Debiasing Judgements Using a Distributed Cognition Approach: A Scoping Review of Technological Strategies

Abstract

Objective
To review and synthesise research on technological debiasing strategies across domains, present a novel distributed cognition-based classification system, and discuss theoretical implications for the field.

Background
Distributed cognition theory is valuable for understanding and mitigating cognitive biases in high-stakes settings where sensemaking and problem-solving are contingent upon information representations and flows in the decision environment. Shifting the focus of debiasing from individuals to systems, technological debiasing strategies involve designing system components to minimise the negative impacts of cognitive bias on performance. To integrate these strategies into real-world practices effectively, it is imperative to clarify the current state of evidence and types of strategies utilised.

Methods
We conducted systematic searches across six databases. Following screening and data charting, identified strategies were classified into (i) group composition and structure, (ii) information design and (iii) procedural debiasing, based on distributed cognition principles, and cognitive biases, classified into eight categories.

Results
Eighty articles met the inclusion criteria, addressing 100 debiasing investigations and 91 cognitive biases. A majority (80%) of the identified debiasing strategies were reportedly effective, whereas fourteen were ineffective and six were partially effective. Information design strategies were studied most, followed by procedural debiasing, and group structure and composition. Gaps and directions for future work are discussed.

Conclusion
Through the lens of distributed cognition theory, technological debiasing represents a reconceptualisation of cognitive bias mitigation, showing promise for real-world application.

Application
The study results and debiasing classification presented can inform the design of high-stakes work systems to support cognition and minimise judgement errors.

Full text

Review Article
Human Factors
2024, Vol. 0(0) 1–21
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and Ergonomics Society
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DOI: 10.1177/00187208241292897
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Debiasing Judgements Using a
Distributed Cognition Approach: A
Scoping Review of Technological
Strategies
Harini Dharanikota
1
, Emma Howie
1,2
, Lorraine Hope
3
,
Stephen J. Wigmore
1,2
, Richard J. E. Skipworth
1,2
, and Steven Yule
1,2

Abstract
Objective: To review and synthesise research on technological debiasing strategies across domains,
present a novel distributed cognition-based classification system, and discuss theoretical implications for
the field.
Background: Distributed cognition theory is valuable for understanding and mitigating cognitive biases in
high-stakes settings where sensemaking and problem-solving are contingent upon information repre-
sentations and flows in the decision environment. Shifting the focus of debiasing from individuals to
systems, technological debiasing strategies involve designing system components to minimise the negative
impacts of cognitive bias on performance. To integrate these strategies into real-world practices ef-
fectively, it is imperative to clarify the current state of evidence and types of strategies utilised.
Methods: We conducted systematic searches across six databases. Following screening and data charting,
identified strategies were classified into (i) group composition and structure, (ii) information design and
(iii) procedural debiasing, based on distributed cognition principles, and cognitive biases, classified into
eight categories.
Results: Eighty articles met the inclusion criteria, addressing 100 debiasing investigations and 91 cognitive
biases. A majority (80%) of the identified debiasing strategies were reportedly effective, whereas fourteen
were ineffective and six were partially effective. Information design strategies were studied most, followed
by procedural debiasing, and group structure and composition. Gaps and directions for future work are
discussed.
Conclusion: Through the lens of distributed cognition theory, technological debiasing represents a
reconceptualisation of cognitive bias mitigation, showing promise for real-world application.
Application: The study results and debiasing classification presented can inform the design of high-stakes
work systems to support cognition and minimise judgement errors.
1
The University of Edinburgh, UK
2
Royal Infirmary of Edinburgh, UK
3
University of Portsmouth, UK
Corresponding Author:
Harini Dharanikota, Centre for Medical Informatics, Usher Building, The University of Edinburgh, 3 Little France Road, Edinburgh,
Scotland EH16 4UX, UK; e-mail: l.h.dharanikota@sms.ed.ac.uk

