Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges

Abstract

The current expansion of theory and research on artificial intelligence in management and organization studies has revitalized the theory and research on decision-making in organizations. In particular, recent advances in deep learning (DL) algorithms promise benefits for decision-making within organizations, such as assisting employees with information processing, thereby augment their analytical capabilities and perhaps help their transition to more creative work. We conceptualize the decision-making process in organizations augmented with DL algorithm outcomes (such as predictions or robust patterns from unstructured data) as deep learning-augmented decision-making (DLADM). We contribute to the understanding and application of DL for decisionmaking in organizations by (a) providing an accessible tutorial on DL algorithms and (b) illustrating DLADM with two case studies drawing on image recognition and sentiment analysis tasks performed on datasets from Zalando, a European e-commerce firm, and Rotten Tomatoes, a review aggregation website for movies, respectively. Finally, promises and challenges of DLADM as well as recommendations for managers in attending to these challenges are also discussed.

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

“…Future work should focus on what kind of challenges and opportunities are involved in augmented intelligence or automated decision-making from an organizational perspective. [1,2,3] Second, AI development and adoption requires interdisciplinary and cross-border collaboration among analytics experts, software developers, business and users. Given that AI allows for the implementation of more sophisticated business analytics, we call for research on the role of trust in the development and implementation of the algorithmic cycle in organizations, e.g., functions, openness of coding, data collection and implementation of new services.…”

“…Thus, we identify need to theoretically and empirically advance our understanding of trust and AI. In this mini-track, we focus on addressing the cross-section of the two topic areas (1) in Online Loan Applications'. This paper explores how conversational interaction and the usage of anthropomorphic design elements of AI chatbots influence user trust beliefs by conducting an online experiment.…”

Survey of Deep-learning Techniques in Big-Data analytics

“…Advantages and Disadvantages 1 [45] The decision-making process is determined and in uenced by the Bigdata-analytics factors. Hence in owing to this concept, the organizational decision-making methods ampli ed with Deep-learning algorithms results is conceptualized.…”

Machine learning and deep learning

“…Similarly, instead of codifying knowledge into computers, machine learning (ML) seeks to automatically learn meaningful relationships and patterns from examples and observations (Bishop 2006). Advances in ML have enabled the recent rise of intelligent systems with human-like cognitive capacity that penetrate our business and personal life and shape the networked interactions on electronic markets in every conceivable way, with companies augmenting decisionmaking for productivity, engagement, and employee retention (Shrestha et al 2021), trainable assistant systems adapting to individual user preferences (Fischer et al 2020), and trading agents shaking traditional finance trading markets (Jayanth Balaji et al 2018).…”