## 1. What is Social Epistemology?



Epistemology is concerned with how people should go about the business
of determining what is true. Social epistemology is concerned with how
people can best pursue the truth *with the help* of, or
sometimes *in the face* of, other people or relevant social
practices and institutions. It is also concerned with the pursuit of
truth by groups, or collective agents.



The most influential tradition in (Western) epistemology, best
exemplified by Rene Descartes (1637), has focused almost
exclusively on how individual epistemic agents, using their own
cognitive faculties, can soundly pursue truth. Descartes contended
that the most promising way to do so is by use of one's own
reasoning, as applied to one's own "clear and
distinct" ideas. The central challenge for this approach is to
show how one can discern what is true using only this restricted
basis. Even early empiricists such as John Locke (1690) also insisted
that knowledge be acquired through intellectual self-reliance. As
Locke put it, "other men's opinions floating in
one's brain" do not constitute genuine knowledge.



In contrast with the individualistic orientations of Descartes and
Locke, social epistemology proceeds on the idea that we often rely on
others in our pursuit of truth. Accordingly, social
epistemology's core questions revolve around the nature, scope,
and epistemic significance of this reliance: what are the ways we rely
on others when seeking information, and how does our relying on others
in these ways bear on the epistemic goodness of our resulting beliefs?
(See Greco (2021) for an informative discussion of both of these
questions.)



Since epistemology itself emerged in the modern period along with the
rise of science, where reliance on others (in replication and
elsewhere) is pervasive, one might wonder why social epistemology has
only really come into its own in the last few decades. One possible
explanation may lie in the individualistic self-understanding of early
modern science: the Royal Society of London, created in 1660 to
support and promote scientific inquiry, had as its motto
"*Nullius in verba*"--roughly, "take no
one's word for it." Another important factor appears to
have been the centrality philosophers have traditionally ascribed to
the problem of skepticism. Such an orientation presents the pursuit of
truth as a solitary endeavor, where epistemology itself centers on the
challenges and practices of individual agents. Still, once
individualism in epistemology is called into question, we will see
that there are important connections between social epistemology and
philosophy of science.


## 2. Giving Shape to the Field of Social Epistemology



Along these lines, an approach somewhat analogous to social
epistemology was developed in the middle part of the 20th century.
Primarily sociological in nature, this movement focused on how science
is actually practiced, often aiming to debunk what theorists saw as
the "idealized" accounts of science in orthodox
epistemology and in mid-century philosophy of science. Members of what
came to be known as the "Strong Program" in the sociology
of science, such as Bruno Latour and Steve Woolgar (1986), challenged
the notion of objective truth, arguing that so-called
"facts" are not discovered by science but rather are
"constructed," "constituted," or
"fabricated." Another proponent of the Strong Program,
David Bloor, advocated for the "symmetry thesis,"
according to which scientists' beliefs are to be explained by
social factors, regardless of whether these beliefs are true or false,
rational or irrational (Bloor 1991: 7). This view denies the
explanatory relevance of such things as facts and evidence.



Views like the Strong Program found additional support in certain
parts of philosophy (especially among those embracing
deconstructionism). Michel Foucault, for example, developed a
radically political view of knowledge and science, arguing that
practices of so-called knowledge-seeking are driven by quests for
power and social domination (1969 [1972], 1975 [1977]). Richard Rorty
(1979) rejected the traditional conception of knowledge as
"accuracy of representation" and sought to replace it with
a notion of the "social justification of belief." Other
philosophers ascribed a central explanatory role to social factors but
were less extreme in their critique of objectivity in science. Most
significant among these was Thomas Kuhn, who had been trained as a
physicist but who moved post-PhD into the history and philosophy of
science. Kuhn held that purely objective considerations could never
settle disputes between competing theories, underscoring the social
factors that influence the development of scientific theory (Kuhn
1962/1970). Debates about these topics persisted under the heading of
"the science wars."



Even for those philosophers who repudiate these sorts of skeptical and
debunking positions, there are important lessons from the debates such
positions inspired. Above all, the key lesson concerns the importance
of social factors in cognition, including the role of cultural beliefs
and the biases that operate in the pursuit of knowledge. It is in this
context that what we might call the dominant strand of social
epistemology emerged.



Departing sharply from the debunking themes sketched above,
contemporary social epistemology aims to acknowledge and account for
the variety of social factors that figure centrally in the pursuit of
truth, without surrendering the very notions of truth and falsity,
knowledge and error. Theorists in this tradition often defend
proposals that are continuous with traditional epistemology. While
they continue to acknowledge that there are identifiable cases in
which social factors or social interactions pose threats to truth
acquisition (see below), they also contend that the right kinds of
social organization and social norms enhance the prospects of
acquiring truth.



The seminal defense for this sort of approach to social epistemology
is Alvin Goldman (1999). This book, whose influence on contemporary
social epistemology is hard to overstate, developed Goldman's
"veritistic" approach, which focuses on the reliability
with which various social practices produce true beliefs (Goldman
1999: 5). While Goldman (1999) presented a framework for research in
social epistemology, work on relevant topics had already begun to
flourish. Testimony emerged as a central topic for social epistemology
owing to works such as Elizabeth Fricker (1987), Edward Craig (1990),
and C.A.J. Coady (1992). Philosophy of science, too, eventually became
an important site of contemporary social epistemology; seminal work
includes Philip Kitcher (1990, 1993), Helen Longino (1990, 2002), and
Miriam Solomon (2007). Margaret Gilbert (1989) made a forceful case
for the existence of "plural subjects," thereby providing
the metaphysical foundations for the social epistemology of
collectives. And, under the influence of Cristina Bicchieri (2005),
social epistemologists began to take stock of the importance of social
norms in social epistemological analysis (see e.g. Henderson and
Graham (2019) and Sanford Goldberg (2018, 2021).



The salience of social epistemology has also been enhanced by journals
devoted in whole or in part to the topic. In 1988, Steve Fuller
created the journal *Social Epistemology*, whose original focus
was on social studies of science and on science and technology
studies. Some years later Alvin Goldman, inspired by the desire for a
social epistemology journal that engaged more with
"mainstream" epistemology, started *Episteme: A Journal
of Individual and Social Epistemology* in 2004. (While it
originally focused exclusively on social epistemology, it has since
expanded its scope to include individual epistemology.)


## 3. Central Topics in Social Epistemology



Here we look at some core topics.


### 3.1 Testimony



When it comes to the various ways we rely on others as we engage in
the pursuit of truth, testimony is paradigmatic. For our purposes
here, we can think of testimony as the act in which one agent (the
speaker or writer) reports something to an audience. An audience who
accepts the report on the speaker's authority acquires a
"testimony-based" belief. Social epistemologists have
raised several questions regarding testimonial transactions.



The central topic in the "epistemology of testimony"
concerns how testimony-based beliefs are to be evaluated. The core
question here is whether testimony is to be regarded as a basic source
of justification. We can think of a basic source of justification as a
source whose reliability can be taken for granted and relied upon,
except in cases in which one has reasons for doubt. As illustration,
consider perception. A perceptual belief of yours can be justified
even without your having reasons to assume that perception is
reliable. (It suffices that you lack reasons for doubt in the case at
hand.). The question is whether testimony can be treated
similarly.



