The Evaluation of the Black Box Problem for AI-Based Recommendations: An Interview-Based Study

Ochmann, Jessica; Zilker, Sandra; Laumer, Sven

doi:10.1007/978-3-030-86797-3_16

Cited by 7 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking into account costs and implementation issues of ADSS solutions, using or not using these systems in an organization is a strategic decision made by top management, and recruiters must abide by this decision. In Ochmann et al’s (2021) study, a mandatory context strengthened aversion for ADSSs in users’ discourses more than a voluntary context. However, Ochmann et al did not measure actual behaviors but attitudes, which leaves an important avenue of research on the link between attitude and behavior in predictive hiring.…”

Section: Discussionmentioning

confidence: 79%

“…This “black box problem” is an emerging issue in HR management. For example, attitudes of users confronted with predictable but opaque and inexplicable ADSSs seem to increase algorithmic aversion, according to an interview-based study ( Ochmann et al, 2021 ). In our sample, only 5% of recruiters reported using ADSSs regularly, and 63.1% had never used one.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Should I Trust the Artificial Intelligence to Recruit? Recruiters’ Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening

Lacroux

Martin‐Lacroux²

2022

Front. Psychol.

View full text Add to dashboard Cite

Resume screening assisted by decision support systems that incorporate artificial intelligence is currently undergoing a strong development in many organizations, raising technical, managerial, legal, and ethical issues. The purpose of the present paper is to better understand the reactions of recruiters when they are offered algorithm-based recommendations during resume screening. Two polarized attitudes have been identified in the literature on users’ reactions to algorithm-based recommendations: algorithm aversion, which reflects a general distrust and preference for human recommendations; and automation bias, which corresponds to an overconfidence in the decisions or recommendations made by algorithmic decision support systems (ADSS). Drawing on results obtained in the field of automated decision support areas, we make the general hypothesis that recruiters trust human experts more than ADSS, because they distrust algorithms for subjective decisions such as recruitment. An experiment on resume screening was conducted on a sample of professionals (N = 694) involved in the screening of job applications. They were asked to study a job offer, then evaluate two fictitious resumes in a 2 × 2 factorial design with manipulation of the type of recommendation (no recommendation/algorithmic recommendation/human expert recommendation) and of the consistency of the recommendations (consistent vs. inconsistent recommendation). Our results support the general hypothesis of preference for human recommendations: recruiters exhibit a higher level of trust toward human expert recommendations compared with algorithmic recommendations. However, we also found that recommendation’s consistence has a differential and unexpected impact on decisions: in the presence of an inconsistent algorithmic recommendation, recruiters favored the unsuitable over the suitable resume. Our results also show that specific personality traits (extraversion, neuroticism, and self-confidence) are associated with a differential use of algorithmic recommendations. Implications for research and HR policies are finally discussed.

show abstract

Section: Discussionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Should I Trust the Artificial Intelligence to Recruit? Recruiters’ Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening

Lacroux

Martin‐Lacroux²

2022

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…Beyond team attitudes toward AI, a major consideration in human–AI team collaboration is whether AI is introduced as a mandatory part of the work process or something a team elects to use. Research spanning traditional technologies (e.g., Lin & Shao, 2000; Tsai et al, 2017) and AI implementations (e.g., Ochmann et al, 2021) points to the importance of discretion in implementation outcomes. Although many other factors are likely at play (e.g., top management support and culture), we first look at how information systems (ISs) literature considers voluntariness (Hartwick & Barki, 1994; Jasperson et al, 2005) and discretion (G. S. Howard & Mendelow, 1991).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on studies in other realms (e.g., training) and meta-analysis regarding technology adoption (Wu & Lederer, 2009), we see that discretion is a significant factor. Considering the role of discretion is a crucial piece of how team members might, or might not, collaborate with AI (Ochmann et al, 2021).…”

Section: Ai Discretion: Voluntary or Mandatory?mentioning

confidence: 99%

“…Whether organizations mandate AI use or offer discretion may be constrained by technical design. Mandatory use of AI requires teams to use specific technology, denying the option to perform the task without this technology (Brown et al, 2002;Ochmann et al, 2021;Venkatesh & Davis, 2000). On the one hand, mandating AI may signal an organization's commitment and support for AI from top management (Alsheibani et al, 2018;Jöhnk et al, 2021).…”

Section: Ai Discretion: Voluntary or Mandatory?mentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

Bezrukova

Spell

Rice

et al. 2023

Group & Organization Management

View full text Add to dashboard Cite

We theorize about human-team collaboration with AI by drawing upon research in groups and teams, social psychology, information systems, engineering, and beyond. Based on our review, we focus on two main issues in the teams and AI arena. The first is whether the team generally views AI positively or negatively. The second is whether the decision to use AI is left up to the team members (voluntary use of AI) or mandated by top management or other policy-setters in the organization. These two aspects guide our creation of a team-level conceptual framework modeling how AI introduced as a mandated addition to the team can have asymmetric effects on collaboration level depending on the team’s attitudes about AI. When AI is viewed positively by the team, the effect of mandatory use suppresses collaboration in the team. But when a team has negative attitudes toward AI, mandatory use elevates team collaboration. Our model emphasizes the need for managing team attitudes and discretion strategies and promoting new research directions regarding AI’s implications for teamwork.

show abstract

If You Go for AI, Be Aware of the Psychological Hurdles Around It—Practical and Theoretical Insights on the Industrial Application of Artificial Intelligence

Demlehner

Schoemer

Laumer

2021

Digital Business Models in Industrial Ecosystems

View full text Add to dashboard Cite

The Evaluation of the Black Box Problem for AI-Based Recommendations: An Interview-Based Study

Cited by 7 publications

References 34 publications

Should I Trust the Artificial Intelligence to Recruit? Recruiters’ Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening

Should I Trust the Artificial Intelligence to Recruit? Recruiters’ Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening

Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

If You Go for AI, Be Aware of the Psychological Hurdles Around It—Practical and Theoretical Insights on the Industrial Application of Artificial Intelligence

Contact Info

Product

Resources

About