Edited by Jacy Reese Anthis and Ali Ladak. Many thanks to Thomas Moynihan, Tobias Baumann, and Teo Ajantaival for reviewing and providing feedback. This article is also available and referenceable on the Open Science Framework: https://doi.org/10.31234/osf.io/sujwf
We consider the terminology used to describe artificial entities and how this terminology may affect the moral consideration of artificial entities. Different combinations of terms variously emphasize the entity's role, material features, psychological features, and different research perspectives. The ideal term may vary across context, but we favor “artificial sentience” in general, in part because “artificial” is more common in relevant contexts than its near-synonyms, such as “synthetic” and “digital,” and to emphasize the sentient artificial entities who deserve moral consideration. The terms used to define and refer to these entities often take a human perspective by focusing on the benefits and drawbacks to humans. Evaluating the benefits and drawbacks of the terminology to the moral consideration of artificial entities may help to clarify emerging research, improve its impact, and align the interests of sentient artificial entities with the study of artificial intelligence (AI), especially research on AI ethics.
The importance of conceptual clarity
Terminology and conceptual definitions
Table 1: Terminology defining an entity’s role
Table 2: Terminology defining material features
Table 3: Terminology defining psychological features
Table 4: Consequential combinations of features and role
Reasons for “artificial sentience”
Reasons against “artificial sentience”
Appendix: Terminology defining relevant fields of study
Sentience Institute uses the term “artificial sentience” to describe artificial entities with the capacity for positive and negative experiences. When we survey the public, we use the terms “artificial beings” and “robots/AIs” to refer to “intelligent entities built by humans, such as robots, virtual copies of human brains, or computer programs that solve problems, with or without a physical body, that may exist now or in the future.” We have also used the terms “artificial intelligences” and “artificial entities.” These five terms are just a few of the many used in the design, development, and scholarship of AI.
When a term that expresses a concept is ambiguous, it becomes difficult to disentangle the theoretical connotation intended by the researchers and the meaning of participants’ responses in empirical studies. This reduces the impact of empirical research, especially for concepts that are tied to ordinary language like “artificial.” Researchers may assume that everyone shares their same definitions when in fact they are fuzzy concepts, “[that] possess two or more alternative meanings and thus cannot be reliably identified or applied by different readers or scholars” (Markusen, 2003, p. 702). Markusen argues that this can decrease scholars’ belief in the need for empirical tests, can lead to the devaluation of empirical evidence, and can reduce the credibility of research.
Conceptual ambiguity can lead to concept creep, when a concept becomes so broad and deep that non-examples are difficult to find. The methodological and real-world implications of conceptual ambiguity range from reduced trustworthiness of the research to the detachment of scholarship from policy-making and advocacy. For instance, a policy-maker is more likely to avoid “fuzzy concepts” when implementing policies because there are no clear definitions that can be used to structure their policy. That is, “fuzzy concepts” provide fuzzy guidance on how a policy will affect systemic power distributions, legal structures, and the actions of different people (e.g., advocates, politicians, lay people).
In the following sections, we attempt to include as many of the terms as we could find relevant to the interdisciplinary study of artificial entities’ moral, social, and mental capacities.
Note. We loosely group terms into four categories although several terms could belong to other categories.
The first set of terms elicits a mental image of an entity’s role in the world. These terms are most frequently used as nouns that can be modified with information about their material or psychological features, although some, like “super” and “transformative,” typically serve as adjectives. “Machine” is used as a noun with feature modifiers (e.g., “digital machine,” “autonomous machine”). However, it is also sometimes used as a feature-like modifier of other features (e.g., “machine intelligence”). Below are terms defining an entity’s role.
The second set of terms evokes a mental image of an artificial entity’s material structure. These terms focus either on what the entity is made from or how they exist. They often modify terms defining an entity’s role (e.g., “artificial agent”). Below are terms defining material features.
A third set of terms defines the psychological features of an entity. These terms operationalize abstract and indirectly observable mental phenomena (e.g., intelligence, friendliness). Psychological features are inferred from observing complex criteria agreed upon by experts. For example, we cannot observe “autonomy” directly. We infer “autonomy” from introspection (i.e., self-reports) and behavior. These terms differ from material features because material features can be directly observed. Below are terms defining psychological features.
Combinations of feature and role terms have been used within and across many fields of study to describe artificial entities. For instance, “friendly AI” has been used to describe AIs who mimic human friendliness and are benign to humans. “Moral machine” has been used to describe machines that make ethical decisions or need to solve moral dilemmas, like self-driving cars. Some of these combinations are more widely used and well-known. Other combinations have stronger implications for taking moral action and receiving moral consideration. Many combinations are used only in specific contexts, based on convenience, or merely as placeholders. Below we outline some combinations that we think are consequential and related to the moral consideration of artificial entities.
Is the same terminology useful for the general public, engineers, scientists, ethicists, lawyers, and policy-makers? There’s some suggestion that the general public and experts differ in their understanding of artificial entities and support for their rights. Would a common, consensual terminology enable more effective interdisciplinary research, policy-making, and advocacy? If having an imprecise definition is preferred, how can differences in terminology be reconciled to maximize the clarity and utility of the terms?
Whether or not terms generalize across contexts might matter. Should we use terminology that can be applied to humans, nonhuman animals, algorithms, and machines? Is it necessary to specify an entity’s role? Several of the consequential combinations do not (e.g., “artificial general intelligence,” “artificial sentience”). Does the psychological feature need to be narrow or broad? “Consciousness” is broad, but it is also “fuzzy” because of the many conceptualizations in current usage. On the other hand, a narrow term like “friendly” only applies to one aspect of an entity’s behavior and may not retain meaning over time and across context.
Below we explain our preference for “artificial sentience,” consider some possible reasons not to use “artificial sentience,” and consider using multiple terms.
We favor the term “artificial sentience” for the following linguistic reasons:
We favor the term “artificial sentience” for the following conceptual reasons:
We favor the term “artificial sentience” for the following moral reasons:
The best strategy might be to use multiple terms to represent the interests of algorithmically-based, at least partly non-biological entities, of which artificial sentience (AS) may eventually be considered an umbrella term. Below is a possible initial taxonomy of terms based on their relationship to the moral referents of patiency and agency and to the specificity of the term.
Figure 1: Possible Taxonomy of Terms
Note. Terms are positioned along two (of many possible) dimensions. Moral references are on the x-axis and specificity is along the y-axis. These locations are imprecise as they reflect cultural and intellectual associations that are likely to vary across readers and change over time.
Possible uses for specific terms:
Some terms define the academic fields of study surrounding artificial entities. The fields described below seem likely to have the greatest impact on long-term outcomes of artificial entity development such as how they will be designed and the resulting ethical implications.
 We consider terms and their connotations in the English language. The usage and meaning of some terms is likely to be different in other languages, a topic that requires future study. Linguistic differences may have global implications for the moral consideration of artificial entities that have yet to be examined.
 Blascovich and Ginsburg (1978) discuss conceptual ambiguity in regards to the social psychological study of “risk-taking.”
 See the Appendix for a list of terms defining relevant fields of study.
 See the Appendix for a list of terms defining relevant fields of study.
 Summarizing from Gray et al. (2007) and Gray et al. (2012).
 A term like “non-biological sentience” might contrast better with “biological sentience,” increasing the focus on “sentience” rather than on the entity’s material features and substrate (i.e., whether they are a human, a nonhuman animal, algorithm, or machine). Reducing the emphasis on substrate may facilitate attempts to increase the moral consideration of artificial entities.
 See Holden Karnofsky’s Cold Takes blog for more on “digital people.”