2.1. Coherentist Approach to Knowledge from Instruments

One approach to the topic of knowledge and technologies within the field of epistemology is found in the concepts of instrumental knowledge and knowledge from indicators. According to Lehrer's (Reference Lehrer1995) Evaluation Model of Instrumental Knowledge, to know that P through the use of an instrument, a subject must accept its trustworthiness and its output that P, as true. This acceptance depends upon having a trustworthy basis for evaluating the belief that P, and on being able to defend P's acceptance against possible objections.

Lehrer distinguishes between instruments which aid our senses (such as reading glasses) and instruments that provide information otherwise not available (such as a microscope). In the first case, a person accepts her own senses to be trustworthy; while in the second case, by contrast, a person must accept that the instrument and the relevant background theory involved are trustworthy.

Lehrer is considered a coherentist, meaning he denies the notion of basic foundational beliefs. The coherence theory of justification states that a belief is justifiably held if the belief coheres with a set of beliefs. Lehrer's early work on the coherence theory of justification can be distinguished from his later developments: first, Lehrer's (Reference Lehrer1990) “acceptance system”, in which a person needs to accept a belief, and Lehrer's (Reference Lehrer2000, Reference Lehrer and Olsson2003) “evaluation system” which involves more complex cognitive processes (Olsson Reference Olsson and Zalta2017: §4). The Evaluation Model of Instrumental Knowledge from 1995 employs both acceptance and evaluation and can be considered an early sign for Lehrer's later (2000, 2003) work.

Acceptance of information from instruments results from an effort to obtain truth and avoid an error. However, it is insufficient (i.e., it can be Gettiered). Only by evaluating beliefs based on the acquired information and knowing how to answer some possible objections about the instrument and its theory can beliefs acquired from instruments be considered as knowledge.

I recognize two problems with Lehrer's account. First, Lehrer spells out the justification condition in terms of the properties of the individual believing subject, rather than considering the social and technical environment. Second, Lehrer does not address the question of the extent to which a knower should know the inner workings of the instrument for defending the acceptance of P against any objections. Defending the acceptance of P is the basis for the evaluation of the belief that the instrument itself is trustworthy.

2.2. Rational-Inductive Approach to Knowledge from Instruments

Ernst Sosa's account of knowledge from instruments (Sosa Reference Sosa, Lackey and Sosa2006) can give an answer to the evaluation of a belief that an instrument is trustworthy and can solve the second problem posed by Lehrer's account. Phrased differently, Sosa's account addresses the question of the extent to which a knower should hold knowledge of the inner workings of the instrument. Sosa argues that the notion of testimonial knowledge presupposes the notion of instrumental knowledge. In his account, testimonial knowledge is considered as knowledge that is verbally transmitted from one subject to another using the instrument of language. Because we do not have direct access to another subject's perception, instrumental knowledge, including testimonial knowledge, cannot be reduced to non-instrumental knowledge: “Our access to the minds of others is after all mediated by various instruments, and we must trust such media at least implicitly in accessing the testimony all around us” (Sosa Reference Sosa, Lackey and Sosa2006: 118, emphasis in original).

A justified belief that an instrument is reliable ultimately derives from relying on our perceptual input. Unlike instruments, our senses differ insofar as we do not need, or cannot have, a rational basis to justify our beliefs. Our senses are “a gift of natural evolution, which provides us with perceptual modules that encapsulate sensory content and reliability in a single package” (Sosa Reference Sosa, Lackey and Sosa2006: 122). While we have some kind of a default justification for trusting our senses, we need some rational basis for accepting the output of instruments.

Sosa argues that the basis for accepting an instrument is reliable exists when a subject has an indication that the instrument indicates the outright truth and accepts this indication. The justification for relying on an instrument has an inductive basis: the more a subject uses it, the more she gains support for its reliability (Sosa Reference Sosa, Lackey and Sosa2006: 120). Once this rational basis is established, the behavior of relying upon what the instrument delivered is adopted. That is, when an instrument repeatedly produces a truth output, the subject is inclined to incorporate its reliability as an assumption.

Here, too, problems arise: first, like Lehrer, Sosa spells out the conditions for acquiring knowledge from instruments in terms of properties of the individual believing subject, without including social or technical characteristics. Second, Sosa does not explain how a subject acquires indications that the instrument is reliable.