Those who deny that testimony is a basic source of justification hold
that testimony-based beliefs are justified only if the audience has
adequate independent reasons to regard the speaker's testimony
as trustworthy. Such a view, known as "reductionism" since
it proposes that the justification of these beliefs can be
"reduced to" justifications provided by other sources
(perception, memory, induction), was defended by David Hume.
Contemporary defenders argue that the denial of reductionism is a
"recipe for gullibility," (Fricker 1994), or that it
sanctions irresponsibility (Faulkner 2000; see also Malmgren 2006 and
Kenyon 2013). By contrast, those who hold that testimony *is* a
basic source of justification hold that testimony-based beliefs are
justified so long as the audience has no reasons for doubt. Such a
view, known as "anti-reductionism," was defended by Thomas
Reid (1764/1983), who argued that honesty (in speakers) and credulity
(in audiences) are as much a part of our natural psychological
endowment, and so are as worthy of being relied upon in
belief-formation, as is the faculty of perception. Contemporary
theorists have offered additional arguments for anti-reductionism.
Coady (1990) argues that audiences typically lack the evidence needed
to confirm the reliability of the speakers they encounter, so that
denying anti-reductionism is a recipe for skepticism; Burge (1993)
argues that intelligible speech itself is an indication of having been
produced by a rational source, one which by nature aims at truth (and
so is worthy of being believed); and various others have offered
variations on Reid's own argument.



In addition to the question of whether testimony is a basic source of
justification, a second question concerns whether we can specify the
conditions on justified testimony-based belief in individualistic
terms, that is, terms that are restricted to materials from the
audience alone (the evidence in her possession, the reliability of her
faculties, etc.). While individualism remains the dominant view on
this score, a number of social epistemologists have rejected this in
favor of one or another version of *anti-individualism* about
testimonial justification. According to these views, the justification
of an audience's testimony-based belief can be affected by
factors including *the speaker's* epistemic condition
(Welbourne 1981, Hardwig 1991, Schmitt 2006, Lackey 2008, Goldberg
2010) or the general reliability of testimony *in the
audience's local environment* (Kallestrup and Pritchard
2012, Gerken 2013, 2022). Still, anti-individualistic views remain
controversial (see Gerken 2012, Leonard 2016, 2018).



A third question social epistemologists have raised regarding
testimonial exchanges concerns the interpersonal nature of the act of
testifying itself. According to the assurance view of testimony
(Hinchman 2005, Moran 2006, McMyler 2011), testifying is an act of
assurance, and beliefs formed on the basis of another's
assurance should not be understood in ordinary evidentialist terms.
(See also Lawlor (2013).) According to trust views (Faulkner 2011,
Keren 2014), the act of testifying takes place in a context rich with
norms of trust whose presence serves to make testimonies more
reliable, and hence more worthy of trust (see also Graham 2020 for
related discussion). Both views remain controversial (see Lackey
2008).



A question that has recently begun to attract more attention from
social epistemologists concerns the role of technology in testimony.
*Wikipedia* entries may be testimony, but they have multiple
authors; how does this affect the epistemology of
*Wikipedia*-based belief? (See Tollefsen 2009, Fallis 2011,
Fricker 2012). ChatGPT and other forms of AI produce reports (or
apparent reports) that incorporate results from fully automated
search; is this testimony? Some, thinking of testimony as a speech act
for which a speaker bears responsibility, deny that AI-produced
sentences constitute testimony (see e.g. Goldberg 2020 for a defense
of this view regarding instrument-based belief); others embrace the
idea of AI-testimony and argue that we ought to extend the
epistemology of testimony accordingly (Freiman and Miller 2020,
Freiman 2023).


### 3.2 Peer Disagreement



In many cases of testimony, we believe what another person tells us.
But in other cases we disagree with them. When this is so, is it
rational for us to continue to hold onto our beliefs with the same
degree of confidence as before? Or does rationality require us to
reduce our confidence? This would appear to depend on our evidence
regarding who is better-placed to reach a correct verdict on the
matter at hand. But consider the case in which, prior to the
disagreement, one has excellent evidence that one's interlocutor
is an "epistemic peer," someone who is roughly as likely
as oneself to get it right on the matter at hand. What, if anything,
does rationality require in this ("peer disagreement")
case?



"Conciliationism" is the view that in peer disagreement
(some degree of) modification in one's confidence is rationally
required. Two related considerations seem to support conciliationism:
failure to conciliate appears to be objectionably dogmatic, and the
disagreement itself seems to constitute some (higher-order) evidence
that one has erred. The most demanding version of Conciliationism is
the Equal Weight View, according to which one ought to assign equal
weight to a peer's opinion as to one's own (Christensen
2007, Elga 2007, Feldman 2006, 2007, and Matheson 2015).



Critics of conciliationism offer a number of objections. One presents
a charge of self-refutation: since conciliationism itself is a
widely-disputed claim, it follows (given conciliation) that if
conciliationism is true, we are not in a position to rationally
believe it. (For further discussion, see Christensen 2013.) A second
criticism derives from the "right reasons" view of Kelly
(2005). Suppose that after all evidence has been disclosed two peers
continue to disagree over whether interests rates will rise. Since the
fact of disagreement itself is not evidence bearing on whether
interest rates will rise, it is irrelevant to what one should believe
regarding whether interest rates will rise. For this very reason,
learning of a peer disagreement should not affect one's
confidence on this topic at all. Rather, what rationality requires
here is what rationality requires everywhere: belief in accordance
with the relevant evidence. (Titelbaum (2015) offers a version of this
argument restricted to the domain of beliefs regarding the norms of
rationality themselves.)



In a more recent paper, Kelly has developed a third criticism of
conciliationism, which calls into question whether rationality
requires the same thing in every peer disagreement. Developing what he
calls the "Total Evidence View," Kelly (2010) puts the
point this way:



> 
> 
> [If] you and I have arrived at our opinions in response to a
> substantial body of evidence, and your opinion is a reasonable
> response to the evidence while mine is not, then you are not required
> to give equal weight to my opinion and to your own. Indeed, one might
> wonder whether you are required to give *any* weight to my
> opinion in such circumstances. (2010: 135)
> 
> 
> 



Lackey (2010) develops a similar "justificationist"
position about peer disagreement. According to her view, what
rationality requires in a case of peer disagreement is determined by
what one's total evidence supports *after* one adds the
evidence one acquires in the disagreement itself. In some cases, she
argues, one's (post-disagreement) total evidence will require a
significant change in one's degree of confidence, in other cases
it will require no change at all, and in still other cases it will
require something in between. As we will see, these positions are
roughly consistent with more formal, Bayesian approaches to updating
beliefs on the credences of others.



