Browsing by Author "Pauleen D"

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    Cognitive biases in developing biased artificial intelligence recruitment system
    (University of Hawai‘i at Mānoa, 2021-01-01) Soleimani M; Intezari A; Taskin N; Pauleen D; Bui TX
    Artificial Intelligence (AI) in a business context is designed to provide organizations with valuable insight into decision-making and planning. Although AI can help managers make decisions, it may pose unprecedented issues, such as datasets and implicit biases built into algorithms. To assist managers with making unbiased effective decisions, AI needs to be unbiased too. Therefore, it is important to identify biases that may arise in the design and use of AI. One of the areas where AI is increasingly used is the Human Resources recruitment process. This article reports on the preliminary findings of an empirical study answering the question: how do cognitive biases arise in AI? We propose a model to determine people's role in developing AI recruitment systems. Identifying the sources of cognitive biases can provide insight into how to develop unbiased AI. The academic and practical implications of the study are discussed.
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    Introduction to the Judgement, Big Data-Analytics and Decision-making Minitrack
    (University of Hawai‘i at Mānoa, 2021-01-05) Pauleen D; Weerasinghe K; Taskin N; Intezari A; Bui TX
    2021 is the first year that the Judgement, Big Data-Analytics and Decision-making mini-track has been offered. The track's objective is to monitor and advance our knowledge of the convergent technologies of Big Data and analytics and their role in augmenting knowledge for better management decision-making. The track attracted seven submissions of which five were accepted. The papers form a diverse group, offering case studies of big data analytics projects and critical analysis of various factors that impact the successful or unsuccessful use of data/analytics in organizational settings.
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    Risks of e-commerce Recommender Systems: A Scoping Review
    (Australasian Association for Information Systems, 2024-11-25) Kathriarachchi E; Alam S; Weerasinghe K; Pauleen D; Kautz K
    While recommender systems (RS) used in e-commerce have improved significantly providing customers with a personalised shopping experience, scholars have constantly raised concerns over the risks associated with e-commerce RS. However, a lack of methodological synthesis of risk-generating events associated with e-commerce recommender systems has curtailed systematic investigation of the risks of e-commerce RS. This paper presents a scoping review aimed at addressing this gap by synthesising different risk-generating events involved with the use of e-commerce RS as reported in the literature that could affect the welfare of customers who use those systems. Accordingly, peer-reviewed research studies published from 2003-2023 were extracted from the SCOPUS database and EBSCOhost platform for review. Sixty-two publications with evidence on risk-generating events of e-commerce RS were considered for the review. Twenty risk-generating events were identified through the review. These events were mapped with the corresponding risks based on existing frameworks on risks of e-commerce. We were able to identify several risk-generating events that had not previously been considered in conceptualising the risks of e-commerce RS. Further, we identified the plurality of the outcomes of risk-generating events which could provide guidance for the evaluation of e-commerce recommender systems from a multistakeholder perspective.