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Critical Thinking Education and Debiasing



There are empirical grounds to doubt the effectiveness of a common and intuitive approach to teaching debiasing strategies in critical thinking courses. We summarize some of the grounds before suggesting a broader taxonomy of debiasing strategies. This four-level taxonomy enables a useful diagnosis of biasing factors and situations, and illuminates more strategies for more effective bias mitigation located in the shaping of situational factors and reasoning infrastructure-sometimes called "nudges" in the literature. The question, we contend, then becomes how best to teach the construction and use of such infrastructures.
Critical Thinking Education and
University of Waterloo
Department of Philosophy
200 University Avenue West
Waterloo, ON
N2L 3G1
Yale University
Department of Philosophy
344 College St.
New Haven, CT. 06511-6629
Abstract: There are empirical
grounds to doubt the effectiveness of
a common and intuitive approach to
teaching debiasing strategies in
critical thinking courses. We
summarize some of the grounds
before suggesting a broader
taxonomy of debiasing strategies.
This four-level taxonomy enables a
useful diagnosis of biasing factors
and situations, and illuminates more
strategies for more effective bias
mitigation located in the shaping of
situational factors and reasoning
infrastructuresometimes called
“nudges” in the literature. The
question, we contend, then becomes
how best to teach the construction
and use of such infrastructures.
sumé: Des données empiriques
nous permettent de douter de
l'efficacité d'une approche commune
et intuitive pour enseigner des
stratégies de correction de biais
cognitifs dans les cours de pensée
critique. Nous résumons certains de
ces résultats empiriques avant de
suggérer une taxonomie plus
étendue de ces stratégies de
correction de biais. Cette taxonomie
à quatre niveaux permet un
diagnostic utile de facteurs causant
les biais et elle met en évidence
davantage de stratégies permettant la
correction plus efficace de biais,
stratégies situées dans des mesures
modifiant les infrastructures et les
environnements cognitifs ("nudge"
dans la littérature). Nous soutenons
que la question porte dès lors sur les
meilleures façons d'enseigner la
construction et l'utilisation de ces
Keywords: Critical thinking, biases, debiasing, education, nudges
1. Introduction
Teaching critical thinking is an undertaking that permits
emphasis on many different combinations of elements, the most
traditional of which are formal logic, informal logic,
argumentation, fallacy theory, and rhetoric. Increasingly, too,
critical thinking courses and texts include an explicit emphasis
on the psychology of cognitive and social biases (see, for
example, Kenyon 2008; Ruggiero 2004; Groarke & Tindale
2004; and Gilovich 1991). While they vary greatly in the length
and detail of their treatments, a common feature of these texts is
that they present names, taxonomies and definitions of some key
biases, perhaps with some examples or explanations of the
underlying empirical work included. Given their role in critical
thinking didactics, it is safe to assume that these treatments are
intended to foster practical reasoning skills of mitigating or
forestalling the effects of biases to enable students to identify
biases in reasoning, and to minimize biases in their own
The overall aim is consonant with the general rationale for
teaching critical reasoning courses in the first place. Yet these
texts also commonly lack empirically-informed material,
distinct from that already mentioned, that aims to teach students
the skills of minimizing bias in their thinking or their actions. In
other words, the combination of what such treatments do and do
not contain reflects the assumption that simply teaching students
about biases is an effective way of enabling them to reduce the
distortions of biases in their own thinking. We identify this
assumption as the intuitive approach to teaching debiasing, or
(IA) Teaching facts about biases, including a taxonomy
of biases and their various propensities to distort
reasoning, is a reasonably effective means of providing
students in critical reasoning courses with skills enabling
the detection and mitigation of biases, including students’
own biases.
Something along the lines of IA informs the treatments that
biases receive in the critical thinking texts already noted. It is
also central to some reviews on the topic (e.g., Larrick 2004),
while its influence can be seen also in training contexts beyond
that of a critical thinking course. An example of this latter type
of context is Croskerry, Singhal, & Mamede’s (2013a, 2013b)
approach to cognitive debiasing for clinicians’ medical
judgments: even though Croskerry et al. record a “general
pessimism […] about the feasibility of cognitive debiasing
(Croskerry et al. 2013a, p. ii63), they adopt the recommendation
that clinicians “must be informed and recognise the need for
constant vigilance and surveillance of their [own] thinking to
mitigate diagnostic and other clinical errors” (Croskerry et al.
2013b, p. 6).1
It is noteworthy that IA characterizes much of how critical
thinking education treats debiasing, we contend, because when
one considers the empirical evidence bearing on it, the most
plausible simple view of IA is that it is—at least in most cases—
false.2 At least, the practice of simply teaching students facts
about biases is not as effective as one might hope. The literature
on the cognitive and social psychology of debiasing indicates,
on balance, that teaching people about biases does not reliably
debias them. Indeed, the literature suggests that (for at least a
wide class of biases) practically any debiasing strategy intended
to be learned and subsequently self-deployed by individuals,
acting alone and at the point of making a judgment, is unlikely
to succeed in significantly minimizing biases.
In the following remarks, we briefly outline why this is
so before moving on to consider the ramifications for critical
thinking education. Vast resources are currently devoted to
teaching critical reasoning worldwide. Does the implausibility
of IA mean that these resources are misused, to the extent that
they are predicated on IA? Should philosophers, psychologists,
and other critical reasoning educators just stop including a focus
on biases in critical thinking education?
We do not think so. Rather, we take the lesson to be that
whole societies and polities have a major interest in promoting
efficacious debiasing education—extending to population-level
demographic scales and intergenerational time scales. The
difficulty of teaching debiasing skills that could be deployed in a
strictly atomistic or individualistic way counts in favor of
teaching and investing also in more collective debiasing
strategies and infrastructure that would serve the latter sorts of
interests. This approach will encompass teaching not just
individual skills and knowledge, but skills that enable the
1 In fact the distinctions between approaches to critical thinking that we
propose in the following remarks should be helpful in characterizing the
kinds of clinical training strategies described by Croskerry et al. (2013a,
2 Below, we identify some methods that would fall under IA that we believe
to be relatively promising.
construction of reasoning infrastructure, and effective
participation in social and organizational reasoning processes
and decision procedures.