2.3. Knowledge from Indicators

The third example of traditional accounts of acquiring knowledge from instruments comes from Millar's (Reference Millar2009) account of knowledge from indicators. He provides a general approach for explaining how a subject acquires indications, such as those Sosa refers to, and as such can solve the second problem posed by Sosa's account (explaining how a subject acquires indications that the instrument is reliable). Millar's work is grounded in the notion of a subject's successfully exercising her recognitional abilities. While Lehrer assumes that the justification of a subject's belief that an instrument is trustworthy depends on the subject's own evidence for the reliability of the instrument, Millar does not limit this justification only to evidence, but also expands to include the subject's recognitional abilities.Footnote ³

Similar to Sosa's account of knowledge from instruments, Millar's notion of knowledge from indicators fundamentally rests upon perception. For Millar, perceptual knowledge is the exercise of the subject's ability to recognize something she perceives (Millar Reference Millar2009: 120). When perceiving an indicator, the indication is understood as factive, grounded in a causal relation (149). An example is the fuel gauge: I look at my fuel gauge and notice it half-full. It is the quantity of fuel in my car's fuel tank that causes the gauge to indicate that it is indeed half-full. The indication is perceived by my perception.

Millar recognizes and acknowledges the problematic question raised by his account: “Knowledge from indicators is problematic because it seems puzzling that we can be entitled to take indicating phenomena to indicate what they do” (Millar Reference Millar2009: 162). The causal chain can be considered a black box. If the content of the black box is unknown, the causal chain cannot constitute a part of that which justifies a subject's knowledge as derived from indicators.Footnote ⁴

2.4. Instrument-based Beliefs and Natural Language Technologies

Traditionally, knowledge from instruments deals with mechanical instruments rather than digital technologies. Indications, such as a green light indicating a device is turned on, a fuel gauge, or a display that says “50 degrees” are different from natural language technologies that speak with words – such as a recording playing the next stop on the subway or speaking with a digital virtual assistant. Physical causality and mechanical explanations describe the indications. At the same time, software code, big data collected from the behavior of many, and algorithms, form the interactions of digital virtual assistants, chatbots, and other technologies that speak. Traditional analysis of knowledge from instruments is not suitable for analyzing natural language and algorithms.

Instrument-based belief is a belief “formed through reliance on an instrument's output or ‘read-out’” (Goldberg Reference Goldberg2012: 184). Epistemic accounts of analyzing knowledge from instruments mostly assess the reliability of the senses and the reliability of the instrument used for measurement rather than assess the content of the knowledge acquired and its source. For example, an analysis of the green led light on my phone charger might assess the causal chain, perceptual or inferential beliefs, or the reliability of the led light or my eyes to perceive it, but not the propositional content – “the battery is full” or “currently charging”. Additionally, these accounts do not capture the epistemic dependency of an individual on the larger technical environment and other people.

Epistemic accounts of analyzing knowledge from instruments are individualistic and suitable for mechanical instruments but not for technologies that speak in natural language. As social creatures, we have always relied on one another to gain knowledge. Since we rely on technologies to acquire knowledge, how else could it be possible to analyze the acquisition of knowledge from technologies that speak in natural language?

3.1. The Social-Philosophical Roots of ‘Testimony’

In the coming section, I present the renewed interest of scholars in the concept of testimony during the early 1990s. The origin of the concept of testimony helps us understand why the concept cannot be used today – for analyzing cases of acquiring knowledge from technologies that speak.

Much of what we know, as individuals and as groups, depends on the words of others: “We live in a sea of assertions and little if any of our knowledge would exist without it” (Lipton Reference Lipton1998: 1). Philosophers from all eras have put effort to making sense of how language represents the world, and how we share these representations. Yet, despite the fundamental role of other people's words in knowledge, it is only relatively recently that the concept of testimony has become an object of research. Current research originates from several seminal works.Footnote ⁵ Two of them are Coady's (Reference Coady1992) Testimony: A Philosophical Study, and about two years later, Shapin's (Reference Shapin1994) A Social History of Truth: Civility and Science in Seventeenth-Century England.