Debates over peer disagreement have branched out in interesting ways.
One branch concerns the epistemic significance of disagreement that is
(not actual but) reasonably anticipated. (See Ballantyne (2015) for an
argument for skeptical results.) A second branch concerns the
epistemic significance of what Fogelin (1985) called
"deep" disagreements, long-standing controversies in which
there is no consensus about how to resolve the disagreement. While
deep disagreement has long been the basis for skeptical worries about
the possibility of ethical or religious knowledge, social
epistemologists have turned their eyes more recently towards
philosophy itself (Frances 2010, Goldberg 2013, Kornblith 2013) and
beyond (Frances 2005).


### 3.3 Group Belief



The cases discussed thus far focus on epistemic agents who are
individuals. What makes them topics of social epistemology is that
they involve agents interacting in the course of belief-formation,
where this interaction bears on the epistemic status of one or more of
their beliefs. The cases we will now consider involve not individual
agents but rather groups that appear to act as collective epistemic
subjects. When we speak of collective epistemic subjects, we have in
mind collections of individuals which constitute a group to which
actions, intentions, and/or representational states, including
beliefs, are ascribed. Such collections might include juries, panels,
governments, assemblies, teams, etc.



Social epistemologists have addressed various questions concerning the
nature of such "collective" subjects, of which we
highlight the two most salient ones. First, under what conditions can
a group be said to believe something? (Here social epistemology has
borrowed extensively from extant discussions of this question in
philosophy of mind, action theory, and social and political
philosophy). Second, given a case in which a group believes something,
under what conditions does this belief count as epistemically
justified (or amount to knowledge)? We consider this latter question
below in section 3.4.



Let us begin with the question about group belief. Two main views have
dominated the discussion: Summativism and Non-Summativism (or
Collectivism). According to Summativism about group belief, group
belief is a function of the beliefs of its members. On a simple
version, a group believes something just in case all, or almost all,
of its members hold the belief (Quinton 1976: 17). Since Summativism
construes claims asserting group beliefs as merely summarizing claims
about the beliefs of the individuals who make up the group, this view
is popular among those who worry about the "metaphysics"
of group agents.



Non-summativist or "Collectivist" accounts of group belief
are motivated by objections to Summativism. Margaret Gilbert objects
that it is common in ordinary language to ascribe a belief to a group
without assuming that most or all members hold the belief in question.
On this basis, she advances a Non-Summativist account of group belief
based on the notion of joint commitment, according to which:



> 
> 
> A group *G* believes that *p* if and only if the members
> of *G* are *jointly committed* to believe that
> *p* as a body.
> 
> 
> 



Joint commitments create normative requirements for group members to
emulate a single believer. On Gilbert's account, the commitment
to act this way is common knowledge, and if group members do not act
accordingly they can be held normatively responsible by their peers
for failing to do so (see Gilbert 1987, 1989, 2004; see also Tuomela
1992, Schmitt 1994a, and Tollefsen 2015 for variations on this
theme).



While joint commitment accounts of group belief are popular, they are
not beyond criticism. One worry is that they focus on responsibility
to peers, and not on the belief-states of the group members. On this
basis Wray (2001) suggests that they should be considered accounts of
group acceptance instead. Another worry is that joint commitment
accounts fail to recognize that there can be various reasons for joint
commitment, not all of which are reflective of group belief (Lackey
2021).



A different Collectivist approach is taken by Alexander Bird (2014,
2022) who contends that the joint acceptance model of group belief is
only one of many different (but legitimate) models. For instance, he
introduces the "distributed model" to deal with systems
that feature information-intensive tasks which cannot be processed by
a single individual. Several individuals must gather different pieces
of information while others coordinate this information and use it to
complete the task. (See also Hutchins 1995.) Bird contends that this
is a fairly standard type of group model that occurs in science. (See
also Brown 2023 for a variant on this position, motivated by
functionalism about the propositional attitudes.)



An interestingly "hybrid" account of group belief is that
of Lackey (2021), who adopts what she terms the "Group
Agent" account. According to it,



> 
> 
> A group, G, believes that p if and only if: (1) there is a significant
> percentage of G's operative members who believe that p, and (2)
> are such that adding together the basis of their beliefs that p yields
> a belief set that is not substantively incoherent. (2021: 49)
> 
> 
> 



While (1) is a Summativist condition, (2), which is meant to avoid
ascribing belief to a group when its members' reasons cannot be
coherently combined, is a normative requirement that governs the
collective itself. The account is thus Summativist without being
reductive.


### 3.5 Group Justification



Until this point, we have only looked at the phenomenon of group
belief itself. We have not yet considered how such beliefs can be
evaluated from the epistemic point of view -- that is, how such
belief can be evaluated as to whether it is justified, reasonable,
warranted, rational, knowledgeable, etc. In this section we explore
this question by focusing on the conditions under which a
group's belief is justified.



If the government of the United States were to believe that global
warming presents significant environmental challenges, we might say it
was justified in doing so because of the overwhelming consensus of
climate scientists to this effect. Under what conditions can we say
that the belief of a group is justified?



Schmitt (1994a: 265) held that a group belief is justified only if
every member of the group has a justified belief to the same effect.
But this seems to make group justification too hard to come by (Lackey
2016: 249-250). Goldman (2014) defended a Process Reliabilist
account of group justification. The basic idea of Process Reliabilism
is to construe justification in terms of the reliable production of
true belief, where this is understood to involve (i) a cognitive
process that reliably produces true belief, or else (ii) a cognitive
process (such as *drawing an inference*) that takes beliefs as
inputs and which reliably produces true beliefs when its inputs are
justified (Goldman 1979). Goldman (2014) proposes to treat group
justification in analogous terms. Starting with the requirement that
the group's belief be caused by a type of belief-forming process
that takes inputs from member beliefs in some proposition and outputs
a group belief in that proposition, his idea is to model group
justification along the lines of (ii). A type of process that
exemplifies this feature might be a majoritarian process in which
member beliefs (of the group) are aggregated into a group belief. Such
a process is likely to produce a true belief when its inputs--the
individual members' beliefs to the same effect--are
justified.



Lackey (2021) develops various criticisms of Goldman (2014) and
defends an alternative account of group justification on this basis.
According to her alternative account, a group G justifiedly believes
that p if and only if (1) G believes that p [see 3.3 above for her
"hybrid" analysis of this] and



> 
> 
> (2) Full disclosure of the evidence relevant to the proposition that
> p, accompanied by rational deliberation about that evidence among the
> members of G in accordance with their individual and group epistemic
> normative requirements, would not result in further evidence that,
> when added to the bases of G's members' beliefs that p,
> yields a total belief set that fails to make sufficiently probable
> that p. (2021: 97)
> 
> 
> 



An alternative account of group justification can be found in Brown
(2024). Like Goldman (2014), Brown appeals to the testimony of group
members in its account of group justification, but unlike Goldman,
Brown's account does not require the beliefs expressed in these
testimonies to be justified in order for the group's belief to
be justified. In this way, Brown argues, her account is not
susceptible to the objections Lackey (2021) raises against Goldman
(2014).


