With machine learning models being increasingly used to aid decision making even in high-
stakes domains, there has been a growing interest in developing interpretable models …

The use of AI-enabled hiring software raises questions about the practice of Human
Resource (HR) professionals' use of the software and its consequences. We interviewed 15 …

Training datasets fundamentally impact the performance of machine learning (ML) systems.
Any biases introduced during training (implicit or explicit) are often reflected in the system's …

Trust has been recognized as a central variable to explain the resistance to using automated
systems (under-trust) and the overreliance on automated systems (over-trust). To achieve …

Large-scale generative models enabled the development of AI-powered code completion
tools to assist programmers in writing code. However, much like other AI-powered tools, AI …

Recently, the automatic prediction of personality traits has received increasing attention and
has emerged as a hot topic within the field of affective computing. In this work, we present a …

In recent years, deep learning-based automated personality traits detection has received a
lot of attention, especially now, due to the massive digital footprints of an individual …

Machine learning (ML) recourse techniques are increasingly used in high-stakes domains,
providing end users with actions to alter ML predictions, but they assume ML developers …

Supervised machine learning utilizes large datasets, often with ground truth labels
annotated by humans. While some data points are easy to classify, others are hard to …

In recent years, deep learning-based automated personality trait detection has received a lot
of attention, especially now, due to the massive digital footprints of an individual. Moreover …