What would these processes, strategies and infrastructure
look like? A key first step here is to reflect on the breadth of
what can count as debiasing from a critical thinking perspective.
Our aim in this reflection is to help motivate and set the stage
for creative and empirically guided work on how to teach
debiasing in ways that might be efficacious, serving both private
and public interests in minimizing distorted or unreliable
reasoning. By focusing on choices, behavior, and agent-world
interactions, we suggest a broader range of outcomes for critical
thinking than that informing IA, and therefore a broader range
of options for critical thinking education as well.
Those familiar with the critical thinking literature can
skip section 2, in which we develop and justify our
characterization of IA. In section 3, we present some reasons for
pessimism towards IA. Finally, in sections 4 and 5, we introduce
our positive proposal by first distinguishing different ways in
which we can debias and then discussing how this impacts the
way we conceive critical thinking education.
2. Characterizing the intuitive approach
First, an explanation and a caveat. By ‘bias’ we most generally
mean something neutral with respect to both moral properties
and questions of accuracy. A bias in this sense is simply a
disposition, implicit or explicit, to reach a particular kind of
conclusion or outcome, or to remain in one. This interpretation,
common in the psychological literature, accommodates the idea
that biases can skew a process in a way that makes its outcome
inaccurate or otherwise wrong, but it also leaves open the
prospect that biases play a role in truth-conducive reasoning
processes and morally unproblematic judgments or attitudes. In
common parlance, of course, one normally goes to the trouble of
saying that some attitude, reasoning, or person is biased only if
the operation of the bias is claimed to be problematic—a
distortion, or a prejudice that amounts to a vice. Our focus on
debiasing is one that presumes the former meaning: it is
predicated on the thought that biases should be mitigated when
they are problematic, and not because they are by definition
problematic (e.g., Klein & Kahneman 2009 explore when and
how heuristics and their associated biases can help us get things
right). Not everything that is a bias needs to be debiased; only
biases manifesting in a problematic manner or degree.
The caveat is that the empirical literature on biases and on
debiasing is massive and varied; even to summarize it
comprehensively would be impossible for a single paper. The
number of biases to consider is moreover increasing as
psychologists discover or propose new ones.3 We will use just a
few results we believe to be representative to illustrate the
grounds for thinking that teaching students about biases, and
warning them to be on the lookout for biases, is unlikely to
significantly reduce the generation of distorted judgments “in
the wild,” or to increase the likelihood that biased judgments
will be recognized and remedied by the agent herself. Because
we hope to spend some time on the implications of this fact for
critical thinking education, we are compelled to move through
the empirical evidence rather briskly. So our subsequent
reflections will have to remain conditional, not just on the
probity of the defeasible empirical literature, but on the accuracy
of our depiction of that literature.4
The most general problem to emphasize about IA is
illustrated in Baruch Fischhoff’s (1982) influential work on
mitigating the hindsight bias. Hindsight bias is the tendency to
regard actual outcomes as inevitable outcomes, in retrospect,
and to overestimate the extent to which one had antecedently
expected the actual outcome. Fischhoff points out that a number
of approaches to debiasing subjects for hindsight effects simply
do not work very well under a wide range of experimental
conditions (1982, pp. 427-431). These approaches, falling under
IA, include:
Explicitly explaining the bias to subjects, and asking
them to avoid it in their own reasoning;
Inducing subjects to value the accuracy of their
Encouraging subjects to think first in terms of
diagnosing other subjects’ biased reasoning, before
turning to the question of their own reasoning.
These strategies (and others that Fischhoff describes) are
motivated by some quite natural assumptions about the nature of
learning, of cognition, and of error—the most basic one being
the idea behind IA, that forewarned is forearmed. But the
3 Stanovich reviews this expanding literature and offers a useful taxonomy
with relatively few categories (2009, p. 182; 2011, p. 230-243).
4 A somewhat more detailed examination of this evidence is provided in
Kenyon (2014).
ineffectiveness of these strategies at mitigating hindsight bias,
and many other biases, has been quite strongly confirmed by
subsequent psychological work (Wilson et al. 2002).
3.!Pessimism about teaching debiasing abilities
Fischhoff’s studies and subsequent ones are, perforce, largely
experimental designs that isolate specific instances of biased
reasoning, rather than longitudinal analyses of learning
outcomes in educational contexts. The latter type of study,
though, tends to be hard to perform rigorously, and hard to
interpret (Willingham 2007, p. 12). Experimental designs
constitute the best evidence we now possess about the
propensity for normally teachable information and skills to
reduce biased reasoning in students, outside the classroom and
in later life. How much confidence to place in the applicability
of these results is a good question; but this is the evidence we
have. On balance, it weighs against the thought that simply
teaching and warning people about biases will successfully
mitigate biased reasoning. IA is not well supported by evidence.
Perhaps the most significant factor explaining why
teaching people about biases does not itself particularly reduce
their biases is known in the literature as bias blind spot (Pronin
and Kugler 2007; Pronin, Lin & Ross 2002).5 Put simply:
knowing that people in general are subject to a particular bias is
consistent with one’s believing that one is not subject to it.
Indeed, more importantly, even knowing that one is generally
susceptible to a bias is consistent with one’s believing, on the
specific occasion one considers the matter, that one is not
displaying a bias. Bias blind spot thereby insulates one’s
judgments, in the event, from the application of whatever
debiasing strategies might actually be effective.