## 4. Formal Approaches to Social Epistemology



We have now seen some of the problems that face those who develop
accounts of knowledge acquisition within a community. In recent years
philosophers have turned to formal methods to understand some of these
social aspects of belief and knowledge formation. There are broadly
two approaches in this vein. The first comes from the field of formal
epistemology, which mostly uses proof-based methods to consider
questions that mostly originate within individual-focused
epistemology. Some work in this field, though, considers questions
related to, for example, judgment aggregation and testimony. The
second approach, sometimes dubbed "formal social
epistemology," stems largely from philosophy of science, where
researchers have employed modeling methods to understand the workings
of epistemic communities. While much of this work has been motivated
by a desire to understand the workings of science, it is often widely
applicable to social aspects of belief formation.



Another distinction between these traditions is that while formal
epistemologists tend to focus on questions related to ideal belief
creation, such as what constitutes rationality, formal *social*
epistemologists have been more interested in explaining real human
behavior, and designing good knowledge-creation systems. We will now
briefly discuss relevant work from formal epistemology, and then look
at three topics in formal social epistemology.


### 4.1 Formal Epistemology in the Social Realm



As mentioned, formal epistemology has mostly focused on issues related
to individual epistemology. This said, there is a significant portion
of this literature addressing questions including 1) how should a
group aggregate their judgements? 2) how should a group aggregate
their (more fine-grained) beliefs? 3) how should Bayesians update on
the testimony of others? and 4) what sorts of aggregation methods
create rational or effective groups?



Let's start with judgement aggregation. Judgement aggregation
assumes that individuals in a group hold binary opinions or attitudes
on some matters. These could be factual like "defendant X is
innocent" or actionable like "we should get Chinese food
tonight." The question is then how the group should aggregate
these judgements to facilitate group action. Or: how can individually
rational judgements be combined into a rationally judging group?



A sticky problem that emerges is the "doctrinal paradox,"
originally formulated by Kornhauser and Sager (1986) in the context of
legal judgments. Suppose that a court consisting of three judges must
render a judgment. The group judgment is to be based on each of three
related propositions, where the first two propositions are premises
and the third the conclusion. For example:


1. The defendant was legally obliged not to do a certain action.
2. The defendant did do that action.
3. The defendant is liable for breach of contract.



Legal doctrine entails that obligation and action are jointly
necessary and sufficient for liability. That is, conclusion (3) is
true if and only if the two preceding premises are each true. Suppose,
however, as shown in the table below, that the three judges form the
indicated beliefs, vote accordingly, and the judgment-aggregation
function delivers a conclusion guided by majority rule.




|  |  |  |  |
| --- | --- | --- | --- |
|  | Obligation? | Action? | Liable? |
| Judge 1 | True | True | True |
| Judge 2 | True | False | False |
| Judge 3 | False | True | False |
| Group | True | True | False |



In this example, each of the three judges has a logically
self-consistent set of beliefs. Moreover, a majority aggregation
function seems eminently reasonable. Nonetheless, the upshot is that
the court's judgments are jointly inconsistent.



This kind of problem arises easily when a judgment is made by multiple
members of a collective entity. This led a number of authors, starting
with List and Pettit (2002), to prove impossibility theorems in which
reasonable-looking combinations of constraints were nonetheless shown
to be jointly unsatisfiable in judgment aggregation. Further
generalizations are due to Pauly and van Hees 2006, Dietrich 2006, and
Mongin 2008. These results reflect Arrow's famous impossibility
theorem for preference aggregation (Arrow 1951/1963).



In light of these results, various "escape routes" have
been proposed. List and Pettit (2011) offer ways to relax requirements
so that majority voting, for example, satisfies collective
rationality. Briggs et al. (2014) argue that it may be too strong to
require that entities always have logically consistent beliefs.
Following Joyce (1998), they introduce a weaker notion of coherence of
beliefs. They show that the majority voting aggregation of logically
consistent beliefs will always be coherent, and the aggregation of
coherent beliefs will typically be coherent as well.



Some apply the theory of judgment aggregation to the increasingly
common problem of how collaborating scientific authors should decide
what statements to endorse. Solomon (2006), for instance, argues that
voting might help scientists avoid "groupthink" arising
from group deliberation. While Wray (2014) defends deliberation as
crucial to the production of group consensus, Bright et al. (2018)
point out that consensus is not always necessary (or possible) in
scientific reporting. In such cases, they argue, majority voting is a
good way to decide what statements a report will endorse, even if
there is disagreement in the group.



Rather than focusing on the aggregation of judgements, we might,
instead, consider aggregating degrees of belief, or
"credences." These are numbers between 0 and 1
representing an agent's degree of certainty in a statement. (For
instance, if I think there is a 90% chance it is raining, my credence
that it is raining is .9.) This representation changes the question of
judgement aggregation to something like this: if a group of people
hold different individual credences, what should the group credence
be?



This ends up being very closely related to the question of how an
individual ought to update their credences upon learning the credences
of others. If a rational group ought to adopt some aggregated belief,
then it might also make sense for an individual in the group to adopt
the same belief as a result of learning about the credences of their
peers. In other words, the problems of belief aggregation, peer
disagreement, and testimony are entangled in this literature. (Though
see Easwaran et al. (2016) for a discussion of distinctions between
these issues.) We'll focus here on belief aggregation.



In principle, there are many ways that one can go about aggregating
credences or pooling opinions (Genest and Zidek 1986). A simple option
is to combine opinions by linear pooling--taking a weighted
average of credences. This averaging could respect all credences
equally, or put extra weights on the opinions of, say, recognized
experts. This option has some nice properties, such as preserving
unanimous agreement, and allowing groups to aggregate over different
topics independently (DeGroot 1974; Lehrer and Wagner 1981). Despite
some issues which will be described shortly, it is defended by many in
formal epistemology as the best way to combine credences (Moss 2011,
Pettigrew 2019b).



In thinking about ideal knowledge creation though, we might ask how a
*Bayesian* (i.e., an individual who rationally holds to ideals
of credence updating) should update credences in light of peer
disagreement or how a group of Bayesians should aggregate beliefs. A
Bayesian will not simply average across beliefs, except under
particular assumptions or in special cases (Genest and Zidek 1986;
Bradley 2007; Steele 2012; Russell et al. 2015). And a group that
engages in linear averaging of this sort can typically be Dutch
booked, meaning they will accept a series of bets guaranteed to lose
them money.



A fully-fledged Bayesian approach to aggregation demands that final
credences be derived by Bayesian updating in light of the opinions
held by each group member (Keeney and Raiffa 1993). Notice, this is
also what a Bayesian individual should do to update on the credences
of others. To do this properly, though, is very complicated. It
requires prior probabilities about what obtains in the world, as well
as probabilities about how likely each group member is to develop
their credences in light of what might obtain in world. This will not
be practical in real cases.



Instead, many approaches consider features that are desirable for
rational aggregation, and then ask which simpler aggregation rules
satisfy them. For instance, one thing a rational aggregation method
should do (to prevent Dutch booking) is yield the same credence
regardless of whether information is obtained before or after
aggregating. For instance, if we all have credences about the rain,
and someone comes in wearing shorts, it should not matter to the final
group output whether 1) they entered and we all updated our credences
(in a Bayesian way) and then aggregated them, or 2) we aggregated our
credences, they entered, and we updated the aggregated credence (in a
Bayesian way). Geometric methods, which take the geometric average of
probabilities over *worlds*, yield this desirable property (and
other desirable properties) in many cases (Genest 1984; Dietrich and
List 2016, Russell et al. 2015, Baccelli & Stewart 2023). These
methods proceed by multiplying (weighted) credences over worlds that
might obtain and then renormalizing them to sum to 1.