Keywords
Cognitive bias, decision making, cognitive engineering, reasoning, performance
Introduction
Expertise and training are only a part of sustained
optimal performance in many real-world settings.
High-stakes decision-making literature has long
demonstrated the impact of the spatial, temporal,
social and information context on outcomes. In es-
sence, cognitive processes are distributed across the
environment within which they occur (Hutchins,
1995). These processes can be degraded by other-
wise adaptive cognitive biases due to inadequate
information representations through different com-
ponents of the system. Cognition has traditionally
been conceptualised in terms of information pro-
cessing within the mind, placing the burden of errors
solely on the individual. Fischhoff (1982) first
classified debiasing efforts based on whether the
cognitive bias originates from “the judge, the task, or
some mismatch between the two”(p. 424). Later
works, including Soll et al. (2015) and Larrick
(2004), have discussed the importance of broaden-
ing the scope of debiasing to the environment.
In this paper, we expand on reorienting cog-
nition from the individual to the system and its
relevance to understanding and mitigating biases
in reasoning (i.e., debiasing) in practice. To
achieve this, we draw on distributed cognition
theory, defining and reviewing technological de-
biasing strategies from the literature, classifying
them along the principles of the distributed cog-
nition framework, and discussing implications for
enhancing human factors and performance in real-
world settings.
Cognitive biases represent cognitive processes
that influence the application of specificreasoning
rules to information, when forming judgements or
making decisions. First introduced by Tversky and
Kahneman (1974), cognitive biases are defined as
systematic patterns of deviation from a pre-
determined rational ideal of reasoning. While eco-
logically adaptive (Haselton et al., 2015), they have
the potential to lead to erroneous judgements and
negatively consequential decisions. Cognitive biases
are well-documented in a variety of critical perfor-
mance contexts including surgery (Armstrong et al.,
2023), crisis management (Comes, 2016)and
aviation (Murata et al., 2015). These situations are
often characterised by high cognitive load, uncer-
tainty, task saturation and environmental disruptors
which may exacerbate the prevalence and impact of
cognitive biases.
Debiasing for Human Factors
Lilienfield et al. (2009) wrote of debiasing as en-
compassing “not only techniques that eliminate
biases but also those that diminish their intensity or
frequency”(p. 391). Although not yet formally de-
fined in the context of human factors, debiasing is
understood as efforts or techniques to eliminate the
presence of bias or the undesirable consequences of
biases in judgement and decision-making tasks. The
instrumental view of rationality is the most appli-
cable to human factors; the goal is to improve human
and system capacity to maximise utility, accuracy
and goal-achievement, acknowledging that biases
may be adaptive in achieving these ends. Experts
may utilise simplifying heuristics to guide their
judgement, but their use alone does not define expert
performance. Optimal outcomes are achieved when
there is coherence between the heuristics applied and
the conditions of performance (Kahneman & Klein,
2009). The goal of debiasing should be to reduce
errors and inefficiencies and promote the achieve-
ment of optimal outcomes given the opportunities
and constraints of the decision environment and
human capabilities. This means, the decision-making
environment must be aligned to fit adaptive human
reasoning, as opposed to attempting to debias the
human judge to completely eliminate any biased
tendencies.
Widely studied, cognitive and motivational
strategies (Arkes, 1991;Larrick, 2004), that aim to
“modify the decision-maker”(Solletal.2015;
p. 926), maintain the individual at the core of biased
decision-making. Cognitive strategies attempt to
modify individuals’reasoning through instruction
(e.g., Fahsing et al., 2023)ortraining(e.g.,Dunbar
et al., 2014), whereas motivational strategies utilise
incentives and accountability (e.g., Finkelstein et al.,
2022). However, from a human factors perspective,
2Human Factors 0(0)

