Understanding the Effect that Task Complexity has on Automation Potential and Opacity: Implications for Algorithmic Fairness

Scholars have increasingly focused on understanding different aspects of algorithms since they not only affect individual choices and decisions but also influence and shape societal structures. We can broadly categorize scholarly work on algorithms along the dimensions of economic gain that one achieves through automation and the ethical concerns that stem from such automation. However, the literature largely uses the notion of algorithms in a generic way and overlooks different algorithms’ specificity and the type of tasks that they perform. Drawing on a typology of tasks based on task complexity, we suggest that variations in the complexity of tasks contribute to differences in 1) their automation potential and 2) the opacity that results from their automation. We also suggest a framework to assess the likelihood that fairness concerns will emanate from automation of tasks with varying complexity. In this framework, we also recommend affordances for addressing fairness concerns that one may design into systems that automate different types of tasks

A task framework for predicting the effects of automation

The ongoing digitalization changes the nature of work. Nowadays, even complex tasks can be automated and reliably performed by machines. This new wave of automation has led to an increased interest in predicting the effects of automation on job design. A recent study suggests that around half of today’s jobs could disappear in the coming twenty years. However, these results are heavily debated. Other studies claim that the effect of automation will be much less dramatic. A fundamental issue underlying all these studies is the question of how to categorize tasks. Some authors simply divide tasks into routine and non-routine tasks, others also consider which kind of cognitive abilities are required. Since the predicted effect of automation directly relates to the categories considered, a sound task framework is essential for useful predictions. Recognizing that existing task models are limited in terms of granularity and time, we use a literature study, interviews, and an analysis of historical data to systemically develop a new task framework for predicting the effects of automation. We conduct an evaluation of our framework to demonstrate the generalizability of the framework and compare the framework with existing models

A task framework for predicting the effects of automation

The ongoing digitalization changes the nature of work. Nowadays, even complex tasks can be automated and reliably performed by machines. This new wave of automation has led to an increased interest in predicting the effects of automation on job design. A recent study suggests that around half of today's jobs could disappear in the coming twenty years. However, these results are heavily debated. Other studies claim that the effect of automation will be much less dramatic. A fundamental issue underlying all these studies is the question of how to categorize tasks. Some authors simply divide tasks into routine and non-routine tasks, others also consider which kind of cognitive abilities are required. since the predicted effect of automation directly relates to the categories considered, a sound task framework is essential for useful predictions. Recognizing that existing task models are limited in terms of granularity and time, we use a literature study, interviews, and an analysis of historical data to systemically develop a new task framework for predicting the effects of automation. We conduct an evaluation of our framework to demonstrate the generalizability of the framework and compare the framework with existing models