One thing that geometric averaging does not do, though, is allow for
credences over different propositions to be aggregated completely
independently from each other. (This is something often treated as a
desideratum for judgment aggregation.) For instance, our beliefs about
the probabilities of hail might influence how we will aggregate our
beliefs over the probabilities of rain. Instead, a more holistic
approach to aggregation is required. This is an important lesson for
approaches to social epistemology which focus on individual topics of
interest in addressing peer disagreement and testimony (Russell et al.
2015).



Another thing that some take to be strange about geometric averaging
is that it sometimes will aggregate identical credences to a different
group credence. For instance, we might all have credence .7 that it is
raining, but our group credence might be .9. Easwaran et al. (2016)
argue, though, that this often makes sense when updating on the
credences of others--their confidence *should* make us
more confident (see also Christensen 2009). In light of critiques of
both geometric and linear averaging, Kinney (2022) argues that
attention should be paid to the underlying models of the world that
individuals are working from when combining credences. He advocates
for an aggregation method using "model stacking" a la Le
and Clarke (2017). A general take-away is that although the question
of rational credence aggregation might initially sound trivial, this
is very far from the reality. There are deep and enduring questions
about what a rational group consists in.



The question of whether aggregated credences can be more extreme than
individual ones echoes much earlier work bearing on the question: are
groups smart? In 1785, the Marquis de Condorcet wrote an essay proving
the following. Suppose a group of individuals form independent beliefs
about a topic and they are each more than 50% likely to reach a
correct judgement. If they take a majority vote, the group is more
likely to vote correctly the larger it gets (in the limit this
likelihood approaches 1). This result, now known as the
"Condorcet Jury Theorem," underlies what is sometimes
called the "wisdom of the crowds": in the right conditions
combining the knowledge of many can be very effective. Many have drawn
on this result to think about the rationality of group reasoning and
decision making.



In many cases, though, real groups are prone to epistemic problems
when it comes to combining beliefs. Consider the phenomenon of
information cascades, first identified by Bikhchandani et al. (1992).
Take a group of agents who almost all have private information that
Nissan stock is better than GM stock. The first agent buys GM stock
based on their (minority) private information. The second agent has
information that Nissan is better, but on the basis of this observed
action updates their belief to think GM is likely better. They also
buy GM stock. The third agent now sees that two peers purchased GM and
likewise updates their beliefs to prefer GM stock. This sets off a
cascade of GM buying among observers who, without social information,
would have bought Nissan. The problem here is a lack of independence
in the "vote"--each individual is influenced by the
beliefs and actions of the previous individuals in a way that obscures
the presence of private information. In updating on the credences of
others, we thus may need to be careful to take into account that they
might already have updated on the credences of others.


### 4.2 The Credit Economy



Let us now turn to three paradigms in formal social epistemology: the
credit economy, network models, and models of epistemic diversity.



A key realization, due initially to sociologist of science Robert
Merton, is that scientists often seek *credit*--a proxy
for recognition and approbation of one's scientific work, along
with all the attendant benefits (Merton 1973). Credit economy models
draw on game and decision theory to model scientists as rational
credit seekers, and then assess the epistemic impacts of the credit
incentives scientists face. At the heart of much of this work is a
debate going back as far as Du Bois (1898) that asks: what is the best
motive for an epistemic community? Is it credit seeking or
"pure" truth seeking? Or some combination of the two?



Philip Kitcher's 1990 paper "The Division of Cognitive
Labor" argues that scientists divide labor more effectively when
they are motivated by credit. Truth seekers might all herd onto the
most promising problem in science, while credit seekers will choose
less popular topics where they are more likely to be the one
generating a finding. Strevens (2003) extends Kitcher's work by
arguing that an existing feature of credit incentives, the priority
rule, can lead to an even better division of labor. This is the rule
which stipulates that credit will be allocated only to the scientist
who first makes a discovery. Zollman (2018) critiques both of these
models, though, by pointing out that pure truth seekers should be
happy if anyone makes new discoveries (while Kitcher and Strevens
assume that "truth seekers" are only motivated to find
truth themselves). If scientists do not care who makes a discovery,
then credit is not needed to motivate division of labor.



Others point out that the priority rule has its downsides. Higginson
and Munafo (2016) and Romero (2017) argue that the priority rule
strongly disincentivizes scientists from performing replications
because credit is so strongly associated with new, positive findings.
Replications are often crucial in determining whether new results are
accurate. Another worry is that the priority rule incentivizes fraud
and/or sloppy work by those who want to quickly claim credit (Merton
1973, Casadevall and Fang 2012). Both Zollman (2022--see Other
Internet Resources) and Heesen (2021) show how fraud can positively
impact a scientist's credit in light of the priority rule. And
Higginson and Munafo (2016) and Heesen (2018) show that on the
realistic assumption that speed of production trades off with quality,
the priority rule incentivizes fast, poor, sloppy science. Bright
(2017a), though, uses a model to point out that credit-seekers who
fear retaliation may publish more accurate results than truth-seekers
who are convinced of some fact, despite their experimental results to
the contrary. In other words, a true believer may be just as
incentivized to commit fraud as someone who simply seeks approval from
their community.



There is one more worry about the priority rule, which regards unfair
scientific rewards and their consequences. Merton (1968) described the
"Matthew Effect," that pre-eminent scholars often get more
credit for work than less famous ones. Strevens (2006) argues that
this follows the scientific norm to reward credit based on the benefit
a discovery yields to science and society. Because famous scientists
are more trusted, their discoveries do more good. Heesen (2017), on
the other hand, uses a credit economy model to show how someone who
gets credit early on due to luck may later accrue more and more credit
because of the Matthew effect. When this kind of compounding luck
happens, he argues, the resulting stratification of credit in a
scientific community does not improve inquiry. Rubin and Schneider
(2021) and Rubin (2022) add to these worries with network models
showing how older, more connected member of the community will tend to
get unfair credit in cases of multiple discovery as a result of the
dynamics of information sharing. As they argue, these dynamics will
often unfairly advantage dominant social groups who tend to be more
established and connected in science.



On the positive side, besides possible benefits to division of labor,
the priority rule incentivizes sharing in science. The communist norm
states that scientists will share work promptly and widely, which
benefits scientists because they want to establish priority and
receive credit. While models show how scientists can be incentivized
to hide intermediate research to get later credit (Dasgupta and David
1994), they also show how enough credit can promote communism
(Banerjee et al. 2014, Heesen 2017b).