Coady's influential monograph discusses philosophical arguments about knowledge acquired from others. Gelfert, in his book dedicated to the concept of testimony, summarizes why Coady's monograph is considered the cornerstone of a research program about the say-so of others: “As with most truly influential books in philosophy, perhaps the greatest significance of Coady's book lies in the responses and criticisms it provoked, as well as in the philosophical theories that it inspired others to develop” (Gelfert Reference Gelfert2018: 2).

Shapin (Reference Shapin1994) studies the production of knowledge in 17th-century England. His cultural-historical research is based upon an argument that the scientific culture of that time was built upon the word of a gentleman. A gentleman's social status was a crucial factor in considerations regarding the question of whom to trust. Unlike common laborers or merchants, gentlemen were not affected by economic pressure, a force that could compromise the ability to tell the truth. Therefore, the question of which testimony we should accept can be answered based on social factors, such as status.

Shapin's work heavily influenced historians of science, sociologists, and specifically sociologists of knowledge – where the concept of testimony became a fundamental theoretical notion. For example, the Strong Programme in the Sociology of Knowledge is committed to the accepted formulation and defense of a theory of knowledge that holds testimony to the principal method by which epistemic communities are formed, and knowledge is generated. This is due to the ability of testimony to establish a social agreement that transforms mere opinion or belief into knowledge (Kusch Reference Kusch2002).

Within epistemology, the concept of testimony is used to describe cases in which a testifier asserts a proposition that the receiver of the testimony consequently believes. It is described in terms of a testifier T, who testifies a proposition P, to a hearer H (or a reader, or a receiver of the testimony). Hearer H's belief that P can be considered as a testimonial-based belief (Pritchard Reference Pritchard2004: 326). Described this way, testimony is the most elementary, yet all-encompassing, concept to describe the knowledge and the justification for knowledge traveling from one agent to another. Testimonies differ in their contents and contexts, and as such, the subfield of the epistemology of testimony grapples with various core issues and debates.Footnote ⁶

The concept of testimony can be considered a natural candidate for analyzing the case of a person who acquires beliefs by receiving verbal propositions from a device. However, whether or not a device can deliver testimony is debatable: the received view of testimony holds, generally, that only persons can participate in the act of testimony. This view is advocated by most philosophical accounts of testimony (Coady Reference Coady1992: 268; Lackey Reference Lackey2008: 189).

Similar to the received view of testimony within epistemology, the view that only persons can give testimony is also advocated by leading sociologists (e.g., Collins and Kusch Reference Collins and Kusch1998; Bloor Reference Bloor1999; Collins Reference Collins2010; for further sociological contexts of the concept of testimony, see Neges 2018; Freiman and Miller Reference Freiman, Miller and Goldberg2020). As Shapin argues, “in securing our knowledge we rely upon others, and we cannot dispense with that reliance. That means that the relations in which we have and hold our knowledge have a moral character” (Shapin Reference Shapin1994: xxv). Instruments, unlike gentlemen, lack such a character and cannot give testimony.

While both Coady and Shapin focus on the ubiquity of testimony, Coady focuses on the questions which shape a person's justification for accepting the testimony of others. Shapin argues that decisions concerning ‘who to believe’ are matters of moral and social characteristics (Lipton Reference Lipton1998). The received view in both the fields of epistemology and the sociology of knowledge has developed to reject the possibility of a technology testifier.

3.2. Anthropocentric Assumptions in Testimonial Theories of Knowledge

Having established the historical grounds for the position that a technological artifact cannot give testimony, it is possible to turn to current reasonings that categorically reject this option.

Elsewhere, I (Freiman Reference Freiman2021) recognize the ‘anthropocentric view of testimony’ as a commonly held view among social epistemologists. The view presupposes that only persons can participate in the act of testimony because only humans, in principle, can be qualified as a testifier. Underlying this view are commonly held assumptions in mainstream social epistemology that a testifier (a) must have intentions to deliver the testimony; (b) be subject to normative assessment; and (c) constitute a putative object in trust relations.

Technologies, arguably, do not have intentions. If testimony requires some kind of intention to deliver a proposition to the recipient of the testimony, then the concept of testimony cannot be used for analyzing knowledge from technologies. For example, Fricker (Reference Fricker2015: 179) categorizes “[automated] announcements at railway stations of train times, or automated messages one receives on telephone connections, that sound like a live human voice making statements, but are no such thing” as fake testimony.Footnote ⁷

Additionally, testifiers must be normatively responsible for what they say. As Fricker (Reference Fricker2002: 379) argues, “a teller is normatively responsible for the truth of what she asserts”. Since technological artifacts cannot be assigned that responsibility yet, they fail to be considered testifiers. For example, Goldberg (Reference Goldberg2012: 191) argues that only epistemic agents are “susceptible to full-blooded normative assessment”.

