Designing Fair AI in Human Resource Management: Understanding Tensions Surrounding Algorithmic Evaluation and Envisioning Stakeholder-Centered Solutions

Park, Hyanghee; Ahn, Daehwan; Hosanagar, Kartik; Lee, Joonhwan

doi:10.1145/3491102.3517672

Cited by 32 publications

(7 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These systems not only foster a sense of individual attention and care but also promote a culture of continuous learning and improvement (Sari et al, 2020) [8] . [6,7] .…”

Section: Application In Employee Engagementmentioning

confidence: 99%

Untitled

2024

Int. J. Appl. Res.

View full text Add to dashboard Cite

This research paper delves into the transformative impact of Artificial Intelligence (AI) on Human Resource Management (HRM), specifically focusing on performance evaluation and employee engagement. The primary objective of this study is to explore how AI technologies, such as machine learning and natural language processing, can enhance the accuracy and fairness of performance evaluations, and how they can be leveraged to improve employee engagement within organizations. Through a systematic literature review, this study synthesizes findings from recent research papers and case studies to assess the effectiveness of AI tools in HRM practices. The methodology employed involves a comprehensive analysis of peer-reviewed articles, conference proceedings, and empirical studies published between 2015 and 2023. Key findings indicate that AI significantly contributes to performance evaluation by providing data-driven insights that ensure fairness and objectivity. Furthermore, AI-driven tools are found to be instrumental in enhancing employee engagement by facilitating real-time feedback and personalized engagement strategies. However, the integration of AI in HRM also presents challenges, including concerns related to privacy, bias, and the need for human oversight. The paper concludes with practical recommendations for HR professionals aiming to implement AI in HRM practices, emphasizing the importance of ethical considerations and the human element in technology adoption. This study contributes to HRM theory by highlighting the role of technology in evolving HR practices and offers insights into the future of work in the AI era.

show abstract

“…These systems not only foster a sense of individual attention and care but also promote a culture of continuous learning and improvement (Sari et al, 2020) [8] . [6,7] .…”

Section: Application In Employee Engagementmentioning

confidence: 99%

Untitled

2024

Int. J. Appl. Res.

View full text Add to dashboard Cite

show abstract

“…Researchers have conducted interviews or workshops [14,72] and co-design or participatory design sessions [60,87] with impacted stakeholders and shown how this can generate AI design ideas based on stakeholders' own experiences and situations. Researchers have also worked specifically with gig workers.…”

Section: Stakeholder Involvement In Ai Designmentioning

confidence: 99%

Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes

Zhang

Boltz

Lynn

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of gig worker-centered tools as informed by their driving patterns, decisions, and personal contexts. Using workers' own data as well as city-level data, we create probes-interactive data visuals-that participants explore to surface the well-being and positionalities that shape their work strategies. We describe participant insights and corresponding AI design considerations surfaced from data probes about: 1) workers' well-being trade-offs and positionality constraints, 2) factors that impact well-being beyond those in the data probes, and 3) instances of unfair algorithmic management. We discuss the implications for designing data probes and using them to elevate worker-centered AI design as well as for worker advocacy. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI).

show abstract

“…Abdeldayem and Aldulaimi (2020) discussed that the application of AI enables the formation of a digitally transformed work environment that will be helpful in dealing with an inconsistent environment. Park et al (2022) identifying how artificial intelligence adoption created employee integration strategies and focused on developing core skills among employees to develop an efficient knowledge-based view. Thus, based on the stated discussion of the literature, it is clearly evident that artificial intelligence adoption led to the development of employee-centric measures, and it provides a valuable addition to exploring the resources to the maximum way and enhancing the competencies of employees to deal with the crisis.…”

Section: Emerging Research Streams In Hrr and Aimentioning

confidence: 99%

Artificial intelligence as an enabler for achieving human resource resiliency: past literature, present debate and future research directions

Panda,

Dash,

Samadhiya

et al. 2023

IJIEOM

View full text Add to dashboard Cite

PurposeArtificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.Design/methodology/approachThe present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.FindingsThe authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.Research limitations/implicationsThe present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.Originality/valueThe present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

show abstract

Designing Fair AI in Human Resource Management: Understanding Tensions Surrounding Algorithmic Evaluation and Envisioning Stakeholder-Centered Solutions

Cited by 32 publications

References 59 publications

Untitled

Untitled

Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes

Artificial intelligence as an enabler for achieving human resource resiliency: past literature, present debate and future research directions

Contact Info

Product

Resources

About