there are limitations to the real-world application of
such approaches.
Cognitive and motivational strategies assume
that humans have unlimited cognitive resources at
their disposal to support analytic and rational
reasoning. They further require that humans can
recognise when they are at risk of being biased.
Both cognitive and motivational strategies largely
rely on humans’conscious will and ability. The
efficacy of these strategies is further limited by the
“bias blind spot,”our tendency to see and exag-
gerate the impact of biases in others, while denying
the existence of our own (Pronin, 2009).
Optimal debiasing strategies should instead
be context-specific and minimise reliance on
individuals’cognitive resources in order to at-
tenuate the trade-off between cognitive effort
and accuracy (Larrick, 2004). In many critical
contexts, it is unlikely that individuals can al-
ways allocate the motivation and cognitive re-
sourcestoengageinaccurateandelaborate
reasoning to reach optimal decisions. In order to
model performance environments where human
sensemaking and environmental factors function
cohesively as a unified system, further consid-
eration of the broader contextual factors that
influence judgement in critical situations is
necessary. Distributed cognition provides a
framework to reorient debiasing applications to
high-stakes contexts, taking into consideration
environmental constraints and opportunities.
Distributed Cognition and
Technological debiasing
The distributed cognition approach views cogni-
tion not as confined within an individual, but as an
emergent property of the system within which the
decisional task is performed. This contrasts with
the classical approach to cognition that places the
emphasis solely on the individual’s own capacity.
Hutchins (1995) proposed the view of cognitive
activity as organised across (i) different members
of teams (i.e., social environment), (ii) the tools
and artefacts (i.e., material environment) and (iii)
time (i.e., flow and transformation of information
over time). In this view, human sensemaking is one
part of the whole system.
In the distributed cognition approach, posi-
tive outcomes are attributed to the smooth and
effective coordination between human and
nonhuman contributors to the system, and not
merely to human skill. In this vein, biased de-
cision processes and outcomes can be considered
a product of ineffective information represen-
tation, transformation and assimilation through
different components of the system. As such,
debiasing strategies in high-performance con-
texts must address all these system-level factors,
beyond individual human cognition.
Soll et al. (2015) referred to these as “modify
the environment”strategies. Larrick (2004) con-
ceptualised these strategies as “technological de-
biasing strategies.”Building on existing work and
incorporating distributed cognition theory, we
define technological debiasing strategies as use or
modification of systems, processes, artefacts and
agents, both human and nonhuman, that are ex-
ternal to individual decision-maker(s) for the
purpose of minimising biased reasoning. In this
context, technology is defined broadly as any
process, artefact, tool or component of the decision
environment (including humans), that is external
to the individual mind. It includes but is not limited
to digital technology.
Further extending this notion, technological
debiasing strategies can be broadly classified into
three categories: (i) Information design, (ii) Pro-
cedural debiasing and (iii) Group composition and
structure, based on the three processes of dis-
tributed cognition introduced by Hutchins (2000)
and further elaborated on by Hollan et al. (2000).
Information design. Information design strategies
pertain to how we make sense of information
structures and stimuli from the decision environ-
ment. Cognitive processes “involve coordination
between internal and external (material or envi-
ronmental) structures”(Hollan et al., 2000, p. 175).
Externally presented information interacts with
humans’internal cognitive structures, resulting in
sensemaking. However, biases may occur when
information stimuli do not align with internal
representations. Addressing the flow or interpre-
tation of information from information sources in
the environment into human cognitive processing,
information design debiasing strategies modify the
organisation, structure and nature of the infor-
mation available or selectively present specific
types of information (e.g., Cook & Smallman,
Dharanikota et al. 3

2008) to ensure accurate mental representations of
information used in decision-making.
Procedural debiasing. The term procedural debiasing
was first introduced by Lopes (1987),whosework
aimed to modify the cognitive procedures within the
judge (or individual decision-maker) to debias
judgements. Here, however, procedural debiasing
pertains to strategies that improve the nature of tasks
in order to fit human cognition for optimal judgement
or decision outcomes. According to distributed
cognition theory, cognitive processes are “distributed
through time such that the outcomes of earlier events
can alter later events”(Hollan et al., 2000,p.175).
Biases can, hence, occur when the sequence of in-
formation gathering or decision subtasks are or-
ganised in ways that promote inaccurate weighting or
processing of information based on when in the
decision workflow information is presented or pro-
cessed. Further, they can potentially accumulate
throughout the system’s cognitive workflow. Ad-
dressing the temporally interdependent nature of
decision tasks, procedural debiasing strategies in-
clude modification of task components, tools and
sequences of subtasks (e.g., Bhandari et al., 2008).
Group composition and structure. Group composi-
tion and structure modifying strategies include the
use of groups to debias judgements, including
replacing individuals with groups (e.g., O’Leary,
2011) but also modifying group structures to de-
bias group decisions (e.g., Meissner & Wulf,
2017). This category corresponds to the idea
that cognitive processes are “distributed across
members of a social group”(p. 175). Biases can
occur at the group-level, due to interactional
properties of social cognition that may facilitate or
hinder different types of reasoning. Modifying
social antecedents of cognitive biases in collective
decision-making, these strategies address infor-
mation exchange between members of a team
occurring as a product of various collective and
individual attributes.
The objective of this scoping review is to
characterise evidence on technological debiasing
strategies across domains according to the three
distributed cognition principles (Hollan et al.,
2000;Hutchins, 1995) for human factors and
identify considerations for determining their ap-
plicability to real-world settings.
Methods
The scoping review method is best suited to the
nature of the research that constitutes this inquiry,
since the literature is broad, fragmented across dis-
ciplines and uses varied terminology and con-
ceptualisations of bias and debiasing. We synthesised
the literature and identified research gaps, following
Arksey and O’Malley’s (2005) recommendations for
scoping reviews, modified by Levac et al. (2010),
that comprises the five following stages:
Identifying the Research Question
Following the PCC (Participant, Concept, Con-
text) framework recommended by the Joanna
Briggs Institute (JBI; Pollock et al., 2023), the
primary research question guiding the scoping
review is “What types of technological debiasing
strategies have been employed to debias human
judgement and decision-making across domains?”
Identifying Relevant Studies
Systematic searches were conducted across six da-
tabases: IEEE Xplore, ACM Digital Library, Psy-
cINFO, Emerald, Web of Science and PubMed.
Search strings captured three elements of the research
question: (i) Adult human judgement and decision-
making (Participant), (ii) Use of technological
strategies (Concept) and (iii) Debiasing or cognitive
bias mitigation (Context). The search strings were
adapted to each database. A pilot search was first
conducted followed by a narrow search using key-
words that retrieved the most relevant articles. Search
terms utilised are presented in Tab l e 1. Hand searches
of the reference lists of relevant papers were carried
out. Articles retrieved by the searches were exported
to Covidence software (Veritas Health Innovation,
2023) to manage study selection.
Study Selection
Studies were selected if they met the following
inclusion criteria: published peer-reviewed, ex-
perimental studies in English that explicitly aimed
to minimise cognitive bias in human decision-
making or judgements using any technological
strategy and addressed at least one type of cog-
nitive bias. This included studies across all
4Human Factors 0(0)

