Ethics of AI-enabled recruiting and selection: A review and research agenda
AL Hunkenschroer, C Luetge - Journal of Business Ethics, 2022 - Springer
Companies increasingly deploy artificial intelligence (AI) technologies in their personnel
recruiting and selection process to streamline it, making it faster and more efficient. AI …
recruiting and selection process to streamline it, making it faster and more efficient. AI …
A survey of algorithmic recourse: contrastive explanations and consequential recommendations
Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods
TP Pagano, RB Loureiro, FVN Lisboa… - Big data and cognitive …, 2023 - mdpi.com
One of the difficulties of artificial intelligence is to ensure that model decisions are fair and
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Machine learning is increasingly used to inform decision-making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …
where decisions have consequential effects on individuals' lives. In these settings, in …
Algorithmic hiring in practice: Recruiter and HR Professional's perspectives on AI use in hiring
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 …
Resource (HR) professionals' use of the software and its consequences. We interviewed 15 …
Fairness via explanation quality: Evaluating disparities in the quality of post hoc explanations
As post hoc explanation methods are increasingly being leveraged to explain complex
models in high-stakes settings, it becomes critical to ensure that the quality of the resulting …
models in high-stakes settings, it becomes critical to ensure that the quality of the resulting …
Fairness through robustness: Investigating robustness disparity in deep learning
Deep neural networks (DNNs) are increasingly used in real-world applications (eg facial
recognition). This has resulted in concerns about the fairness of decisions made by these …
recognition). This has resulted in concerns about the fairness of decisions made by these …
Human-centric multimodal machine learning: Recent advances and testbed on AI-based recruitment
The presence of decision-making algorithms in society is rapidly increasing nowadays,
while concerns about their transparency and the possibility of these algorithms becoming …
while concerns about their transparency and the possibility of these algorithms becoming …
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview
The increasing digitization and datification of all aspects of people's daily life, and the
consequent growth in the use of personal data, are increasingly challenging the current …
consequent growth in the use of personal data, are increasingly challenging the current …
Fairness in matching under uncertainty
The prevalence and importance of algorithmic two-sided marketplaces has drawn attention
to the issue of fairness in such settings. Algorithmic decisions are used in assigning students …
to the issue of fairness in such settings. Algorithmic decisions are used in assigning students …