Three Population Covariate Shift for Mobile Phone-based Credit Scoring

Speakman, Skyler; Sridharan, Srihari; Markus, Isaac

doi:10.1145/3209811.3209856

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…This is the case in certain domains where there is a scarcity of data (low-frequency transactions). These conditions require the expansion of existing datasets to the level required by some ML algorithms, which we applied to use cases in financial services (e.g., credit scoring and credit-limit management [3]). Data generation based on small datasets can become a powerful tool in the data scientists toolbox when considering these circumstances, and when working on network-based ML algorithms like Federated Learning where bespoke Deep Learning (DL) model structures need to be defined and optimized beforehand and later optimized as more real-world data is collected.…”

Section: Motivationmentioning

confidence: 99%

Evaluation of Bias in Sensitive Personal Information Used to Train Financial Models

Bryant¹,

Cintas²,

Wambugu³

et al. 2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Bias in data can have unintended consequences which propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can result in discrimination from certain financial instruments and services. At the same time, data privacy is of paramount importance, and recent data breaches have seen reputational damage for large institutions. Presented in this paper is a trusted model-lifecycle management platform that attempts to ensure consumer data protection, anonymization, and fairness. Specifically, we examine how datasets can be reproduced using deep learning techniques to effectively retain important statistical features in datasets whilst simultaneously protecting data privacy and enabling safe and secure sharing of sensitive personal information beyond the current state-of-practice.

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Section: Motivationmentioning

confidence: 99%

Evaluation of Bias in Sensitive Personal Information Used to Train Financial Models

Bryant¹,

Cintas²,

Wambugu³

et al. 2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

View full text Add to dashboard Cite

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Algorithmic fairness datasets: the story so far

Fabris

Messina

Silvello

et al. 2022

Data Min Knowl Disc

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Data-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a result, a growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair machine learning and equitable algorithm design hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which we compile in-depth documentation. This unifying documentation effort supports multiple contributions. Firstly, we summarize the merits and limitations of Adult, COMPAS, and German Credit, adding to and unifying recent scholarship, calling into question their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness practitioners and researchers, including their format, cardinality, and the sensitive attributes they encode. We summarize this information, zooming in on the tasks, domains, and roles of these resources. Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.

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