Recently, several large tech companies have pushed the notion of AI assistants into the public debate. These envisioned agents are intended to far outshine current systems, as they are intended to be able to manage our affairs as if they are personal assistants. In turn, this ought to give users a leg up, as one prominent tech exec has put it. However, it remains to be seen how these Personal AI Assistants (PAIAs) are implemented, and critical reflection on how and whether they can be implemented in a responsible way is needed. Currently, such agents are undertheorized and this may cause us to misunderstand their value and capacity. In this paper, we explore and critique the potential for responsible implementation by considering some design requirements based on the notion of meaningful human control. If we desire to have control over such assistants, then we need to be able to do so meaningfully and effectively. In looking at the design requirements, we run into the issue that their broad and differing capacities make any kind of design requirements hard because there are simply no standards to which we can measure PAIAs. Furthermore, it seems that the implementation of these assistants will be a matter of trade-offs both in capacities and in values, which will likely lead to enhancement for some rather than an improvement for all.

Under which conditions can you say that a system is actually and meaningfully under your control? Accidents happen with machines and often that is not the fault of the user. So what does control entail? To gain some modicum of understanding, we need to learn how technology and control relate to one another. Of course, the concept of control also relates to the consequences of control, namely the responsibility you have for that which you can control.
On average,we are used to thinking about technology as a kind of hammer, something on hand that we can make use of. Yet, such a hammer also invites us to hammer things. This invitation is a kind of mediation through which we are encouraged to act in one way and not another. In short, technology can also influence us.
If technology can exert a kind of influence on you, then we need to ask what that means for control. What kind of issues do we run into because of that influence? In this dissertation I investigate a few key issues: self reinforcement and relevancy. Technology tends to entrench itself in society once it is widely implemented. This process of entrenchment is often through self-reinforcement (chapter 2). Like a snowball rolling down a hill that picks up more snow as it goes, so too can technology gain a kind of traction that becomes harder and harder to ignore and disband or even change. Consider, for example, how much has to change if we want to live without cars. The moment technology gets picked up at large, we also institute policies and create institutions around which such technology can be legitimized. The point is that the technology can create a new standard to which everyone grows accustomed...

Relevancy is a prevalent term in value alignment. We either need to keep track of the relevant moral reasons, we need to embed the relevant values, or we need to learn from the relevant behaviour. What relevancy entails in particular cases, however, is often ill-defined. The reasons for this are obvious, it is hard to define relevancy in a way that is both general and concrete enough to give direction towards a specific implementation. In this paper, we describe the inherent difficulty that comes along with defining what is relevant to a particular situation. Simply due to design and the way an AI system functions, we need to state or learn particular goals and circumstances under which that goal is completed. However, because of both the changing nature of the world and the varied wielders and users of such implements, misalignment occurs, especially after a longer amount of time. We propose a way to counteract this by putting contestability front and centre throughout the lifecycle of an AI system, as it can provide insight into what is actually relevant at a particular instance. This allows designers to update the applications in such a manner that they can account for oversight during design.

Many technological systems these days interact with their environment with increasingly little human intervention. This situation comes with higher stakes and consequences that society needs to manage. No longer are we dealing with 404 pages: AI systems today may cause serious harm. To address this, we wish to exert a kind of control over these systems, so that they can adhere to our moral beliefs. However, given the plurality of values in our societies, which “oughts” ought these machines to adhere to? In this article, we examine Borda voting as a way to maximize expected choice-worthiness among individuals through different possible “implementations” of ethical principles. We use data from the Moral Machine experiment to illustrate the effectiveness of such a voting system. Although it appears to be effective on average, the maximization of expected choice-worthiness is heavily dependent on the formulation of principles. While Borda voting may be a good way of ensuring outcomes that are preferable to many, the larger problems in maximizing expected choice-worthiness, such as the capacity to formulate credences well, remain notoriously difficult; hence, we argue that such mechanisms should be implemented with caution and that other problems ought to be solved first.