Publications

My research addresses current AI and analytics ethics questions faced by organizations. I use data analytics, machine learning, qualitative interview-based research methodologies to study AI ethics from multiple angles.

 
Open Book

A Code of Conduct for the Ethical Use of Artificial Intelligence in Financial Services
(Stephanie Kelley, Yuri Levin, & David Saunders)

October 2018

A public policy paper written in partnership with several large Canadian banks with principles for the ethical use of artificial intelligence in the Canadian financial services industry.

Anti-discrimination Laws, AI, and Gender Bias in Non-mortgage Fintech Lending
(Stephanie Kelley & Anton Ovchinnikov)

Working Paper (September 2020)

We study the impact of the existing anti-discrimination laws in different countries on gender bias in the non-mortgage consumer fintech lending setting. Building on the study of discrimination in operations, financial economics, and computer science, our paper investigates the impact and drivers of discrimination in machine learning models, trained on the alternative data used by fintech firms, and provide technically and legally permissible approaches for firms to reduce discrimination, whilst managing profitability.

Effective Adoption and Implementation of AI Principles
(Stephanie Kelley)

Working Paper (September 2020)

The study examines employee perceptions on the adoption and implementation of artificial intelligence (AI) principles in organizations. 49 interviews were conducted with employees of 24 organizations, across 11 countries. Participants all worked directly with AI and represented a range of positions from junior data scientist to Chief Analytics Officer. The study found that there are eleven components that could impact the effective adoption and implementation of AI principles in organizations: communication, management support, training, ethics office(r), reporting mechanism, enforcement, measurement, accompanying technical processes, sufficient technical infrastructure, organizational structure, and an interdisciplinary approach. The components are discussed in the context of the research on business code effectiveness. The findings offer a first step in understanding the impact of adoption and implementation on the effectiveness of AI principles in organizations.

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