures of uncertainty. Thus, state-of-the-art tools instead proceed by creating counterfactual or adversarial feature vectors, and assign attributions by direct comparison to original images. In this paper, we present a novel framework that combines path integrals, counterfactual explanations and generative models, in order to procure attributions that contain few observable artefacts or noise. We evidence that this outperforms existing alternatives through quantitative evaluations with popular benchmarking methods and data sets of varying complexity.

cs.LG↗

arXiv2021

Distinguishing spin pumping from spin rectification in organic semiconductor-based lateral spin pumping device architectures

Over the last two decades organic spintronics has developed into a striving field with exciting reports of long spin diffusion lengths and spin relaxation times in organic semiconductors (OSCs). Easily processed and inexpensive, OSCs are considered a potential alternative to inorganic materials for use in spintronic applications. Spin currents have been detected in a wide range of materials, however, there is still uncertainty over the origin of the signals. Recently, we explored spin transport through an organic semiconductor with lateral spin injection and detection architectures, where the injected spin current is detected non-locally via spin-to-charge conversion in an inorganic detector. In this work we show that the widely-used control experiments like linear power dependence and inversion of the signal with the magnetic field are not sufficient evidence of spin transport and can lead to an incorrect interpretation of the signal. Here, we use in-plane angular dependent measurements to separate pure spin signal from parasitic effects arising from spin rectification (SREs). Apart from well established anisotropic magnetoresistance (AMR) and anomalous Hall effect (AHE), we observed a novel effect which we call spurious inverse spin Hall effect (ISHE). It strongly resembles ISHE behaviour, but arises in the ferromagnet rather than the detector meaning this additional effect has to be considered in future work.

cond-mat.mes-hall↗