industry contexts, disciplines and healthy adult
populations. Studies were excluded if they (i)
addressed debiasing for behaviour change or
learning, (ii) employed cognitive or motivational
strategies, (iii) involved nonadult populations or
(iv) populations with mental health conditions, (v)
not peer-reviewed publications and (vi) if full texts
were not available. There were no limits on the
year of publication; all relevant studies published
before September 2023 were included.
Charting the Data
Information extracted from the identified articles
included authorship, year of publication, sample size
and type, experimental design, level of analysis
(individual vs. group), industry domain, biases as
named in the studies, cognitive bias category, de-
biasing strategies tested, debiasing category, expo-
sure variables, outcome variables and effectiveness
of the debiasing strategy (effective, ineffective or
partially effective) as reported by the studies.
Collating and Summarising Data
To categorise the cognitive biases studied,
Fleischmann et al.’s(2014)taxonomy of eight
cognitive biases in information systems literature
was used as it was derived from the analysis of
literature in an applied performance domain. The
Table 1. Key Search Terms Utilised to Identify Articles Based on the PCC Framework.
Participant Concept Context
Adult decision-
makers
AND The use of technology in debiasing
strategies
AND Debiasing and cognitive bias
mitigation
Combined with OR Combined with OR Combined with OR
Human AND “Decision support”AND Debias*
Cognitive error “Decision aid*”De-bias*
Human error Computer Anchoring
Decision-making “DSS”Availability
Judgement “GDSS”Base-rate
Tool Confirmation
Nudg* Framing
Technolog* Representative*
Checklist Disposition
Visuali?* “illusion of control”
“Visual analytics”Omission
“Choice architecture”Commission
“Information presentation”Satisf*
Graph* Hindsight
Team Overconfidence
Group Bandwagon
“Cognitive diversity”Ambiguity
Workflow Ascertainment
Bias
Fallacy
Heuristic
Minimi?*
Reduce*
Mitigat*
Alleviat*
Address*
Overcom*
Remediat*
Counter*
Dharanikota et al. 5

