Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society

Recent developments in artificial intelligence (AI) have increased interest in combining AI with human intelligence to develop superior systems that augment human and artificial intelligence. In this paper, augmented intelligence informally means computers and humans working together, by design, to enhance one another, such that the intelligence of the resulting system improves. Intelligence augmentation (IA) can pool the joint intelligence of humans and computers to transform individual work, organizations, and society. Notably, applications of IA are beginning to emerge in several domains, such as cybersecurity, privacy, counterterrorism, and healthcare, among others. We provide a brief summary of papers in this special section that represent early attempts to address some of the rapidly emerging research issues. We also present a framework to guide research on IA and advocate for the important implications of IA for the future of work, organizations, and society. We conclude by outlining promising research directions based on this framework for the information systems and related disciplines.

[1]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[2]  Daron Acemoglu,et al.  Automation and New Tasks: How Technology Displaces and Reinstates Labor , 2019, Journal of Economic Perspectives.

[3]  Paul A. Pavlou,et al.  State of the information privacy literature: where are we now and where should we go? , 2011 .

[4]  Vasant Dhar,et al.  Prediction in Economic Networks , 2014, Inf. Syst. Res..

[5]  Thomas W. Malone,et al.  Modeling Coordination in Organizations and Markets , 1987 .

[6]  Paul A. Pavlou,et al.  On the Longitudinal Effects of IT Use on Firm-Level Employment , 2013, Inf. Syst. Res..

[7]  David Autor,et al.  The Growth of Low Skill Service Jobs and the Polarization of the U.S. Labor Market , 2009, SSRN Electronic Journal.

[8]  Dorothy E. Leidner,et al.  Review: A Review of Culture in Information Systems Research: Toward a Theory of Information Technology Culture Conflict , 2006, MIS Q..

[9]  Laura A. Dabbish,et al.  Designing games with a purpose , 2008, CACM.

[10]  Angèle Christin,et al.  Algorithms at Work: The New Contested Terrain of Control , 2020, Academy of Management Annals.

[11]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[12]  Gediminas Adomavicius,et al.  A Machine Learning Approach to Improving Dynamic Decision Making , 2014, Inf. Syst. Res..

[13]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[14]  Dean Pomerleau,et al.  Efficient Training of Artificial Neural Networks for Autonomous Navigation , 1991, Neural Computation.

[15]  G. Krogh.  Artificial Intelligence in Organizations: New Opportunities for Phenomenon-Based Theorizing , 2018, Academy of Management Discoveries.

[16]  Thomas G. Dietterich.  Steps Toward Robust Artificial Intelligence , 2017, AI Mag..

[17]  M A H Dempster,et al.  An automated FX trading system using adaptive reinforcement learning , 2006, Expert Syst. Appl..

[18]  Michael D. Mumford,et al.  UNDERSTANDING WORK USING THE OCCUPATIONAL INFORMATION NETWORK (O*NET): IMPLICATIONS FOR PRACTICE AND RESEARCH , 2001 .

[19]  Hokey Min,et al.  Artificial intelligence in supply chain management: theory and applications , 2010 .

[20]  P. Bain,et al.  ‘An assembly line in the head’: work and employee relations in the call centre , 1999 .

[21]  Allen Newell,et al.  Computer science as empirical inquiry: symbols and search , 1976, CACM.

[22]  Jay R. Galbraith.  Organization Design: An Information Processing View , 1974 .

[23]  R. Banker,et al.  Information Technology Skills and Labor Market Outcomes for Workers , 2013, Inf. Syst. Res..

[24]  Michael A. Osborne,et al.  The future of employment: How susceptible are jobs to computerisation? , 2017 .

[25]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[26]  Jan Muntermann,et al.  Research Note - Heuristic Theorizing: Proactively Generating Design Theories , 2014, Inf. Syst. Res..

[27]  Mark Graham,et al.  Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy , 2018, Work, employment & society : a journal of the British Sociological Association.

[28]  Terry Winograd,et al.  Shifting viewpoints: Artificial intelligence and human-computer interaction , 2006, Artif. Intell..

[29]  Michel Benaroch,et al.  Real Options Models for Proactive Uncertainty-Reducing Mitigations and Applications in Cybersecurity Investment Decision-Making , 2017, Inf. Syst. Res..

[30]  Ron Weber,et al.  On the Ontological Quality and Logical Quality of Conceptual-Modeling Grammars: The Need for a Dual Perspective , 2016, Inf. Syst. Res..

[31]  D. Ariely,et al.  Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially , 2007, SSRN Electronic Journal.

[32]  Andrew B. Whinston,et al.  Collaborative Decision Making: A Connectionist Paradigm for Dialectical Support , 2001, Inf. Syst. Res..