This chapter analyzes the evolution of the new ways of working, especially in terms of algorithms and machine learning. Special attention is given to algorithmic management and its ethical concerns, as well as to practical examples of the application of algorithms in different sectors. Faculty discussions about how to best prepare students to deal with human-machine interactions at work are presented, with algorithmic management and accountability the discussion's central axis. In algorithmic management, there are distinct positions to analyze; one that favors innovation and efficiency and privileges dignified work and ethics. A brief proposal on introducing algorithmic ethics into the programs offered at a private business school in Mexico is included.
TopIntroduction
Advances in technology, especially in Artificial Intelligence (AI), have been exponential. Digital innovation touches every aspect of life, both personal and social, affecting how we understand the world and ourselves. Digital innovation and AI are increasingly present in human activity and decision-making and are altering the way we think and act. A study by PwC has even suggested that human-machine interactions will soon become as common as watercooler conversations between colleagues today (Donkor et al., 2017).
AI is driving the adoption of technology at an unprecedented rate (Oracle, 2019). The application of AI in the workplace is being led by India, China, the United Arab Emirates, Brazil, Australia, New Zealand, Singapore, the United States, the United Kingdom, France, and Japan (Oracle, 2019). Organizations are increasingly using AI to manage their workforces through algorithmic technologies that rely on large datasets, making it possible for machines to become bosses (van Hooijdonk, 2019).
In this regard, employers are ultimately responsible for their employees' wellbeing, and the underlying issue is whether or not algorithms will be able to treat people fairly. AI and algorithms offer many opportunities to design more flexible, fulfilling ways to work, but they need to be developed and managed ethically and effectively (Walsh, 2019), and it is crucial to understand how this technology makes decisions (Heilweil, 2020).
Numerous questions require a prompt response, as human-machine interactions will continue to increase, especially those regarding employees' ethical responsibility following orders and decisions produced by an algorithm or a robot. Others include how socialization at work is going to be affected, the essential changes we will see in terms of requirements of educational programs, and more specifically, how to ensure that students will consider AI as a support to human decision-making and action, instead of promoting a lack of accountability and the loss of free will.
There are no easy answers, and machine learning in itself is not unethical. Human identity and dignity will be impacted based on how algorithms are developed, and it is the interaction between machines and humans that needs to be addressed.
The field study is centered on a private higher education institution in central Mexico. General conditions regarding stress and working hours in the country are described to create the general background of the study, along with two significant regulations that legislate psychosocial risks and remote work. The population considered were full-time faculty members who had moved their activities online. A section of a scale that is part of a more extensive study about the effects of remote work during the pandemic was used. Results were analyzed as a whole and later on divided according to gender to determine whether there were significant differences in terms of burnout syndrome in faculty.