Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GlobalSIP45357.2019.8969527 | IEEE Global Conference on Signal and Information Processing |
Keywords | Field | DocType |
bias,trust,machine learning models,financial services,data skewness | Financial modeling,Internet privacy,Software deployment,Computer science,Financial services,Financial instrument,Personally identifiable information,Data breach,Information privacy,Data Protection Act 1998 | Conference |
ISSN | Citations | PageRank |
2376-4066 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reginald E. Bryant | 1 | 13 | 3.72 |
| Celia Cintas | 2 | 6 | 3.15 |
| Isaac Wambugu | 3 | 0 | 0.34 |
| Andrew Kinai | 4 | 13 | 4.36 |
| Abdigani Diriye | 5 | 0 | 0.34 |
| Komminist Weldemariam | 6 | 154 | 27.27 |