A related explanation for the relative ineffectiveness of
teaching information about biases is that we can easily think that
we have debiased when we have not. A theoretical knowledge
of the need to adjust for bias does not reduce this problem, since
the problem is precisely that one falsely believes oneself to have
addressed that need. Indeed, merely thinking about debiasing
can enable the problem! By thinking over the details of the case
at hand, and considering the prospect of being biased, one may
5 We would encourage the use of another expression that could communicate
this idea just as well, without using the ableist expression blindness to denote
a type of ignorance (cf. Schorr 1999).
simply give one’s biases more raw material to operate on
(Thompson 1995). Here, then, another appealing thought about
clear thinking meets unwelcome data: the idea that one can
debias by firmly thinking it over, that debiasing can be a matter
of having a stern word with oneself about not being biased, is
mistaken (Frantz & Janoff-Bulman 2000). In fact, attempting to
self-debias in this way can even make one’s biases worse (Hirt
and Markman 1995; Sanna, Stocker & Schwartz 2002). Telling
ourselves that we have debiased, we can come to hold our
attitudes and views more strongly—convinced that they have
been vetted for distortion. As Frantz (2006, p. 165) observed,
merely to ask a question like “Am I being fair?” is to provide an
additional opportunity for a bias to operate, accompanied by a
greater conviction that one’s judgment is unbiased.
This conveys a sense of the kinds of evidence speaking
against the idea that we can teach people to be significantly less
biased reasoners simply by teaching and warning them about
biases. But this is not to say that no debiasing strategies have
been shown to work in this literature. A range of strategies work
to varying degrees, depending on the bias, with the single most
effective (and most generally effective) strategy being for the
subject to explicitly consider and entertain a range of alternative
perspectives or counterfactual outcomes, and what would have
had to happen in order for those outcomes to occur (Pronin,
Puccio and Ross 2002; Wilson et al 2002; Anderson & Sechler
1986; Fischhoff 1982). So we do have at least one mitigation
strategy with a significant prospect of success, taken as an
experimental treatment.
The problem is that the strategy is extremely difficult to
implement as a self-deployed skill. Existing biases and
attentional limits can easily make themselves felt as an
unwillingness or inability to generate plausible alternative
scenarios (O’Brien 2009, pp. 329-330); and even a willingness
to do so is no guarantee that the generation and consideration of
alternatives will be sufficiently disciplined or constrained to
actually lead to a less distorted judgment (Tetlock 2005, p. 199).
Absent the sort of facilitation or guidance by assistants that
tends to characterize the experimental contexts in which
“consider the opposite” is an effective strategy, there is little
reason to expect it to be employed with regularity by individual
agents in normal contexts, nor to work well when it is
Roughly and readily, then, there is a seeming dilemma
for those who wish to teach debiasing as part of critical thinking.
The things that are most easily teachable and open to long-term
retention by learners—what biases are and how they work; and
that their distortive influences are to be avoided—are not in
themselves very effective at debiasing people’s judgments;
while the things that are rather effective at debiasing
judgments—counterfactual or opposite-scenario consideration—
are not very teachable as individual skills to be recalled and
applied when needed, nor to be implemented easily even when
attempted. In either case, IA does not deliver.
Again, we do not take this as grounds to doubt that there
is still a rationale for focusing on biases in critical thinking
education. Rather, we believe that the difficulty of teaching
effective debiasing strategies under the assumption of IA is
really an invitation to a broader and more fine-grained
taxonomy of debiasing outcomes than is presupposed in IA.
This larger terrain of debiasing outcomes should in turn create
space for additional strategies to mitigate biases in outcomes so
construed. If teaching debiasing looks too hard in light of the
data just described, it is because IA focuses on doing it at the
least plausible levels: by giving students propositional
knowledge that will enable them to debias, or to debias their
own thinking at the point of bias manifestation. Put differently,
IA requires students to debias in the most cognitively
demanding way.
We propose to alleviate this problem by distinguishing
further levels or domains of debiasing. These levels, we believe,
are partly anticipated in the extant literature; various authors
allude to some of the strategies we will explore below. But there
exists at the moment no taxonomy of the kind we outline here,
illuminating a wider range of overlapping skills and habits that
can more plausibly be taught and implemented, with the aim of
addressing biases at those different levels. We see this as
supplementing existing strategies and, hopefully, as offering
new ways of thinking about the challenges we outlined in this
4. The scope of debiasing
While knowing about a bias is no prophylactic in itself, it may
serve as one of many steps along a path to debiasing (Stanovich
& West 2008; Wilson & Brekke 1994). For example, Wilson &
Brekke’s model lists the awareness of an unwanted process as
the first step in debiasing. Of course, one must also be motivated
to correct the bias, know the direction and the magnitude of this
bias, and be sufficiently in control, with sufficient mental
resources, to be able to adjust the response (Wilson & Brekke
1994, p. 119). Now, we have briefly reviewed grounds to
believe that no individually portable suite of skills seems very
apt to put these cognitive and affective resources to work at the
right times. But what if we did not limit ourselves to the goal of
preventing biased judgments, nor even that of unskewing
judgments after they are made?
We take the problem thus far to be an artefact of the level
at which we have been considering both biases and debiasing
strategies. We submit that the core issues of interest from a
critical thinking perspective are broader—including not simply
what one thinks, but how one acts.6 This opens up the scope of
what will count as a debiasing strategy in the relevant sense. It
holds out the promise that a more variegated conception of bias-
reduction will offer a range of strategies that limit bias at
different levels and in different ways.
In effect, we propose swapping a teaching approach that is
simple in presentation but has little hope of success for an
approach that will certainly be more complex in presentation,
but has a greater chance of bearing fruit. The implausible
approach is IA: teaching information about biases in such a way
that learners will somehow subsequently recall that information,
recognize its situational relevance, and act on it appropriately in
bias-fraught contexts of thought and action. We advocate not
only teaching information about biases, but also teaching and
ingraining the habits, skills and dispositions that facilitate
adopting general reasoning and decision-making principles,
which nudge agents away from biased reasoning and filter its
effects out of their actions.