taxonomy comprises (i) perception biases, (ii)
pattern-recognition biases, (iii) memory biases,
(iv) decision biases, (v) action-orientated biases,
(vi) stability biases, (vii) social biases and (viii)
interest biases. The debiasing strategy categories
described in the included studies were derived
basedondistributedcognitiontheoryprinciples
and classified into the three aforementioned
categories: (i) information design, (ii) procedural
debiasing and (iii) group composition and
structure. Descriptive statistics were used to
summarise the data. Analysed data are presented
in tabular and graphical formats as appropriate to
aid synthesis.
Results
Keyword and hand searches resulted in a total of
2667 records. After the removal of 305 duplicates,
two independent reviewers screened 2362 ab-
stracts. Of the 184 full texts screened, 80 articles
met the inclusion criteria and were included for
data extraction. See Figure 1 for a flow chart of
screening methods. Identified studies reported
100 debiasing strategies, addressing 91 cognitive
biases. Of these, 71 studies (88.8%) investigated
one bias, eight studies (10.2%) investigated two
biases, and one study addressed three biases within
one debiasing intervention. Fifty-eight studies
Figure 1. Methods flow chart based on Preferred Reporting Items for Systematic reviews and Meta-Analyses
extension for Scoping Reviews (PRISMA-ScR; Tricco et al., 2018).
6Human Factors 0(0)

(72.5%) used a single debiasing strategy and 20
(25%) employed more than one strategy to debias
one or more biases.
Domains of Included Studies
The most represented domains were healthcare
(n= 26, 32.5%), management (n= 13, 16.3%) and
legal or forensic decision-making (n= 9, 11.3%).
Three (3.8%) studies examined transport-related
judgements. Other domains studied include fi-
nance, transport, construction, software devel-
opment, intelligence analysis, political and sport-
related judgements. Seventeen studies (21.3%)
did not study debiasing within a specialised in-
dustry or domain, instead recruiting lay partici-
pants to lab-based decision-making tasks and
experimental paradigms with no specific domain-
focus.
Study Sample Characteristics
Sample sizes varied widely, ranging from 27 to
4101 participants. Thirteen studies recruited par-
ticipants from the relevant judgement context, that
is, samples representative of those making the
decisions in real-world settings, such as navy in-
telligence analysts (Cook & Smallman, 2008),
physicians (Arkes et al., 2022) and crime scene
investigators (de Gruijter et al., 2017). Thirty-three
studies included student samples. Of these,
28 studied student-only samples. Three involved
student samples in educational training relevant to
the industry or context of application, such as law
students (Lid´
en et al., 2019;Schmittata et al.,
2022) and medical students (Almashat et al.,
2008). Four studies included a mixture of stu-
dent and professional samples. These included
judges and law students (Lid´
en et al., 2019) and
managers and management students (Hodgkinson
et al., 1999;Ni et al., 2019;Pitaloka et al., 2019).
Twenty studies involved unspecified samples
drawn from the general population.
Biases Addressed
Of the 91 cognitive biases addressed, pattern-
recognition biases were the most studied cate-
gory (n= 27, 29.8%), followed by decision
biases (n= 17, 18.7%) and perception biases
(n= 17, 18.7%). Of the specificbiasesad-
dressed, confirmation bias (n= 12, 13.2%) was
the most common, followed by framing effect
(n= 7, 7.7%), then anchoring effect (n=6,
6.6%). There were no studies on interest biases,
defined as “biases that lead to suboptimal
evaluations and/or decisions owing to an indi-
vidual’s preferences, ideas, or sympathy for
other people or arguments”(Fleischmann et al.,
2014,p.5).Table 2 describes the cognitive
biases addressed in the identified studies.
Debiasing Strategies Investigated
Of the 100 debiasing strategies, information design
(i.e., presentation, restructuring or selective pre-
sentation of information) accounted for 59%,
followed by 34 studies examining procedural
debiasing, and 7 exploring group composition and
structure. Several studies used a single debiasing
strategy to address multiple biases, and some used
multiple strategies to address a single bias. There
were 114 individual debiasing-bias investigation
pairs (Figure 2). See Appendix 1 for overview of
included papers classified by debiasing strategy
and cognitive bias type.
Reported Effectiveness of Technological
Debiasing Strategies
Of the 100 debiasing strategies, study authors
reported 80 to be effective in minimising bias.
Strategies were considered effective using a
single criterion: the identification of a statisti-
cally significant difference between experi-
mental groups, wherein one group’s responses
were significantly less prone to cognitive bias
than the comparator (Figure 3). Fourteen strat-
egies were reported as ineffective. Ineffective
strategies were those that either found no sig-
nificant difference between groups (n= 12/14),
or a statistically significant increase in biased
reasoning (n= 2/14). Six strategies were par-
tially effective, that is, either effective under
specific conditions (Abyankar et al., 2014;Ni
et al., 2019;Rieger, 2012;Sezer et al., 2016;
Shaikh, 2022;van Dogen et al., 2005)oref-
fective in mitigating one of the biases studied but
not another (Bhasker & Kumaraswamy, 1991).
Dharanikota et al. 7

