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Research on AI in the Workplace

AI@Work

This update is dedicated to supporting the study of issues related to the multivalent involvement of artificial intelligence (AI) in the workplace. In discussing these issues I share some initial thoughts about one of my two research foci.

The potential for AI to augment and perform human tasks is no longer reserved for spectacular and futuristic demonstrations by IBM’s Watson or Alphabet’s Deepmind, but a present day reality changing the way we work, collaborate, and communicate. Google describes its focus as ‘AI first,’ while Microsoft says AI is the ultimate breakthrough in technology. These claims are backed by heavy investments in AI by Apple, Facebook, and Amazon; together these are the five highest valued companies in the world. Additionally, the Massachusetts Institute of Technology (MIT) is investing $1 billion to set up a college to educate a new generation of AI experts. Needless to say, there is a plethora of examples at individual, organisational, and country levels that testify to the rise of AI in almost every aspect of our daily lives. My aim here is not to provide a comprehensive picture of current AI adoptions in society, or more specifically in organisations. However, those of you who know my work, or me personally, know that I have always been interested in research at the intersection of technology and employee wellbeing. The exponential and impressive rise of artificial intelligence has raised exciting, and vexing, questions about our relationship with technology in the workplace. With this post I hope to instigate a broad range of contributions to facilitate a better understanding of the implications of AI for employee wellbeing.

What is AI?

At this point a brief explanation of my understanding of AI might be of use. I have realised AI is quite sexy, and therefore, a concept that is widely abused and misused to oversell fancy bits of code writing and scripted algorithms as AI. Scholarly definition of AI define these technologies as those dealing with simulation of intelligent behaviour in computers, mimicking the ‘cognitive’ functions associated with the human mind, including problem solving and learning. Put differently, AI can be defined as a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation. Notably, AI is a ‘general purpose technology’ like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities. AI is not one universal technology, rather it is an umbrella term that includes a multitude of different technologies such as machine learning, computer visions, natural language processing, that individually or in combination add intelligence to applications. As a result, there are many different types and levels of artificial intelligence with various ways in which they impact work, for instance depending on the organisational context, sophistication of the AI, the level of autonomy, or decision-making authority it has.

AI and Employee Wellbeing

The current (scientific) debate is dominated by de-contextualized discussions on the types of tasks AI might take over or is highly speculative and controversial in their attempts to label new technological realities. For instance, by making predictions about potential technological (un)employment (e.g., the dystopian perspective: man and machine will wage a Darwinian struggle that machines will win causing mass technological unemployment: versus, the utopian perspective intelligent machines will bring unprecedented wealth and do the work, a universal income will cover basic needs). This discussion distracts from more direct and imminent influences of AI on work and communication processes here and now. Meanwhile many industry leaders and legislative bodies focus on the need for ethical and human-centred AI. The European Commission recently published their guidelines about ethical development and implementation of AI in society. These guidelines propose a few key requirements for trustworthy AI including; I) human agency and oversight (opting for human-in-the-loop, human-on-the-loop, and human-in-command approaches), II) transparency (about the capabilities and limitations of AI), and III) accountability for AI systems and their outcomes (referring to the  auditability and assessment of algorithms and data processing). These guidelines explicitly anchor the importance of human wellbeing by requiring AI systems to benefit human beings including future generations. This is in line with initiatives from Google to give away 25 Million Dollar to ‘humane’ AI projects. Similarly, Microsoft formulated six principles (i.e., socially fair, reliable, safe, inclusive, transparent, and accountable) to ensure AI initiatives benefit individuals and society at large. In addition, Jack Ma CEO and founder of Alibaba, who notoriously claimed workers in his company should be ready to work 80 plus hours per week, recently suggested AI gives us the possibility to head toward a 12-hour work week and employees should focus on the tasks and jobs that make them happy. Finally, universities and scholarly initiatives attempt to emphasise the importance of human(e) AI, as evidenced for instance by the articulation of a new research priority area at the University of Amsterdam.

Although experts and commentators from business and academic spheres often emphasised the importance of the human experience, attention for the impact of AI is almost exclusively focused on the capabilities of the technology and the types of tasks AI is able to perform; overlooking AI’s impact on employee wellbeing. My aim shift our attention to the social implication of AI, by also considering the implications of AI for employee wellbeing.

Below is a list of references that may be of use to those interested in the impact of AI in the workplace. This list is not exhaustive, and given the rapid expansion of the field will soon need to be updated. Recently, my colleague Jeff Treem together with organisational communication scholars compiled a similar list for the study of communication and visibility. If you have any suggestions for material to add to this list, please contact me.

References:

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