When we talk of a nudge, we mean the term in the sense
advanced by Thaler & Sunstein (2009). A nudge is a strategy or
an infrastructure put in place in order to minimize or to
eliminate a set of cognitive biases by using aspects of the
environment. Changing the way information is presented to
participants or changing what the default option is are common
examples of nudges. A striking example from Thaler &
Sunstein’s discussion is the way food is displayed in a buffet:
depending on where certain food items are placed, their
“popularity” as a choice can increase by 50%. The idea here is
to pre-emptively construct situations in order to minimize
biases. From an individual agent’s perspective, this presents two
6 Cf. Beaulac & Robert (2011) on critical thinking attitudes. One strategy
they deem particularly promising is that of epistemic caution (prudence
dimensions of action to mitigate bias: exploiting existing nudges
in the environment, and constructing nudges of one’s own—
either individually or collaboratively. Both the ability and the
need to do these things generally are potential learning
outcomes for a critical thinking course, outcomes that are
insufficiently explored in the critical thinking literature at the
moment. We believe our outlook gives better tools to integrate
these ideas in the curriculum.
While a more fine-grained analysis is surely possible, we
will for the sake of brevity limit the current discussion to four
broad levels at which debiasing can be implemented, once it is
taken to span the distinction between thought and action. We
provide both a general description and an example for each
level. It is worthy being clear, however, that this is meant as a
broad characterization of these levels. We recognize that the
divisions between the levels are not razor-sharp; there may be
borderline cases, and complex examples might bridge across
Level 1 debiasing: Owing to general education,
environment (family, neighbourhood, education, etc.),
habits, critical thinking education and training over a
long period, an agent has no disposition to produce a
particular sort of biased judgment; that is, the bias
does not arise. This sort of debiasing process is
implemented during education (mostly on a very long
period) and applies to individual agents’ judgments.
E.g., A hiring committee member does not notice or
attend to racial differences, and shows no bias in
reasoning about the quality of candidates from visible
minority groups in hiring contexts because she grew
up and still lives in a multicultural neighborhood and
has not been markedly influenced by the media
characterization of some groups.
Level 2 debiasing: A biased judgment occurs or is
incipient, but critical thinking education and training
facilitate the agent’s deployment of cognitive or
behavioral strategies that lead to a revision of the
judgment in context. Debiasing of this kind is
implemented within the context of judgment-fixation,
is initiated and mediated by agents’ psychological
processes, and applies to individual agents’
judgments. (E.g., models by Stanovich & West 2008,
Wilson & Brekke 1994)
E.g., A hiring committee member’s first reaction is
to assign an unwarrantedly low rating to a dossier
from a candidate with a name connoting ethnic
minority status. On second thought, though, she
wonders whether she is being biased by the character
of the name, and reflects on the positive features of
the file. Eventually she comes to think of the
candidate in more accurate terms.
Level 3 debiasing: A biased judgment occurs or is
incipient, but critical thinking education and training
(individual or collective) leads (or has led) to the
creation of situational “nudges” that debias the agent’s
judgment in context. This sort of debiasing process is
implemented within the context of judgment-fixation,
is initiated or mediated by environmental cues or
infrastructure, and applies to individual agents’
E.g., A hiring committee is given a preliminary
presentation about the prospects for biased reasoning
in hiring contexts. Notes and other guidelines from
this presentation are kept in the meeting room, in a red
folder on the table around which committee members
sit. Later, a hiring committee member encounters a
dossier from a candidate with a name connoting
ethnic minority status. The visual salience of the red
folder reminds her to attend to the significance of the
candidate’s name. She would otherwise have assigned
an unwarrantedly low rating to the file, but owing to
the earlier presentation she makes a point of reflecting
on the candidate’s positive features, considers how
those features would appear if part of a privileged
candidate’s application, and ranks the file more
Level 4 debiasing: A biased judgment occurs, and is
not significantly remedied, but situational constraints
nevertheless debias the action or outcome. This type
of debiasing process is implemented over time, both
in advance of and during the context of judgment-
fixation. It is initiated or mediated by environmental
cues or infrastructure, and applies to group judgments,
or to actions and outcomes.
E.g., A hiring committee member has an uncorrected
bias of judgment against women in the profession; but
anonymized applications hide candidates’ gender
information, and the committee member ultimately
(unknowingly) votes to hire a superior woman
E.g., A hiring committee member displays
uncorrected biased reasoning in judging that a
superior candidate should not be hired because of her
sexual orientation; but declines to voice this view in
light of the negative responses it would draw from
colleagues, and ultimately votes in favor of the
E.g., A hiring committee member displays
uncorrected biased reasoning in judging that a
superior candidate has an inferior track record, but the
majority vote of the hiring committee favors the
candidate, and she is offered the job anyhow.
The levels represent a way of carving up the gradient from the
most individualist and internalist character-driven approaches,
to the most outcomes-oriented and externally-mediated
approaches. We can characterize Levels 1 and 2 as the more
individualistic levels; they essentially treat the particular agent
as both the source and the focus of debiasing outcomes. Levels 3
and 4 appeal to external, situational factors to a greater extent.