Lastly, testifiers must be trusted. Testimonial accounts of knowledge demand that the act of testimony will entail trust between the hearer and the speaker (e.g., Gelfert Reference Gelfert2014: §8, 2018: §5). However, according to a commonly accepted approach in the epistemology of trust, only humans can be objects of trust relations, rendering technologies as lacking, in principle, the property of trustworthiness (Miller and Freiman Reference Miller, Freiman and Simon2020; Freiman Reference Freiman2021). This, yet again anthropocentric view, usually shifts discussions of trust from artifacts to the humans behind the technologies (Pitt Reference Pitt2010: 445). Coeckelbergh (Reference Coeckelbergh2012) nailed its essence: “direct trust in artefacts is indirect trust in the humans related to the technology”.

Moving from technology to AIs, the issue of whether or not, and in what conditions, it is possible to trust AI is extensively discussed (see, e.g., Alvarado Reference Alvarado2022a). However, according to the traditional social-epistemic approach that trust entails a human quality that technologies lack, it is controversial to hold the view that AI can, in principle, be trustworthy or be an object of trust (Bryson Reference Bryson2018; Rieder et al. Reference Rieder, Simon and Wong2020; Ryan Reference Ryan2020; Freiman Reference Freiman2022). Since according to testimonial theories trust relations cannot be formed with technologies, AIs included, technologies, conversational AIs included, cannot be qualified as testifiers. The category of testimony-based beliefs is not suitable for analyzing beliefs acquired from conversational AIs.

3.3. Against the Argument that Testimony-Based Beliefs are Enough

There are existing approaches to technological artifacts as giving testimonies that contrast the received view. For example, Green (Reference Green2006, Reference Green2008) develops a view according to which technologies, and zombies, can give testimony. Green (Reference Green2008) argues that (some) of the beliefs that originated from technologies are testimony-based beliefs and that there is no need for a different category. Since beliefs from humans and beliefs from technologies have the same epistemic status and content, are a result of the same cognitive ability by the human hearer, and are experienced the same, the concept of testimony is sufficient.Footnote ⁸

Green's approach to testimony might solve the problem of acquiring knowledge from technologies that speak. However, this solution comes with a price: The symmetry between technologies and humans runs the risk of undermining the distinction between human and non-human agencies. Specifically, it would associate a non-human with intentions, the ability to be normatively assessed, and as a valid object in trust relations. These are incompatible with the accepted view in social epistemology.

In addition, it is possible that the outputs of conversational AIs can be analyzed as a form of a group testimony. There are at least two different arguments to make:Footnote ⁹ One sees the conversational AI's outputs as the testimony of the humans whose expressions were used in the training data sets – as a collective, and the second considers the conversational AI's outputs as the testimony of an expert community.

The first possible objection that recognizes the outputs of conversational AIs as a form of a group testimony argues that the corpus of their training data sets can be reduced to humans who expressed the text. While the idea is worth exploring, it is problematic: The epistemology of group testimony would either associate the group testimony to a collective knower or accord the group testimony to the many knowers who make up the group (Lackey Reference Lackey2014; Miller Reference Miller2015).

While I accept that there are viable possibilities for social structures that can deliver testimony as groups (e.g., commissions, research groups, departments, states, and so forth, see Faulkner Reference Faulkner2018), I reject both options regarding conversational AIs: recognizing a technological artifact such as a conversational AI as a collective knower anthropomorphizes it (similar to saying ‘Google knows’); and the individual humans, whose texts contributed to the data sets, lack the intention necessary for that act to be considered as giving testimony. Additionally, such a notion places a smokescreen on the ability to normatively analyze the algorithms involved in generating the propositions and those who engineered them – humans and institutions, as responsible and accountable for the product (Freiman and Geslevich Packin Reference Freiman and Geslevich Packin2022). In this case, the notion of group testimony is not suitable for acquiring beliefs from the outputs of conversational AIs.

