Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions

• URL: http://arxiv.org/abs/2405.19699v2
• Date: Sun, 2 Jun 2024 22:11:32 GMT
• Title: Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions
• Authors: Dena F. Mujtaba, Nihar R. Mahapatra,
• Abstract summary: Big data and machine learning has led to a rapid transformation in the traditional recruitment process. Given the prevalence of AI-based recruitment, there is growing concern that human biases may carry over to decisions made by these systems. This paper provides a comprehensive overview of this emerging field by discussing the types of biases encountered in AI-driven recruitment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The recruitment process is crucial to an organization's ability to position itself for success, from finding qualified and well-fitting job candidates to impacting its output and culture. Therefore, over the past century, human resources experts and industrial-organizational psychologists have established hiring practices such as attracting candidates with job ads, gauging a candidate's skills with assessments, and using interview questions to assess organizational fit. However, the advent of big data and machine learning has led to a rapid transformation in the traditional recruitment process as many organizations have moved to using artificial intelligence (AI). Given the prevalence of AI-based recruitment, there is growing concern that human biases may carry over to decisions made by these systems, which can amplify the effect through systematic application. Empirical studies have identified prevalent biases in candidate ranking software and chatbot interactions, catalyzing a growing body of research dedicated to AI fairness over the last decade. This paper provides a comprehensive overview of this emerging field by discussing the types of biases encountered in AI-driven recruitment, exploring various fairness metrics and mitigation methods, and examining tools for auditing these systems. We highlight current challenges and outline future directions for developing fair AI recruitment applications, ensuring equitable candidate treatment and enhancing organizational outcomes.

Related papers

• Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
  We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics [46.025337523478825]
  Talent analytics has emerged as a promising field in applied data science for human resource management. Recent development of Big Data and Artificial Intelligence techniques have revolutionized human resource management.
  arXiv  Detail & Related papers  (2023-07-03T07:53:20Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support [18.148737010217953]
  We conduct interviews and workshops with AI practitioners to identify practitioners' processes, challenges, and needs for support. We find that practitioners face challenges when choosing performance metrics, identifying the most relevant direct stakeholders and demographic groups. We identify impacts on fairness work stemming from a lack of engagement with direct stakeholders, business imperatives that prioritize customers over marginalized groups, and the drive to deploy AI systems at scale.
  arXiv  Detail & Related papers  (2021-12-10T17:14:34Z)
• Towards Fairness Certification in Artificial Intelligence [31.920661197618195]
  We propose a first joint effort to define the operational steps needed for AI fairness certification. We will overview the criteria that should be met by an AI system before coming into official service and the conformity assessment procedures useful to monitor its functioning for fair decisions.
  arXiv  Detail & Related papers  (2021-06-04T14:12:12Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
  We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
  arXiv  Detail & Related papers  (2020-04-15T15:58:05Z)
• Bias in Data-driven AI Systems -- An Introductory Survey [37.34717604783343]
  This survey focuses on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features like race, sex, etc.
  arXiv  Detail & Related papers  (2020-01-14T09:39:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.