Level 3 debiasing retains a crucial individualistic
component, since the “nudges” or external aids to reasoning that
it postulates are devoted to mitigating biases in the individual
agent. The humble notion of a reminder generalizes this
approach to contexts far beyond that of debiasing, whether in
form of a string tied around one’s finger, or in the government-
mandated installation of seatbelt reminder lights and noises in
motor vehicles. Just like in those more general cases, the main
advantages of a Level 3 approach to debiasing have to do with
reducing the cognitive load on the individual agent faced with a
bias-detection problem. As Stanovich & West (2008) and
Wilson & Brekke (1994) aptly observe, factors bearing on the
detection of the bias are some of the most important reasons
why agents end up following their biased judgments. Level 3
debiasing strategies place this crucial detection stage outside the
agent’s mind, making this strategy cognitively easier than Level
2 strategies are. This makes the detection of the bias more
In Level 4 debiasing, this individual aspect is minimized,
in some cases to the point of being eliminated altogether. This
breadth of degree makes Level 4 a relatively broad and complex
class of debiasing approaches, ranging from those that forestall
or minimize individual biases to those that tolerate the
occurrence of individually manifest biased judgments, but
minimize their significance in determining actions or outcomes.
What distinguishes even those at the former sort end of this
spectrum from the debiasing strategies of Level 3 is that they do
not, at the point of decision-making, operate by debiasing the
judgments of individual agents through the cognitive operations
of those agents.
In the Level 3 example provided, an object in the
environment leads to less distorted reasoning by being noticed,
and by focusing the agent’s attention in a potentially corrective
way. While the first two Level 4 examples are also somewhat
focused on individual agents, they do not involve the use of
facilitated individual cognition and perception to promote
debiased individual judgment. The efficacy of anonymizing
applications in hiring, as in the first Level 4 example, is
manifest in individual judgments, but does not require the agent
to reflect on or notice the anonymization. Conversely, in the
second Level 4 example, the agent who notices and reflects on
the social costs of displaying prejudice during group decision-
making, and elects not to do so, need not be debiased in
judgment in order for the relevant outcome to be debiased. Thus
we propose a gradient of Level 4 strategies, some of them
resembling Level 3 strategies in their scope and orientation, but
with an internal unity and distinctiveness all the same. The
external factors invoked in Level 4 strategies are essentially
oriented towards debiasing decisions, actions, and outcomes—
including group outcomes—without specific reference to the
dispositional properties of any particular agent. 7 Level 4
debiasing will of course still have individualistic overtones
dynamically, since an agent may learn what and how better to
think about an issue by seeing a debiased outcome and process.
Indeed, this may well be a valued feature of such debiasing
efforts and infrastructure over the longer term. But it is not a
defining feature of Level 4 debiasing success.
It is worth noting that some approaches to debiasing
already exist in the psychological literature, having some
overlap with elements of our taxonomy. The particular
granularity of our formulations strike us as more felicitous,
however, and the employment of levels is a key refinement. For
example, Croskerry et al. (2013b) place the strategy of “training
7 Bishop & Trout (2005) discuss the wider ramifications of such strategies for
on theories of reasoning and medical decision making” on a par
with creating “supportive environments” for sound reasoning
(pp. ii67-ii68). This tends to obscure not only the significantly
distinct causal domains associated with these strategies—the
reasoner’s psychology versus the reasoner’s environment—but
also the correspondingly different material, structural and
educational preconditions they require. The latter kinds of
difference are especially critical if one’s aim in both cases is to
impart knowledge and training that will enable the relevant
strategies.8 Teaching theories of reasoning is plausibly a very
different undertaking than teaching the skills of creating and
employing a supportive reasoning environment. Similarly, even
though Larrick (2004) and Soll et al. (forthcoming) distinguish
between modifying the person and modifying the environment,
we suggest here a more fine-grained analysis that can reveal
more alternative strategies for mitigating distorted outcomes.
Our conjecture is that, when it comes to biases, an
approach animated by IA treats Level 1 outcomes as the ideal
(the bias should not come up at all), and strives at least to bring
about Level 2 outcomes (if a bias comes about, the agent can
correct it). We think this is practically impossible; if such
education is ever effective, it is more likely because elements of
the education itself are acting as persistent nudges to create
occasional Level 3 outcomes, while the value of Level 4
outcomes is learned by trial and error, if at all, and is
implemented relatively haphazardly. The impetus to treat Levels
1 and 2 as the real aim of critical thinking education depends,
we think, not on evidence that this is a practical possibility, but
substantially on a deep-seated intuition that critical thinking is
properly implemented only in the minds and choices of specific
The three distinct examples for Level 4 debiasing reflect
both the flexibility of the individuation of actions, and the range
of points at which debiasing action can take place. The first
example proposes an intervention affecting the agent’s judgment
of candidates; with the second, the intervention debiases the
agent’s act of voting; while the third describes a mitigation of
nothing more specific than the committee’s collective hiring
actions. The anonymized hiring protocol, the perception of
social disapproval of prejudice, and the committee voting
8 Croskerry et al. (2013a, 2013b) may well be contemplating a clinical
administration that teaches theories of reasoning while itself directly
implementing supportive infrastructure such as decision check-lists; we are
contemplating how to bring about both kinds of outcome through education.
structure each count as an element of contextual engineering that
effectively debiases the Level 4 scenario, even though all Level
4 cases by definition count as failures of debiasing by the purely
individualistic cognitive standards we originally considered.
Clearly, then, this more fine-grained analysis reveals
more opportunities to debias by clarifying the number of stages
open to intervention in thinking, preparing, deciding and acting.
Variations on the theme are not hard to find, moreover,
including some that span the levels we have sketched. For
example, Uhlmann and Cohen (2005) found that, if the notion of
merit were left undefined for a hiring process, it would tend to
become the vehicle of gender-biased decision making. That is,
merit would be operationalized distinctly from case to case, with
the overall effect of promoting hiring along gender lines—and
particularly the hiring of men over women.9 But eliciting a
commitment to some hallmarks of merit from the evaluators
prior to revealing information about the people being evaluated
reduced this biased “moving goalposts” approach in their
judgments (2005, p. 478). The example provides further
empirical support for the idea that education about the advance
construction and acceptance of such policies and organizational
structures should fall within the core mandate of education for
reaching more appropriately reflective and reliable outcomes in
Arguably this counts as a remedy that straddles the
border between Levels 3 and 4, since the incipient bias is
corrected in judgment, not merely in action or outcome; yet in
practice the mechanisms achieving this outcome will be
thoroughly environmental and causally remote. That is,
somebody has to decide (presumably well in advance, in the
case of policy-making) to set out clear rubrics for merit, and to
ensure a hiring process structured so that evaluators review the
hallmarks of merit before they review the details of applicants.