This debate is further complicated by models that look at how credit
influences not just rational decision making, but selective processes
in science. Credit can impact who remains in a discipline, whose
practices and ideas become influential, and whose students get jobs.
Smaldino and McElreath (2016) show how poor methods (like low study
power) tend to generate false positives and thus credit for those
using them. If investigators using these poor methods train their
students into them, and then place those students disproportionately,
they will tend to proliferate. Likewise Tiokhin et al. (2021) show how
selection can drive the proliferation of fast work (using small
samples sizes) and O'Connor (2019) shows how it can lead to
conservative, safe problem choice in science. Others, like Smaldino et
al. (2019) and Stewart and Plotkin (2021) consider what conditions
might promote the selection of good science in light of selection.



As we have seen, credit economy models help answer questions like:
what is the best credit structure for an epistemic community? And how
do we promote true discoveries via incentive systems? As credit
economy models show us, designing good epistemic communities is by no
means a trivial task.


### 4.3 Epistemic Networks



Another paradigm, widely used by philosophers to explore social
aspects of epistemology, are epistemic network models. This kind of
model uses networks to explicitly represent social or informational
ties where beliefs, evidence, and testimony can be shared.



There are different ways to do this. In the social sciences generally,
a popular approach takes a "diffusion" or
"contagion" view of beliefs. A belief or idea is
transmitted from individual to individual across their network
connections, much like a virus can be transmitted (Rogers 1962). (See
Lacroix et al 2021 for a use of this sort of model in philosophy.)
Alternatively, agents can start with credences and in successive
rounds average those credences with their neighbors until reaching a
steady state (Golub and Jackson 2010, 2012).



In these diffusion/contagion models, though, the individuals do not
gather evidence from the world, share evidence with each other, or
form beliefs in any sort of rational way. For this reason,
philosophers of science have tended to use the *network
epistemology framework* introduced by economists Bala and Goyal
(1998) to model how more rational individuals learn from neighbors.
These models start with a collection of agents on a network, who
choose from some set of actions or action guiding theories. Agents
have beliefs about which action is best, and change these beliefs in
light of the evidence they gather from their actions. In addition,
they also update on evidence gathered by neighbors in the network,
typically using some version of Bayes' rule. It is in this sense
that agents are part of an epistemic community. Figure 1 shows what
this might look like. The numbers next to each agent represent their
degree of belief in some proposition like "vaccines are
safe." The black agents think this is more likely than not. As
this model progresses these agents gather data, which increases their
neighbors' degrees of belief in turn.



![a diagram: link to extended description below](fig2.png)

Figure 1: Agents in a network
epistemology model use their credences to guide theory testing. Their
results change their credences, and those of their neighbors. [An
 extended description of figure 1
 is in the supplement.]




Communities in this model can develop beliefs that the better theory
(vaccines are safe) is indeed better, or else they can pre-emptively
settle on the worse theory (vaccines cause autism) as a result of
misleading evidence. Generally, since networks of agents are sensitive
to the evidence they gather, they are more likely to figure out the
"truth" of which is best (Zollman 2013; Rosenstock et al.
2017).



Zollman (2007, 2010) describes what has now been dubbed the
"Zollman effect" in these models; the surprising
observation that it is generically worse for communities to
communicate more (see also, Grim 2009). In tightly connected networks,
misleading evidence is widely shared, and may cause the community to
pre-emptively settle on a poor theory. Others find similar results for
diverse features of networks that slow consensus and promote diversity
of investigation, including irrational stubbornness (Zollman 2010,
Frey and Seselja 2020, Gabriel and O'Connor 2023), using grant
giving strategies to promote diversity (Kummerfeld and Zollman 2020,
Wu and O'Connor 2022), and demographic diversity (Wu 2022,
Fazelpour and Steel 2022).



On the basis of results like these Mayo-Wilson et al. (2011, 2013)
defend the "independence thesis"--that rational
groups may be composed of irrational individuals, and rational
individuals may constitute irrational groups. This supports central
claims from social epistemology espoused by Goldman (1999). Smart
(2018) calls one direction of this claim--that sometimes
individual cognitive vices can improve group performance--
"Mandevillian intelligence."



Others have considered how else cognitive biases might impact the
development of consensus in these models. Weatherall and
O'Connor (2018) and Mohseni and Williams (2019) show how
conformity can prevent the adoption of successful beliefs, or slow
this adoption, because agents who conform to their neighbors are often
unwilling to pass on good information that goes against the grain.
Both Olsson (2013) and O'Connor and Weatherall (2018) consider
network models where actors instead place less trust in the evidence
(or testimony) of those who do not share their beliefs. This can lead
to stable, polarized camps that each ignore evidence and testimony
coming from the other camp.



These latter models relate to other work attempting to show how
polarization might arise in epistemic communities not from biases, but
from more rational forms of updating. Singer et al. (2019) show how
agents who exchange reasons for beliefs, but reject reasons that do
not cohere with their beliefs, can polarize. Jern et al. (2014) show
how actors who hold causally or probabilistically related beliefs can
polarize in light of the same new evidence. This observation is
extended and explored by Freeborn (2023a) and Freeborn (2023b) who
considers how networks of agents who share information and hold
multiple beliefs can polarize and factionalize. Dorst (2023) provides
a model where mostly rational agents can polarize in response to the
same evidence, and in ways that are predictable.



Other literature investigates the role of pernicious influencers,
especially from industry, on epistemic communities. Holman and Bruner
(2015) develop a network model where one agent shares only fraudulent
evidence meant to support an inferior theory. As they show, this agent
can keep a network from reaching successful consensus by muddying the
water with misleading data. Holman and Bruner (2017) show how industry
can shape the output of a community through "industrial
selection"--funding only agents whose methods bias them
towards preferred findings. Weatherall et al. (2020) and Lewandowsky
et al. (2019) show how a propagandist can mislead public agents simply
by sharing a biased sample of the real results produced in an
epistemic network. Together these papers give insight into how
strategies that do not involve fraud can shape scientific research and
mislead the public.



One truth about epistemic communities is that relationships matter.
These are the ties that ground testimony, disagreement, and trust.
Epistemic network models allow philosophers to explore processes of
influence in social networks, yield insights into why social ties
matter to the way communities form beliefs, and think about how to
create better knowledge systems.


### 4.4 Modeling Diversity in Epistemic Communities



Diversity has emerged several times in our discussion of formal social
epistemology. Credit incentives can encourage scientists to choose a
diversity of problems. In network models, a transient diversity of
beliefs is necessary for good inquiry. Let us now turn to models that
tackle the influence of diversity more explicitly. It has been
suggested that cognitive diversity benefits epistemic communities
because a group where members start with different assumptions, use
different methodologies, or reason in different ways may be more
likely to find truth.



Weisberg and Muldoon (2009) introduce a model where actors investigate
an "epistemic landscape"--a grid where each section
represents a problem in science, of varying epistemic importance.
Figure 2 shows an example of such a landscape. Learners are randomly
scattered on the landscape, and follow search rules that are sensitive
to this importance. Investigators can then ask: how well did these
learners do? Did they fully search the landscape? Did they find the
peaks? And: do communities with diverse search strategies outperform
communities with uniform ones?



![3-d depiction of a surface with multiple peaks and valleys](fig3.png)

Figure 2: An epistemic landscape.
Location represents problem choice, and height represents epistemic
significance.




