English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The recent advancements in machine learning (ML) have demonstrated thepotential for providing a powerful solution to build complex prediction systemsin a short time. However, in highly regulated industries, such as the financialtechnology (Fintech), people have raised concerns about the risk of ML systemsdiscriminating against specific protected groups or individuals. To addressthese concerns, researchers have introduced various mathematical fairnessmetrics and bias mitigation algorithms. This paper discusses hidden technicaldebts and challenges of building fair ML systems in a production environmentfor Fintech. We explore various stages that require attention for fairness inthe ML system development and deployment life cycle. To identify hiddentechnical debts that exist in building fair ML system for Fintech, we focus onkey pipeline stages including data preparation, model development, systemmonitoring and integration in production. Our analysis shows that enforcingfairness for production-ready ML systems in Fintech requires specificengineering commitments at different stages of ML system life cycle. We alsopropose several initial starting points to mitigate these technical debts fordeploying fair ML systems in production.

Hidden Technical Debts for Fair Machine Learning in Financial Services