So not every case of debiasing falls entirely within one such
level; but we do think that this particular way of carving up of
levels helps illuminate relevant features of even those cases
spanning levels.
9 There was also weak evidence that female evaluators would
similarly construct merit in a gender-biased way to devalue male
applicants, if the job were sufficiently stereotypically associated with
women’s gender rolese.g., that of a Women’s Studies professor
(2005, p. 478).
5. Teaching debiasing as teaching acceptance of influences
on cognition and constraints on action
There is, then, a very broad recipe for achieving better odds of
teaching successful debiasing strategies: first broaden the
conception of what counts as debiasing, and then be open to
exploiting the full spectrum of opportunities to mitigate bias,
from antecedent reasoning dispositions to the broadest
conception of an action in context. We close with some
schematic remarks about putting debiasing, so construed, into a
typical critical thinking curriculum.
The approach we suggest becomes more plausibly
effective than the debiasing strategies described earlier when it
motivates us to subject ourselves to nudges, infrastructure and
institutions in advance of the circumstances of bias that will
make those things effective debiasing aids. That is, we hold that
knowledge of biases has the best chance of effectiveness when it
leads one generally to accept and construct nudges or contextual
engineering of one’s own. In that case it supports the adoption
of general debiasing strategies that might simply be encoded in
the lived environment, rather than holding out the hope that one
can learn to debias in a series of contextual one-offs, as the need
Of course, it is also important to note that, at this time,
there are few direct empirical grounds for confidence that
teaching skills and attitudes specifically to promote Level 3 and
Level 4 debiasing in critical thinking courses will be easy or
highly effective. We do not claim to show this; only that it is
worth trying. Perhaps the most certain line of reasoning at our
disposal is probabilistic. Unless the probability of success under
our broader construal of debiasing outcomes is literally zero, the
addition of this slate of options can only improve the chances of
overall success in teaching debiasing skills. How close to zero
that probability could be while yet justifying the effort of the
attempt is a good question that we will not attempt to answer
beyond offering three observations: first, that testing such
payoffs is what pilot projects and exploratory studies are for;
second, that critical thinking education incorporating IA already
consumes many resources when its low chances of debiasing
success are known; and third, that the chances of success, on
our account, are unlikely to be that low. After all, the creation of
and deference to bias-reducing infrastructure is palpably
something that can spread through professional mentorship and
collegial training. For example, instructors demonstrably can
acquire from their peers various debiasing practices such as
anonymizing student work. When these outcomes are the
explicit objects of training, they seem teachable and learnable;
we think this shows that one would need a special reason for
thinking that they are not largely or significantly teachable in
courses that explicitly aim to teach them, rather than a special
reason to think that they are. So we do not claim that our
approach is sure bet to succeed, but we do claim that it is a
reasonable bet, and in any case a better bet than what IA
On our view, an education in debiasing includes an
education in how to administer decision-making contexts and
actions in a manner consonant with Level 3 and Level 4
debiasing. Analogies and antecedents might be found with a
range of cases of training in controlling one’s environment and
actions, rather than merely one’s internal states. For example,
clinical psychologists and psychotherapists refer to the strategy
of controlling one’s environment in order to regulate thoughts
and behaviour as stimulus control. When manifest as a kind of
self-regulation it is a familiar and central element of many forms
of (teachable, learnable) therapies, including Cognitive-
Behavioural Therapy (Karoly 2012, p. 201). Roughly speaking,
rather than trying merely to teach patients suffering from
alcoholism or gambling addictions how to avoid drinking while
at a bar, or how to avoid gambling while at the casino,
mitigation strategies include also teaching the ability to avoid
the bar and the casino in the first place. Choices that determine
one’s environmental stimuli have profound influences on the
sort of thoughts and actions that follow.
Similarly, choices ranging from how to form
committees, how to solicit information, which buttons to push
on the television remote control, and whether to ask about
someone’s personal details during a job interview can all
powerfully influence the opportunities for biases to be reflected
in our actions. It follows that the knowledge (both knowledge-
that and knowledge-how) associated with those activities are
reasonable components of an education in critical thinking. This
knowledge will include skills of creating and maintaining
physical, institutional and social infrastructure that facilitates
more truth-conducive reasoning. But often this infrastructure
already exists when students and former students encounter
contexts of judgment and action; in those cases, the relevant
skill will be that of deferring to such truth-conducive
mechanisms. How to teach this knowledge and these action
principles is a good question. Its feasibility, though, seems far
more promising than the mere hope of IA, that some
combination of knowledge of biases and mental continence will
be both effective and learnable.
It is worth considering a potential objection to the Level
4 style of debiasing education proposed here, proceeding from
an amalgam of epistemological and pedagogical scruples. The
worry is this: whatever the didactic barriers to focusing on
Levels 1, 2 and 3 as debiasing strategies, addressing one’s
teaching to these levels at least promotes the right connection
between methods and outcomes. By teaching students to
recognize bias-inducing situations and to mobilize appropriate
debiasing strategies in context as individuals, one would be
teaching students to make cogent inferences regarding the need
for unbiased or less-biased reasoning. Level 4’s blunt focus on
debiased outcomes does not require anyone in the context to
appreciate the problem, nor why it is a problem, nor how the
debiasing mechanisms will address the problem. For all that a
Level 4 approach tells us, successful debiasing processes can be
entirely arational from the perspective of the agents in the
Thinking back to our examples of Level 4 debiasing,
therefore, one might ask: How can these be critical thinking
strategies, strictly speaking, when they encompass solutions that
do not involve thinking about the problem at all? On this
objection, such an approach to debiasing in critical thinking
education misses something valuable about students’
understanding of the rational connections between reasons and
outcomes something that students in a critical reasoning
course should be taught to entertain, not to elide.