Weisberg and Muldoon argue that a combination of
"followers" (who work on problems similar to other
individuals) and "mavericks" (who prefer to explore new
terrain) do better than either group alone; i.e., there is a benefit
to cognitive diversity. Their modeling choices and main result have
been convincingly criticized (Alexander et al. 2015; Thoma 2015;
Poyhonen 2017; Fernandez Pinto and Fernandez Pinto
2018), but the framework has been co-opted by other philosophers to
useful ends. Thoma (2015) and Poyhonen (2017), for instance, show
that in modified versions of the model, cognitive diversity indeed
provides the sort of benefit Weisberg and Muldoon hypothesize.



Hong and Page (2004) (and following work) use a simple model to derive
their famous "Diversity Trumps Ability" result. Agents
face a simple epistemic landscape -- a ring with some number of
locations on it, each associated with a number representing its
goodness as a solution. An agent is represented as a finite set of
integers, such as <3, 7, 10> . Such an agent is placed on the
ring, and can move to locations 3, 7, and 10 spots ahead of their
current position, assuming it improves their position. The central
result is that randomly selected groups of agents who tackle the task
together tend to outperform groups created of top performers. This is
because the top performers have similar integers, and thus gain
relatively little from group membership, whereas random agents have a
greater variety of integers. This result has been widely cited, though
there have been criticisms of the model either as insufficient to show
something so complicated, as lacking crucial representational
features, or as failing to show what it claims (Thompson 2014; Singer
2019).



To this point we have addressed cognitive diversity. But we might also
be interested in diversity of social identity in epistemic
communities. Social diversity is an important source of cognitive
diversity, and for this reason can benefit the functioning of
epistemic groups. For instance, different life histories and
experiences may lead individuals to hold different assumptions and
tackle different research programs (Haraway 1989; Longino 1990;
Harding 1991; Hong and Page 2004). If so, then we may want to know:
why are some groups of people often excluded from epistemic
communities like those in academia? And what might we do about
this?



In recent work, scholars have used models of bargaining to represent
academic collaboration. They have shown 1) how the emergence of
bargaining norms across social identity groups can lead to
discrimination with respect to credit sharing in collaboration (Bruner
and O'Connor 2017; O'Connor and Bruner 2019) and 2) why
this may lead some groups to avoid academia, or else cluster in
certain subfields (Rubin and O'Connor 2018). In addition, in the
credit-economy tradition, Bright (2017b) and Hengel (2022) provide
models showing how women may be less productive if they reasonably
expect more stringent criticism of their work, and react
rationally.



As we have seen in this section, models can help explain how and when
cognitive diversity might matter to the production of knowledge by a
community. They can also tell us something about why epistemic
communities often, nonetheless, fail to be diverse with respect to
social identity.


## 5. Social Epistemology and Society



Let us now move on to see how topics from social epistemology
intersect with important questions about the proper functioning of
democratic societies, and questions about the dysfunctions in the
social practices bound up in our quest for knowledge.


### 5.1 The Social Epistemology of Democracies



A good deal of social epistemology focuses on topics in political
epistemology.



A large portion of this work is devoted to evaluating the epistemic
properties of democratic institutions and practices, falling within
what Alvin Goldman (2010) labeled "systems-oriented"
social epistemology. By a "system" he meant some entity
with various working components and multiple goals. System-oriented
social epistemology asks how best to design systems whose goals
include epistemic goods such as the production or distribution of
knowledge or true belief. A number of theorists have pursued this
research programme, examining various institutions in democratic
political systems (see e.g. Zollman (2015), Fallis and Matheson
(2019), O'Connor and Weatherall (2019), Miller (2020), and
Frost-Arnold (2021).)



The systems-oriented research programme in social epistemology has
also been brought to bear on more general questions regarding
democratic politics. Elizabeth Anderson (2006), for example, addresses
how the epistemic properties of democratic systems can be designed to
attain the best possible form of democracy. She provides three
epistemic models of democracy: the Condorcet Jury Theorem, the
Diversity Trumps Ability result, and John Dewey's
experimentalism. Anderson herself plumps for Dewey's
experimentalist approach, while several others argue for voting
aggregation and the Condorcet Jury Theorem (List and Goodin 2001;
Landemore 2011), and Singer (2019) defends a version of diversity
trumps ability (albeit in connection with scientific teams). By
contrast, Claudio Lopez-Guerra (2010), Helene Landemore
(2013) and Alex Guerrero (2014) defend a lottery system for selecting
political representatives, contending that the policies that would
result would be better than those arrived at through voting. (Estlund
2008 rejects the core idea of systems-oriented social
epistemology-- that the epistemic goodness of democratic politics
is to be sought in the quality of its outcomes--arguing instead
that it should be sought in the legitimacy of its procedures.)



Politically-oriented social epistemologists have also written
extensively on the epistemic properties of public deliberation. Since
John Stuart Mill's *On Liberty*, there has been a long
history of attempts to argue for free speech rights on epistemic
grounds. This discussion has continued, albeit with many recent
authors casting a more skeptical eye. Some, recognizing the pitfalls
of deliberation under conditions of oppression, continue to defend the
epistemic potential of deliberation to illuminate social problems
(Young 2000; Anderson 2010).



In addition, there is a lively discussion about the role of experts in
democratic politics. One question concerns how non-experts are to
identify experts (Goldman 2001 is the *locus classicus*).
Another question concerns how to balance reliance on experts with
democracy's commitment to deliberation and equality (see e.g.
Kitcher 2011). There is also a question regarding how to square the
democratic legitimacy conferred by public deliberation among equals
with the distinctive epistemic authority of experts (see e.g.
Christiano 2012).



Two excellent recent handbooks on political epistemology are Hannon
and De Ridder (2021) and Edenberg and Hannon (2021). (In addition to
the topics listed above, these handbooks also delve into such social
epistemology topics as political disagreement, polarization, and the
epistemic responsibilities of citizenship.)


### 5.2 Misleading Online Content



The latest challenge confronting the informational state of the public
is the accelerating spread of misleading content on the internet. Over
the last decade it has become increasingly clear that such content is
widespread, pernicious, and threatening democratic function.
Philosophers have contributed to emerging research on internet
epistemology in a number of ways. 



One question, relevant to thinking about preventing the harms of
misleading content, is how to define and categorize such content. A
typical distinction disambiguates misinformation and disinformation,
where the former is false or inaccurate content not intended to
mislead and the latter is intended to mislead (Fallis 2016, Floridi
2013). It is increasingly recognized, though, that misleading content
need not be false (Fallis 2015, O'Connor and Weatherall 2019),
leading some to define malinformation which is intended to mislead but
potentially true or accurate (Wardle and Derakhshan 2017). Others have
challenged the idea that disinformation needs to be misleading, as
opposed to producing ignorance (Simion 2023) or otherwise blocking
successful action (Harris 2023). Given the variety and complexity in
misleading online content, some argue that these terms will always be
imprecise (Weatherall and O'Connor 2019) or that we should
carefully describe relevant epistemic failures in any case
(Habgood-Coote 2019). 



