Substituting Human Decision-Making with Machine Learning: Implications for Organizational Learning

Balasubramanian, Niranjan; Ye, Yang; Xu, Mingtao

doi:10.5465/amr.2019.0470

Cited by 114 publications

(56 citation statements)

References 45 publications

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“…Despite concentrating on AI’s benefits, we acknowledge potential risks in assisting and even substituting human decision‐making and innovative idea generation. Risks include the myopia of organizational learning (e.g., overlooking long term goal, organizational interdependencies and lack of ability to predict extreme outcomes; see Balasubramanian et al, 2020) and ethical issues (Etzioni & Etzioni, 2017). Future research cannot shy away from this dark side of AI.…”

Section: Discussionmentioning

confidence: 99%

Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence

Garbuio

Lin

2021

J of Product Innov Manag

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This article addresses gaps about abductive reasoning—widely considered key to design‐thinking but rarely detailed in design‐thinking and innovation literatures—by examining two types of abduction; identifying impediments to it; and proposing the promise of Artificial Intelligence (AI) to mitigate those impediments. Contrasting with the deductive and inductive approaches that dominant problem‐solving, we distinguish and elucidate explanatory abduction and innovative abduction in problem finding, where the problem to solve is itself uncertain. We argue these are appropriate for generating innovative problem‐finding ideas. Focusing thenceforth on problem finding alone, the heart of the article proposes a comprehensive conceptual model of innovative idea generation in that more ambiguous, complex, under‐researched but exciting problem space. The model details three chief stages: (1) problem search frame, combining leadership’s vision and innovators’ knowledge; (2) generating abductive hypotheses from often‐surprising observations and their synthesis into insights; and (3) evaluating abductive hypotheses, against the novel quality criteria of plausibility and relevance. Among cognitive impediments we show how the downsides of mental model, limited cognitive load and exemplifying heuristics and cognitive biases, such as confirmation bias, can hinder each stage. Conversely, we examine how support from AI can help human innovators improve the quantity, speed, and quality of their innovative idea generation.

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Section: Discussionmentioning

confidence: 99%

Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence

Garbuio

Lin

2021

J of Product Innov Manag

View full text Add to dashboard Cite

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“…Fourth, when AI serves as a team member [5] [6], the role of emotional trust is increasingly important [4] [7]. Balasubramanian and colleagues [9] note that AI algorithms cannot easily process emotions as such algorithms are designed with formal rationality in mind. We recommend that IS researchers study different human trust dimensions ( i.e.…”

Section: Discussion Of Research Advancesmentioning

confidence: 99%

Introduction to the Minitrack on Advances in Trust Research: Artificial Intelligence in Organizations

Blomqvist

Möhlmann

Järvenpää

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Within the Conference on System Sciences (HICSS), we organize for the fifth time a mini track on 'Advances in Trust Research', this year addressing 'Artificial Intelligence in Organizations'. Through the presentation of four papers and an open discussion, the mini-track participants will debate trust and AI in contexts such as AI-mediated work relations, anthropomorphism of AI chatbot, and AI enabled CRM in e-commerce.

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“…Algorithms are often created and implemented with specific goals predesigned by powerful actors (Kellogg et al, 2020), which may exacerbate agency problems and power imbalances in organizations. As emphasized in the preceding theme relating to bias and opacity in AI, research in this theme also shows that biases are amplified when ML algorithms train on real-world data potentially contaminated with implicit human bias (Balasubramanian et al, 2020;Choudhury et al, 2020). Moreover, organizations that adopt ML to substitute human decision-making may suffer from sub-optimal results and learning myopia if they rely solely on algorithmic decisions (Balasubramanian et al, 2020;Blohm et al, 2020; D. T. Newman et al, 2020).…”

Section: Theme: Organizational Impact Of Ai Adoptionmentioning

confidence: 94%

An Eye for Artificial Intelligence: Insights Into the Governance of Artificial Intelligence and Vision for Future Research

Chhillar

Aguilera

2022

Business & Society

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In this 60th anniversary of Business & Society essay, we seek to make three main contributions at the intersection of governance and artificial intelligence (AI). First, we aim to illuminate some of the deeper social, legal, organizational, and democratic challenges of rising AI adoption and resulting algorithmic power by reviewing AI research through a governance lens. Second, we propose an AI governance framework that aims to better assess AI challenges as well as how different governance modalities (architecture, laws, norms, and market) can support AI. At the heart of our framework lies the governance forces that apply to institutions, organizations, and individuals, who ultimately provide, regulate, and use AI decision-making. We discuss how businesses may harness AI’s economic power through governance solutions without creating or amplifying societal biases and inequalities. Third, as part of our section on future research, we identify a set of governance trade-offs in AI adoption, suggest future research avenues to conceptually strengthen research on the governance of AI, and lay out key policy recommendations.

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Substituting Human Decision-Making with Machine Learning: Implications for Organizational Learning

Cited by 114 publications

References 45 publications

Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence

Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence

Introduction to the Minitrack on Advances in Trust Research: Artificial Intelligence in Organizations

An Eye for Artificial Intelligence: Insights Into the Governance of Artificial Intelligence and Vision for Future Research

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