The worry is based on an overly narrow conception both
of the scope of the problem and of the problem-solving context.
Here it may be useful to return to the analogy with addiction
patients who avoid pathological activities by avoiding situations
that lead to those activities. The gambling addict need not avoid
gambling situations solely by reflecting on the evils of
gambling, nor need she choose to do some other activity on the
basis of such reflections at the time of the engaging in the
alternative activity. She might engage in a non-gambling
activity out of sheer habit; but if she originally cultivated that
habit as a means of avoiding gambling, then any particular case
of avoidance by way of that activity reflects her considered
judgment and her autonomy.
The proposal at hand puts forward a similar (minimally
sufficient) connection between education about biases and
students’ subsequent participation, possibly just from habit, in
cognitive and social routines and practices that promote reliable
reasoning. A rational and appropriately agent-endorsed
connection between outcomes and methods is established when
students are educated about the need to form such habits, or to
defer to truth-conducive judgment and action mechanisms.
Subsequently acting on those habits need not itself be an
exercise in reasoning or inference at the point of action in order
to be an exercise of the agent’s commitment to critically
informed reasoning. Indeed, consciously reviewing one’s
reasons at the point of decision-making might even disrupt the
debiasing process. To deny that this represents the exercise of
critical reasoning is to deny, by parity of reasoning, that the
lifelong alcoholic who cultivates a preference for badminton as
an alternative to hanging out in a pub is not demonstrating a
willful continence regarding alcohol when she remains sober for
years on end by spending time at the gym.
Examples of a similar shape are already implemented in
some institutions, with the case of anonymized musical auditions
being particularly telling. Women have long been
underrepresented in orchestras around the world, comprising
fewer than 10% of musicians in major American orchestras
prior to the 1970s and little more than 20% in the 1980s, a much
lower proportion than their availability in the hiring “pipeline”
(Goldin & Rouse 2000). Of the various practices introduced by
orchestras to reduce biases that might account for this
imbalance, the most common means is to ensure the anonymity
of candidates during auditions, by placing the musician behind a
screen where he or she plays for 5 to 10 minutes (Goldin &
Rouse 2000, p. 722). The screen is not used uniformly across
orchestras; only three of the 11 orchestras discussed in by
Goldin and Rouse use it all the way through the process (2000,
p. 723). The effects of the screen, however, are remarkable:
when the screen was used throughout the process, the
probability that a woman would be offered the job was 60%
higher than without it.
The use of the screen is clearly a Level 4 debiasing
strategy in our taxonomy: its success in debiasing the outcome
of the decision process does not require reduction of the
dispositional or occurrent biases of the individual deciders at the
point of evaluating candidates. Yet the prior decision to
implement a general policy of anonymized auditioning is
plausibly driven by just the sort of empirical details about
biases, and commitments to erring on the side of caution, that a
sound critical thinking education may inculcate. This sort of
decision, made well in advance on the basis of general
principles, is not hostage to the need for agents to recognize in
the context of judgment that they are biased. Nevertheless, it is a
touchstone case of a critical thinking strategy that depends
crucially on agents’ thinking about the problem. It just enables
them to think at arm’s length from the situations in which the
bias itself will disrupt their capacities to mitigate it. Teaching
within critical thinking courses how effective such approaches
are will hopefully increase the proportion of students trying to
solve problems by implementing such Level 3 and 4 strategies
within their own (current or future) workplace.
6. Conclusion
There is a familiar learning model associated with propositional
knowledge of the “Paris is the capital city of France” sort. There
is also a familiar set of habits of learning and application that
enables students (and former students) to apply that knowledge
over the longer term of their lives. The problem with IA is that
this sort of knowledge—that biases operate in particular ways,
that they occur in situations like the one at hand, and that one is
susceptible to them in contexts like this—does not reliably issue
in debiasing behaviours at the point of decision or judgment. A
wider view of what counts as successful debiasing indicates a
richer class of ways to apply teachable knowledge to the project
of debiasing.
Our view, then, is that critical thinking education should
include extensive practical guidance on how to structure and
engage with one’s environment to promote good reasoning. This
will include teaching how and why to adopt decision-making
policies and evidence-gathering practices that do not require the
virtuoso ability to rise above invisible and subtle biases. And it
will offer learners the opportunity to practice and experiment
with infrastructure creation and reasonable epistemic deference.
The intended learning outcomes on our model do include
individual learners’ coming explicitly to reason more truth-
conducively in specific cases. But they also include, and place
great emphasis upon, outcomes that are implicit and habitual
from the individual’s perspective, and which have their main
intended effect over the long term and at group levels.
What kind of information, advice, guidance and practice
will critical thinking courses of this sort offer? How are these
things best taught? These, we think, are among the next big
questions in critical thinking education.
Acknowledgements: We would like to acknowledge the helpful
input of Veromi Arsiradam, Frédéric-I. Banville, Gillian Barker,
Frédéric Bouchard, Samantha Brennan, David DeVidi, Carla
Fehr, Christine Logel, Carolyn McLeod, Chris Viger, Audrey
Yap, Frank Zenker, and two anonymous referees for this
journal. Thanks also to Catherine Hundleby and the students in
the 2014 "Fallacies and Bias" seminar at the University of
Windsor. This work was supported in part by the Faculty of
Arts, University of Waterloo, and by Social Sciences and
Research Council of Canada Grant 410-2011-1737 and
Postdoctoral Fellowship 756-2014-0319.
