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Paper Type

ERF

Abstract

Artificial intelligence (AI) and machine learning (ML) offer promising technologies in areas such as healthcare, transportation, and finance. Yet, many recent cases show a negative side of AI/ML applications. From sexist hiring tools to racist chat-bots, societal bias is propagating in these applications from the data or the developers and leading to undesirable consequences. Using analogical reasoning theory, this research proposes a model that examines the effect of abilities and skills of the AI/ML application development teams on development processes and AI outcomes. This research hypothesizes that teams’ mean knowledge (in terms of breadth and depth) has a positive effect on quality of AI development process, which can further lead to better performance. It also posits a moderating effect of Bottom line mentality on the hypothesized relationships. This research contributes to systems development literature and introduces a novel measurement for knowledge networks.

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Aug 10th, 12:00 AM

Effects of Abilities of Data Analyst Teams and AI Development

Artificial intelligence (AI) and machine learning (ML) offer promising technologies in areas such as healthcare, transportation, and finance. Yet, many recent cases show a negative side of AI/ML applications. From sexist hiring tools to racist chat-bots, societal bias is propagating in these applications from the data or the developers and leading to undesirable consequences. Using analogical reasoning theory, this research proposes a model that examines the effect of abilities and skills of the AI/ML application development teams on development processes and AI outcomes. This research hypothesizes that teams’ mean knowledge (in terms of breadth and depth) has a positive effect on quality of AI development process, which can further lead to better performance. It also posits a moderating effect of Bottom line mentality on the hypothesized relationships. This research contributes to systems development literature and introduces a novel measurement for knowledge networks.

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