We run an online experiment to study the origins of algorithm aversion. Participants are either in the role of workers or of managers. Workers perform three real-effort tasks: task 1, task 2, and the job task which is a combination of tasks 1 and 2. They choose whether the hiring decision...

Implementing new machine learning (ML) algorithms for credit default prediction is associated with better predictive performance; however, it also generates new model risks, particularly concerning the supervisory validation process. Recent industry surveys often mention that uncertainty about how...

Expectation maximization (EM) algorithm is a popular and powerful mathematical method for parameter estimation in case that there exist both observed data and hidden data. The EM process depends on an implicit relationship between observed data and hidden data which is specified by a mapping...

The algorithmic accountability literature to date has primarily focused on procedural tools to govern automated decision-making systems. That prescriptive literature elides a fundamentally empirical question: whether and under what circumstances, if any, is the use of algorithmic systems to make...

This article scrutinises the potential of the existing regulatory apparatus in Union law to tackle the social, technical, and legal challenges inherent in deploying automated systems in high-risk settings such as the workplace, with a view to setting out key lessons for the proposed EU...

This work presents a potent multiobjective multiverse algorithm to solve the highly complicated dynamic economic emission dispatch problem. Solving the dynamic economic dispatch problem with power balance equality constraint, valve point constraints, and ramp limit constraints not only minimizes...

There is growing concern about "algorithmic bias" - that predictive algorithms used in decision-making might bake in or exacerbate discrimination in society. We argue that such concerns are naturally addressed using the tools of welfare economics. This approach overturns prevailing wisdom about...

Algorithms are increasingly used to guide decisions in high-stakes contexts, such as who should receive bail, be approved for a loan, or be hired for a job. Motivated by a growing body of empirical evidence, regulators are concerned about the possibility that the error rates of these algorithms...

The debate about the implications of algorithms on competition law enforcement has so far focused on multi-firm conduct in general and collusion in particular. The implications of algorithms on abuse of dominance have been largely neglected. This article seeks to fill the gap in the existing...