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The interpretation of seismic image data involves dealing with considerable uncertainty. Interpreters employ heuristics (“rules of thumb”) during interpretation to make judgments. These heuristics lead to unwanted, and usually unknown, cognitive biases that influence the interpretation. This chapter introduces the concept of subjective uncertainty and goes on to describe four of the key biases that influence the seismic interpretation process: anchoring; availability; herding; and framing. Each bias is described from original conception through to how it influences the seismic interpretation process. Research specific to seismic interpretation is outlined and summarized. The chapter concludes with an overview of bias mitigation methods that can be employed for seismic interpretation.
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The COVID-19 pandemic has brought about a surge of fake news on social media. This dilemma has caused a ripple effect in society with increasing censorship on social media, which threatens the freedom of expression. The populace cannot effectively progress until they understand the threat posed by fake news and censorship. To protect our fundamental rights of expression, society must learn from librarians. The chapter explores the role of librarians in mitigating fake news. The chapter also identifies possible societal consequences of fake news. The chapter concludes that librarians should inoculate the public to preempt them from accepting fake news.
Two studies demonstrated that attempts to debias hindsight by thinking about alternative outcomes may backfire and traced this to the influence of subjective accessibility experiences. Participants listed either few (2) or many (10) thoughts about how an event might have turned out otherwise. Listing many counterfactual thoughts was experienced as difficult and consistently increased the hindsight bias, presumably because the experienced difficulty suggested that there were not many ways in which the event might have turned out otherwise. No significant hindsight effects were obtained when participants listed only a few counterfactual thoughts, a task subjectively experienced as easy. The interplay of accessible content and subjective accessibility experiences in the hindsight bias is discussed.
Plusieurs théories de l’enseignement de la logique et de la pensée critique prennent pour acquis que l’apprentissage théorique, celui des règles formelles par exemple, et son application pratique sont suffisants pour maîtriser les outils enseignés et pour prendre l’habitude de les mettre en usage. Toutefois, tout indique que cet enseignement n’est pas efficace, une conclusion supportée par plusieurs travaux en sciences cognitives. Approcher l’étude de la cognition évolutionnairement avec les théories à processus duaux permet une explication de ces insuffisances, tout en offrant des pistes pour aborder l’enseignement de la pensée critique et de la logique de manière plus efficace. Dans cet article, nous souhaitons présenter cette approche et explorer ces pistes de solution afin de faire quelques recommandations pédagogiques et mettre en place un cadre théorique. Nous présenterons un exemple d’application de ce programme de recherche avec la philosophie pour enfants.
Critics of intelligence tests-writers such as Robert Sternberg, Howard Gardner, and Daniel Goleman-have argued in recent years that these tests neglect important qualities such as emotion, empathy, and interpersonal skills. However, such critiques imply that though intelligence tests may miss certain key noncognitive areas, they encompass most of what is important in the cognitive domain. In this book, Keith E. Stanovich challenges this widely held assumption. Stanovich shows that IQ tests (or their proxies, such as the SAT) are radically incomplete as measures of cognitive functioning. They fail to assess traits that most people associate with "good thinking," skills such as judgment and decision making. Such cognitive skills are crucial to real-world behavior, affecting the way we plan, evaluate critical evidence, judge risks and probabilities, and make effective decisions. IQ tests fail to assess these skills of rational thought, even though they are measurable cognitive processes. Rational thought is just as important as intelligence, Stanovich argues, and it should be valued as highly as the abilities currently measured on intelligence tests.
Introduction and Definition of a Principle Basic Research Supporting the Principle and its Functional Components Brief History of Self-Regulatory Applications in Cognitive Behavior Therapy Contemporary Evidence-Based Applications of Self-Regulation in CBT The Relation of Self-Regulation to other Cognitive Behavioral Principles and Mechanisms Summary and Conclusion
This book presents a new approach to epistemology (the theory of human knowledge and reasoning). Its approach aims to liberate epistemology from the scholastic debates of standard analytic epistemology, and treat it as a branch of the philosophy of science. The approach is novel in its use of cost-benefit analysis to guide people facing real reasoning problems and in its framework for resolving normative disputes in psychology. Based on empirical data, the book shows how people can improve their reasoning by relying on Statistical Prediction Rules (SPRs). It then develops and articulates the positive core of the book. The view presented - Strategic Reliabilism - claims that epistemic excellence consists in the efficient allocation of cognitive resources to reliable reasoning strategies, applied to significant problems. The last third of the book develops the implications of this view for standard analytic epistemology; for resolving normative disputes in psychology; and for offering practical, concrete advice on how this theory can improve real people's reasoning.
This book attempts to resolve the Great Rationality Debate in cognitive science-the debate about how much irrationality to ascribe to human cognition. It shows how the insights of dual-process theory and evolutionary psychology can be combined to explain why humans are sometimes irrational even though they possess remarkably adaptive cognitive machinery. The book argues that to characterize fully differences in rational thinking, we need to replace dual-process theories with tripartite models of cognition. Using a unique individual differences approach, it shows that the traditional second system (System 2) of dual-process theory must be further divided into the reflective mind and the algorithmic mind. Distinguishing them gives a better appreciation of the significant differences in their key functions: the key function of the reflective mind is to detect the need to interrupt autonomous processing and to begin simulation activities, whereas that of the algorithmic mind is to sustain the processing of decoupled secondary representations in cognitive simulation. The book then uses this algorithmic/reflective distinction to develop a taxonomy of cognitive errors made on tasks in the heuristics and biases literature. It presents the empirical data to show that the tendency to make these thinking errors is not highly related to intelligence. Using a tripartite model of cognition, the book shows how, when both are properly defined, rationality is a more encompassing construct than intelligence, and that IQ tests fail to assess individual differences in rational thought. It then goes on to discuss the types of thinking processes that would be measured if rational thinking were to be assessed as IQ has been.