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Short abstract:

This contribution addresses user-centric contestability of AI-based job interviewing, an increasingly pervasive and often-criticized technology. The focus lies on how contestation can be facilitated for end users (both recruiters and job applicants) through a contestability by design approach.

Long abstract:

Artificial intelligence (AI) supported job interviewing presents itself as a new mainstream solution in the human resources (HR) industry. But the technology has been publicly criticized for a lack of accuracy and potentially producing biased results [1]. In light of such scrutiny and informed by work in two related research projects, this contribution addresses the question of how end users can be enabled to challenge the technology through a contestability by design approach [2].

Critically evaluating and, if necessary, contesting AI mechanisms, assumptions and predictions must be considered a socio-technical challenge involving heterogeneous actors, data, technologies, and infrastructures. Focusing in on users, AI interviewing tools have two types of end users with different needs, expectations, and concerns: 1) Job applicants whose data are analyzed and 2) HR professionals basing further decisions on the analyzed data. Both should be able to make informed choices, communicate issues and challenge outcomes regarding their interactions with an AI-based interviewing system, without this negatively impacting their careers or career prospects. To facilitate this, contestability must be built into AI systems already during their design and development. This contribution thus analyzes key considerations, touching on other concepts such as accountability and transparency from an AI ethics perspective, in enabling end users to reflect and meaningfully intervene in the context of AI interviewing systems.

[1]Wall, Shellmann. 2021. We tested AI interview tools. Here’s what we found. MIT Technology Review. https://www.technologyreview.com/2021/07/07/1027916/we-tested-ai-interview-tools/

[2]Alfrink et al. 2022. Contestable AI by design: Towards a framework. Minds & Machines. https://doi.org/10.1007/s11023-022-09611-z