A second approach to identifying the outputs of AIs as a form of group testimony might derive from a discussion about computer simulations. In Symons and Alvarado's (Reference Symons and Alvarado2019) discussion of what it means to trust the results of a computer simulation, they raise the question of whether computer simulations are sources of expert testimony. While some scholars, as their argument goes, argue that trusting the results of computer simulations poses similarities to trusting the say-so of expert testimony or to trusting perception, they suggest that trust is given to the testimony of expert communities, rather than directly the output of the simulations. To use their example, laypeople trust the judgment of meteorologists, with respect to the models they use to predict if a hurricane is likely to hit their city. While in their example, laypeople do not engage directly with the output of the weather models, in the case of conversational AIs, users engage directly with the output of the technology. The testimony of the expert community is not a notion suitable for analysis of acquiring beliefs from the outputs of conversational AIs.

The lacuna can now be clearly identified: traditional approaches in epistemology and their notion of instrument-based beliefs and approaches in social epistemology and their notion of testimony-based beliefs fail to provide a proper ability to analyze knowledge acquisition from conversational AIs. In the next section, I build upon an existing distinction between instrument-based beliefs and testimony-based beliefs. I suggest adding a new, third, category: technology-based beliefs. Technology-based beliefs acknowledge the non-human agency of the source of knowledge acquired and, at the same time, acknowledge the verbal content of the proposition delivered. Technology-based beliefs address the lacuna of analyzing knowledge acquisition from technologies that speak in natural language.

4.1. Anthropocentricity: Revolutionizing Social Epistemology or Maintaining its Assumptions?

It might be that the commonly accepted social epistemological approaches to the generation, dissemination, and justification of knowledge (e.g., Hardwig Reference Hardwig1985; Kitcher Reference Kitcher1990; Longino Reference Longino2002) are simply insufficient for the analysis of acquiring knowledge from technologies that speak. This is because mainstream approaches regard epistemic processes as socio-cognitive. They ultimately neglect the possibilities of technologies (that speak) participating in knowledge acquisition and belief formation. The question we are left with is how to proceed.

If we wish to evaluate the epistemic roles of technologies that speak in existing social-epistemic terms, we face two options. The first option is rejecting social epistemology's assumptions about the difference between human and non-human agency. Unfortunately, this option will likely lead to a complete revision of fundamental concepts, such as trust, testimony, knowledge, and other concepts currently treated by social epistemologists as anthropocentric.Footnote ¹⁰

The second option is introducing new concepts and methods for evaluating the epistemic roles of technologies in current social-epistemic terms. In this option, the new concepts and methods are consistent with fundamental concepts commonly used within mainstream social epistemology. It expands mainstream social epistemology rather than a fundamental revision of it.

While scholars who thought of the examples in §3.3 favor the first option, I favor the second option. Maintaining a distinction between humans and technologies over issues such as agency and morality and keeping concepts such as knowledge and testimony as human-centred is what I believe social epistemology is about: the humane perspective of social knowledge. It is just that our society now has technologies that speak, too. Therefore, in the next section, I offer a concept for analyzing knowledge acquired from technologies in a way that does not constitute a contradiction with received assumptions.

4.2. Knowledge from Natural Language Technologies

Gelfert, in his book Introduction to Testimony, offers a category of computer-generated belief:

What about a conversation with a technological artifact enacted via natural language rather than indicators? Taking a cue from Gelfert, I suggest deepening the distinction between testimony-based beliefs and instrument-based beliefs by adding a new category. This new category can encompass the agency-status of the originator of the belief and the kind of content that itself eventually becomes a belief. I suggest this third category is technology-based beliefs.

The concept of instrument-based belief correctly captures that the source of the belief, whether perceptual or inferential, is non-human. Similarly, the concept of technology-based belief rests upon the assumption that the source of the content is non-human too. Both categories share that the source of the belief is non-human.

Additionally, the concept of testimony-based belief correctly captures that the testimony is delivered in natural language propositions. Likewise, the concept of technology-based beliefs rests upon the assumption that the non-human delivers propositions in natural language. The ‘technology-based belief’ solution does not assume that the technology that speaks has a human-like agency. Both categories share that the output content is delivered in natural language (Table 1).

Table 1. Technology-Based Beliefs: Sources of Knowledge and Content Type.