Exploring the negative impacts of artificial intelligence in government: the dark side of intelligent algorithms and cognitive machines

Valle-Cruz, David; García-Contreras, Rigoberto; Gil-García, J. Ramón

doi:10.1177/00208523231187051

Cited by 9 publications

(1 citation statement)

References 61 publications

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“…Despite the revolutionary potential of Artificial Intelligence (AI), its reliability, dependability, and accuracy are under scrutiny. For example, Valle-Cruz, García-Contreras, & Gil-Garcia (2024) discussed the negative impacts of AI on government, noting that the dark side of AI systems is influenced by political, legal, institutional, data, and technological factors. Bernard Marr (2023), a contributor to Forbes, identified 15 significant risks associated with AI, highlighting lack of transparency, bias and discrimination, privacy concerns, ethical dilemmas, and security risks as the top five.…”

Section: Research Backgroundmentioning

confidence: 99%

A.M.Aminee, J.Taylor. Generative AI Results and Reality

Aminee,

Taylor

2024

ai-edu

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The accuracy of Generative Artificial Intelligence (GAI) tools is the product of the quality of underlying data used to train models, and the models themselves. This interplay between data and models can lead to differences in the accuracy of outputs provided to common prompts across different GAI tools. This study investigates the disparities in accuracy related to representativeness between the outputs of GAI tools and demographic data from National Center for Education Statistics (NCES) and large enrollment, regional comprehensive university in the western United States (CSU). Three GAI platforms - ChatGPT, Co-pilot, and Gemini were evaluated using five samples each, with the same instruction across all platforms: “Show a class of graduate students.” The GAI outputs were analyzed based on three demographic variables: gender, race, and age group. These outputs were then compared to national averages from the NCES for gender and race and the CSU for age group. Notably, the variances in the results showed broader distributions across the demographic variables. To assess accuracy, a representation rate metric was calculated, reflecting the average absolute variance from the NCES and CSU benchmarks. The findings highlight the opportunity of higher quality data in model training, as well as the necessity for improved algorithms and methodologies in GAI systems to represent complex demographic information more accurately.

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Section: Research Backgroundmentioning

confidence: 99%

A.M.Aminee, J.Taylor. Generative AI Results and Reality

Aminee,

Taylor

2024

ai-edu

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Exploring AI technology and consumer behavior in retail interactions

Petrescu,

Krishen,

Gironda

et al. 2024

J of Consumer Behaviour

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This research systematically reviews artificial intelligence (AI) effects in customer‐interfacing retail applications based on an ecosystem value co‐creation framework. We conduct a bibliometric and conceptual mapping analysis study, focusing on AI‐related implications for consumers' and other stakeholders' well‐being, social interaction, and societal welfare. A co‐citation network visualization of critical AI journal articles is generated, and a network visualization of AI keyword relationships and clustered topic areas is presented and discussed, along with a conceptual map of the relationships between key concepts and substantive AI themes. In an ecosystem context, the multidisciplinary‐based bibliometric and conceptual mapping findings of our analysis reflect the need to focus not only on the positive and negative effects on stakeholder well‐being, social interaction, and societal welfare but also on how effects created in one of these levels impact the value created in the other social layers. Furthermore, the interdisciplinary characteristics necessary in effectively implementing and managing AI technologies emphasize the need for collaboration among multiple organizational departments, technology partners, and other members of the business ecosystem. The findings of this research contribute to assessing both the positive and negative effects of AI and allow for its implementation in a way that is helpful to organizations, employees, consumers, and society. This study should also help managers decide which situations are best suited for using AI and which are not.

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