A number of researchers have turned to virtue epistemology to think
about epistemic failures related to the internet. This work has
identified socially-oriented vices that might increase susceptibility
to misleading content (and increase its sharing), such as Cassam
(2018)'s epistemic insouciance, which he describes as a careless
attitude towards expertise, especially when communicating with others.
Lynch (2018) argues that epistemic arrogance, which involves an
unwillingness to learn from others, undermines the process of public
debate. Meyer et al. (2021), in empirical work, found an association
between high scores on an epistemic vice scale and false belief. And
Priest (2021) worries about the role of epistemic vices among elites,
such as obstructionism (using overly complex language and theory), and
how these vices impact public belief. On the other side, authors like
Porter et al. (2022) and Koetke et al. (2022) argue for the benefits
of intellectual humility - an awareness of one's own
limitations and openness to the possibility of being wrong - in
communities grappling with internet misinformation.



Social media connections and algorithms determine what content is seen
to what degree and by whom. There have been concerns that various
aspects of this process may exacerbate false beliefs. Echo chambers,
where individuals select online connections and spaces that
continually "echo" their own beliefs back to them, may
lead to polarization and prevent disconfirmation of false beliefs
(Cinelli et al. 2021). Nguyen (2020) gives a more specific analysis of
echo chambers as actively discrediting of those with different
beliefs, and disambiguates these from epistemic bubbles where there is
selective exposure to confirmatory content without the discrediting of
outsiders. (This analysis is also in line with the empirical work done
by Ruiz and Nilson (2023).) Both sorts of effects are worrying.
Exacerbating this are tendencies by algorithms to present users with
data and opinions that confirm their beliefs and attitudes, because
that is precisely the content that users tend to like. This is
sometimes called the "filter bubble" or "information
bubble" effect (though these various phenomena are by no means
clearly delineated) (Pariser 2011, Kitchens et al. 2020). One response
might be that platforms should make structural and algorithmic choices
that best promote accurate beliefs among users, but it has been widely
acknowledged that this goes against platform incentives to increase
engagement. Another difficulty is that as platforms shape algorithms
to prevent the spread of disinformation, the producers of
disinformation are incentivized to adapt and create new forms of
misleading content (O'Connor and Weatherall 2019).



An ill-informed populace may not be able to effectively represent
their interests in a democratic society. In order to protect
democratic functioning, it will be necessary for those fighting online
misinformation to keep adapting with the best tools and theory
available to them. This includes understanding social aspects of
knowledge and belief formation. In other words, social epistemology
has much to say to those faced with the challenging task of protecting
democracy from misleading content.


### 5.3 Socio-Epistemic Dysfunctions



Influenced by long-standing work in feminism and critical race theory,
social epistemology has attempted to theorize about various types of
dysfunction in the social practices through which we aim to generate,
communicate, assess, and preserve knowledge. In this subsection we
highlight several of these.



In one of the most influential works in epistemology in the last two
decades, Miranda Fricker (2007) introduced the term "epistemic
injustice" to designate the sort of injustice which wrongs a
subject in their capacity as a knower. Fricker distinguished two
kinds. "Testimonial" injustice obtains when (on the basis
of identity-based prejudice) an audience gives less credence to a
speaker than she deserves. (Fricker illustrated this sort of injustice
with Tom Robinson, a character in *To Kill a Mockingbird* whose
testimony, as a Black man on trial for raping a white woman, was
prejudicially rejected by the all-white jury.)
"Hermeneutical" injustice obtains when, owing to social
forces which reflect the interests of certain social groups, a subject
lacks the concepts for understanding and/or communicating socially
significant aspects of her own experience. (Fricker's example is
the experience of women before the term "sexual
harassment" was coined.) Fricker's (2007) reflections on
epistemic injustice have inspired a generation of social philosophers
to pursue questions in this vicinity. Some have sought to amend or
qualify Fricker's definitions (Medina 2011, Mason 2011, Anderson
2012, Davis 2016, Lackey 2018, Maitra 2018), while others have
employed one or another notion of epistemic injustice in new domains,
including social or political contexts (Medina 2012, Dular 2021),
health care (Carel and Kidd 2014), education (Kotzee 2017), and
criminal law (Lackey 2023).



A second type of socio-epistemic dysfunction of significant interest
to social epistemologists is ignorance. Influenced by the seminal work
of Sandra Harding (1991), Michelle Moody-Adams (1994), Charles Mills
(1997, 2007), Patricia Hill Collins (2000), Nancy Tuana (2004, 2006),
Kristie Dotson (2011), and Gaile Pohlhaus (2012), among others, social
epistemologists have begun to characterize how
ignorance--understood as involving either false belief or lack of
information--is distributed, and sometimes willfully maintained,
in communities. The guiding hypothesis, explicitly formulated by Mills
(2007) in connection with his notion of "white ignorance,"
is that ignorance in contemporary society patterns in ways that
reflect the interests of dominant social groups. (Because Mills,
Dotson, and others have argued that this sort of ignorance can be
willfully maintained through certain social arrangements, it can be
somewhat misleading to label this a "dysfunction.")
Interesting work has been done in the "epistemology of
ignorance" in connection with women's health (Tuana 2004),
matters of race (Sullivan and Tuana 2007), gender oppression (Gilson
2011), and trust in social media (Frost-Arnold (2016)), among other
areas. This work makes clear that the ignorant subject often *lacks
evidence she ought to have*. As such, it challenges the
traditional idea that the focus of epistemic assessment should be
restricted to how well a subject does with the evidence she has. For
this reason, the acquisition and handling of evidence, long a topic in
feminist and virtue epistemology, has recently begun to attract the
attention of social epistemologists as well. (For discussion see
Goldberg 2017, Lackey 2020, Simion 2021, Woodard and Flores 2023).



To be sure, there are many other types of dysfunction that are
discussed by social epistemologists. We have already mentioned several
of these above: misleading online content, polarization, bias, and
echo chambers. Beyond these, social epistemologists have been
developing concepts for additional types. A general framework for
understanding various dimensions of epistemic oppression can be found
in Dotson (2014). Regarding additional types themselves, Abramson
(2014) and McKinnon (2017) treat gaslighting as a socio-epistemic
dysfunction in which one person consistently questions another's
sanity or competence in order to destroy the victim's
self-confidence and undermine her sense of self (Ruiz 2020
develops the cultural analogue of this phenomenon); Stanley (2015)
presents a wide-ranging discussion of propaganda, including its
socio-epistemic dimensions; Berenstain (2016) introduces the notion of
epistemic exploitation, the phenomenon in which members of
underrepresented groups are burdened by the expectation of informing
dominant group members about their experiences; Davis (2018) develops
the notion of "epistemic appropriation," the epistemic
analogue of cultural appropriation; Ballantyne (2019) discusses
"epistemic trespassing," wherein experts assume authority
and speak on topics beyond their expertise; and Leydon-Hardy (2021)
identifies "epistemic infringement" as the phenomenon in
which one person undermines the epistemic agency of another by
violating social and epistemic norms.