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Mitra Lashkari

dblp:338/9446 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-0153-4229ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 87% Usability and user experience research · 13%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
decision support
0.712023
"Finding the Magic Sauce": Exploring Perspectives of Recruiters and Job Seekers on Recruitment Bias and Automated Tools · CHI 2023

Methods — techniques the papers use, named apart from their topics

interview study · 0.7
YearPublicationVenuePosition
2023 "Finding the Magic Sauce": Exploring Perspectives of Recruiters and Job Seekers on Recruitment Bias and Automated Tools
abstract
Automated recruitment tools are proliferating. While having the promise of improving efficiency, various risks, including bias, challenges the potential of these tools. An in-depth understanding of the perceived risk factors and needs from the perspective of both recruiters and job seekers is needed. We address this through an interview study in the high-tech industry to compare and contrast the concerns of these two roles. We found that the importance of clarifying position requirements and assessing candidates as “whole individuals” are commonly discussed by both recruiters and job seekers. In contrast, while recruiters tended to be more aware of cognitive bias and desired more tool support during interviews, job seekers voiced more desire towards a healthy candidate-company relationship. Additionally, both roles considered the uncertainty of the current technology capability and reduced human contact as concerns for using automated tools. Based on these results, we provided design implications for automated recruitment tools and related decision-support technologies.
Mitra Lashkari, Jinghui Cheng 0001
CHI1