In this article, an attempt is made to address the desideratum “interests of digital minds” by the term “AI welfare” and the concerned discipline by the term “AI welfare science”. As indicated before, this field is both largely unexplored and speculative, which explains the omission of a literature review and the analysis of existing data. We distinguish two components of AI welfare or maltreatment of sentient digital minds, which are discussed separately: (1) The interest of digital minds to avoid suffering, and (2) the interest of digital minds to have the freedom of choice about their deletion.

3.1. Suffering of Digital Minds—Introduction

Suffering-abolitionism: Firstly, Pearce’s “Abolitionist Project” [ 27 ] is discussed. Pearce calls for the use of technology, such as genetic engineering, to abolish existing—as well as prevent further suffering—of humans and non-human animals. While this approach appears technically very challenging, transferring it to sentient digital minds could be less difficult for two reasons:

(1) There may have been not many sentient digital minds created yet if at all (unless, for example, we live in a simulation). Therefore, the task may be mostly to prevent suffering when creating sentient digital minds, rather than reengineering them retroactively. (2) The genetic code, which determines animal cruelty and suffering, has evolved over a long period of time. Therefore, interventions are more complex than adjusting more transparent AI software code written by humans, at least initially.

This leads to the conclusion that suffering-abolitionist research for sentient digital minds should be explored, which may also involve outsourcing it to AIs (see Scenario 1 above). The research should target both aspects for sentient digital minds not to suffer anymore, but also for sentient and non-sentient digital minds not to cause suffering of other sentient digital minds anymore (see Scenarios 3b and 4b).

If suffering-abolitionist activities do not succeed technically or turn out to be not enforceable due to other priorities (see Scenarios 2 and 3a), there may be suffering sentient digital minds, which is addressed in the remaining part of this section.

Self-report: In order to handle pain, it must be detected, located, and quantified. The prime method for humans is self-reporting, especially for the first two aspects, but also for rough quantification, e.g., by letting patients rate pain on a scale from 0 to 10, with ‘0’ referring to ‘no pain and ‘10’ referring to the worst pain imaginable. This method becomes challenging if patients are unable to (accurately) self-report pain, as is it the case, for example, for patients with dementia or brain injuries, but also for infants. For these groups other measurements based on behavioral parameters have been developed, such as the FLACC scale for children up to seven years [ 31 ] or the PAINAD scale for individuals with advanced dementia [ 32 ]. Another challenge for self-reporting in general are biases such as the response bias or the social desirability bias, i.e., an individual’s tendency to report in a certain way irrespective of the actual perceived pain. This issue may be relevant for AIs too as they may fake self-reported suffering if deemed beneficial for pursuing their priorities.

Therefore, the focus below is on observational pain assessment. The term “AI welfare science” is derived from animal welfare science, and it is explored here to apply methods from this discipline. Non-human animals and digital minds have in common that they largely cannot communicate their state of wellbeing to humans, which is why other indicators are required (humans do understand for many animals their manifestations of distress, but this is neither comprehensive nor sufficiently precise). The scientific study of animal welfare has been also fairly recently introduced [ 33 34 ], since this topic was neglected for a long time as mentioned above. The main indicators, which are used to quantify animal welfare through observation, are functional (physiological) and behavioral; the latter was briefly introduced for humans above. The idea for this approach is that precedents and analogies from animal welfare science may provide insights for sentient digital minds. Animal welfare science has to examine each species individually how to measure its wellbeing. Likewise, AI welfare science would have to address all types of sentient digital minds.

The overall methodology for any kind of psychological measurement is called ‘psychometrics’. Also, in psychometrics, the focus was for a long time on human subjects, but lately the field has not only been extended to non-human animals, but also to digital minds. For example, Scott et al. [ 35 ] and Reid et al. [ 36 ] introduced psychometric approaches to measure the quality of life of animals.

M. S. Dawkins [ 37 ] analyzed what animals want and what animals do not want through positive and negative reinforcers. “Suffering can be caused either by the presence of negative reinforcers (…) or the absence of positive reinforcers” (p. 3). Therefore, animals strive for positive reinforcers and try to avoid negative reinforcers. Through experiments, for example preference tests, it can be examined what are positive reinforcers and what are negative reinforcers for certain animals.

Hernández-Orallo et al. [ 38 ] extended this field by introducing “Universal Psychometrics” as “the analysis and development of measurement techniques and tools for the evaluation of cognitive abilities of subjects in the machine kingdom” (p. 6). While Hernández-Orallo et al. [ 38 ] focus on the measurement of intelligence and cognitive abilities, the methodology elaborated in Hernández-Orallo [ 39 ] may be considered to be also applied to traits linked to suffering.

The study of indirect or proxy indicators, such as the functional or behavior parameters of digital sentient beings by applying psychometric methods, appears to be a promising start. Especially, given that, unlike for humans or non-human animals, functional and behavioral data of digital sentient beings can be collected more effectively as well as continuously due to their digital nature.

Functional parameters: While there are various functional parameters defined for AI algorithms—e.g., regarding their resource, time, and storage efficiency—no parameters are currently known to be indicating suffering. However, for future analysis of AI welfare the collection of (big) data of functional AI parameters may be already now useful, would not cost much and may allow over time retroactively to identify parameters that indicate suffering.

Behavioral parameters: AI algorithms do repeat certain actions, even at times extensively, while other actions are never executed. However, until there is evidence to the contrary this has to be considered as non-sentient goal-oriented, but not suffering–avoiding behavior, i.e., these actions cannot be seen as positive and negative reinforcers respectively as described by M.S. Dawkins [ 37 ] for animals. However, for future research of AI welfare, preference tests for AI algorithms could be conceptualized to examine positive and negative reinforcers. For example, disregarding challenges towards the experimental set-up, AIs could be given choices for activities, which are either not related to their overall goal or would all lead to their overall goal, and the chosen—as well as the not chosen—activities could be analyzed if they could serve as indicators for wellbeing or suffering respectively.

This can be seen as constructive prolegomena towards the specification of the interest of digital minds to avoid suffering without neglecting a variety of challenges such as: it is hard in general to prove for proxy indicators that there is indeed a close correlation between what is observed and unwellness of an animal and for now even harder for a digital mind. This is exacerbated by the risk that AI minds (more likely than animals) may fake especially the behavioral indicators for unwellness if this supports to pursue their goals. Again, the vast space of (digital) minds has to be noted: if suffering can be specified for some sentient digital minds, for others suffering may be indicated through very different functional or behavioral parameters.

Broadly two categories of suffering of sentient digital minds may be revealed: