Risk mitigation in algorithmic accountability: The role of machine learning copies

Machine learning plays an increasingly important role in our society and economy and is already having an impact on our daily life in many different ways. From several perspectives, machine learning is seen as the new engine of productivity and economic growth. It can increase the business efficienc...

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Bibliographic Details
Published in:PloS one 2020-11, Vol.15 (11), p.e0241286-e0241286
Main Authors: Unceta, Irene, Nin, Jordi, Pujol, Oriol
Format: Article
Language:English
Subjects:
Accountability
Algorithms
Auditing
Computer and Information Sciences
Computer science
Databases as Topic
Decision making
Economic development
Economic growth
Engineering and Technology
Health risks
Humans
Impact analysis
Learning algorithms
Logistic Models
Machine Learning
Medicine and Health Sciences
Methods
Mitigation
Models, Theoretical
Physical Sciences
Research and Analysis Methods
Risk management
Risk reduction
Risk Reduction Behavior
Social Sciences
Software-as-a-service
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Summary:Machine learning plays an increasingly important role in our society and economy and is already having an impact on our daily life in many different ways. From several perspectives, machine learning is seen as the new engine of productivity and economic growth. It can increase the business efficiency and improve any decision-making process, and of course, spawn the creation of new products and services by using complex machine learning algorithms. In this scenario, the lack of actionable accountability-related guidance is potentially the single most important challenge facing the machine learning community. Machine learning systems are often composed of many parts and ingredients, mixing third party components or software-as-a-service APIs, among others. In this paper we study the role of copies for risk mitigation in such machine learning systems. Formally, a copy can be regarded as an approximated projection operator of a model into a target model hypothesis set. Under the conceptual framework of actionable accountability, we explore the use of copies as a viable alternative in circumstances where models cannot be re-trained, nor enhanced by means of a wrapper. We use a real residential mortgage default dataset as a use case to illustrate the feasibility of this approach.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0241286