The Ethics of Algorithmic Outsourcing in Everyday Life

Abstract

This chapter focuses on one specific application of algorithmic regulation: the use of AI-based personal digital assistants. Rather than being employed as a tool for shaping the behaviour of others, these algorithmic tools are employed by individuals to assist them in their own self-regarding decision-making and goal achievement. The chapter argues that autonomy is under threat in new and interesting ways. It evaluates and disputes the claim that these new threats should not be overestimated because the technology is just an old wolf in a new sheep’s clothing. Finally, it looks at responses to these threats at both the individual and societal level, and argues that although an attitude of ‘helplessness’ should not be encouraged among users of algorithmic tools, there is an important role for legal and regulatory responses to these threats that go beyond what are currently on offer.

The Effects of Closed-Loop Brain Implants on Autonomy and Deliberation: What are the Risks of Being Kept in the Loop?

“…"Technological outsourcing" can be defined as the practice whereby people rely on their computers, smart phones, or other electronic devices to perform certain day-to-day tasks that they would otherwise have to perform themselves. 31 Here, however, it is not the case that patients could otherwise decide for themselves: they rely entirely on the BCI device as their sole source of information to forecast their seizures.…”

Infosphere, Datafication, and Decision-Making Processes in the AI Era

“…Now suppose a situation in which we have a high level of optionality, but an environment in which there is a predominance of heteronomy (freedom in the self-governing sense is not respected). In this situation options would be left open to agents, but the agents would not be free by simply having a set of options open, since the algorithms would be in a position to filter the “choice environment” (Danaher 2019 ).…”

Why do people resist algorithms? From the perspective of short video usage motivations

“…Third, the study only considered the context of recommendation algorithms system in short video platforms—just one kind of algorithmic situation. Another significant algorithmic context is algorithmic decision-making ( Kitchin, 2017 ; Danaher, 2019 ), embedded in daily life as intelligent assistants. Our model suggests that users may decide to resist algorithms.…”

“…Our model suggests that users may decide to resist algorithms. Algorithmic decision-making is more complex than recommendation algorithms, depending more on the context, experience, and autonomy ( Danaher, 2019 ; Dogruel et al, 2020 ). Thus, future studies could expand the model to other situational factors to examine how user experience and autonomy impact algorithmic tactics.…”