Table 2. Overview of Biases Addressed by Included Studies, Categorised by Bias Type (n= 91).
Bias category Definitions Biases addressed Studies
Pattern-
recognition
biases
(n= 27)
“Pattern recognition biases
occur when, in the evaluation
of alternative patterns of
thinking, barely known
information or unknown
information is discarded in
favour of familiar patterns of
thinking or information that
currently happen to be
present in the mind.”
Confirmation bias (n= 12) Chuanromanee & Metoyer, 2022;
Cook & Smallman, 2008;de
Gruijter et al., 2017;Hayes et al.,
2016;Hernandez & Preston, 2013;
Huang et al., 2012;Kayhan, 2013;
Kostopoulou et al., 2021;Lid´
en
et al., 2019;Mojzisch et al., 2008;
van Dongen et al., 2005;van Swol
et al., 2023
Availability bias (n=4) Shaikh (2022),Benbasat and Lim
(2000),Liang et al. (2022),Küper
et al. (2022)
Contextual bias (n=4) MacLean & Read (2019),Quigley-
McBride, 2020;Quigley-McBride
& Wells, 2018;Schmittata et al.,
2022
Familiarity bias (n=1) Marett and Adams (2006)
Early-stage neglect of
alternative (n=1)
Hayes et al. (2016)
Causality bias (n=1) D´
ıaz-Lago and Matute (2019)
Order effects (n=5) Bansback et al. (2014),Lau and
Coiera (2009),Ubel et al. (2010),
van Dongen et al. (2005),
Zikmund-Fisher et al. (2008)
Perception
biases
(n= 17)
“Perception biases affect the
processing of new information
that is received by an
individual. A potential
subsequent decision and the
resulting behaviour are flawed,
when based on this biased
information.”
Framing effect (n=7) Abyankar et al. (2014),Almashat
et al. (2008),Bernstein et al.
(1999),Bhandari et al. (2008),
Garcia-Retamero and Galesic
(2010a),Garcia-Retamero and
Dhami (2013),Hodgkinson et al.
(1999)
Decoy (or Attraction)
effect (n=3)
Jeong et al. (2021),Teppan and
Felfernig (2009),Dimara et al.
(2018)
Within-the-bar bias (n=1) Okan et al. (2018)
Temporal inconsistency
bias (n=1)
Zikmund-Fisher et al. (2007)
Equalising bias (n=1) Rezaei et al. (2022)
Representativeness bias
(n=3)
Bhandari et al. (2008),Lim and
Benbasat (1997),Küper et al.
(2022)
Ratio bias (n=1) Zikmund-Fisher et al. (2008)
(continued)
8Human Factors 0(0)

Table 2. (continued)
Bias category Definitions Biases addressed Studies
Decision
biases (n=
17)
“Decision biases occur directly
during the actual process of
decision-making and diminish
the quality of actual as well as
future decision outcomes.”
Conjunction fallacy (n=4) Bhasker and Kumaraswamy (1991),
O’Leary (2011), Morier & Borgida
(1984) Rieger (2012),Arkes et al.
(2022)
Disposition effect (n=3) Quigley-McBride and Wells (2018),
Frydman and Rangel (2014),Król
and Król (2019)
Base-rate fallacy (n=5) Bhasker and Kumaraswamy (1991),
Roy and Lerch (1996),Tsai et al.
(2011),Greening et al. (2005),
Chandler et al. (1999)
Diversification heuristic
(n=1)
Bhandari et al. (2008)
Denominator neglect
(n=2)
Garcia-Retamero and Galesic (2009),
Garcia-Retamero et al. (2010)
Anecdotal reasoning
(n=1)
Fagerlin et al. (2005)
Illusion of control (n=1) Meissner & Wulf (2017)
Action-
orientated
biases
(n= 11)
“Action-oriented biases lead to
premature decisions made
without considering actually
relevant information or
alternative courses of action.”
Overconfidence bias
(n=2)
Meissner et al. (2018),Ferretti et al.
(2023)
Time saving bias (n=4) Eriksson et al. (2015),Fink and
Pinchovski (2020),Gamliel and
Pe’er (2021),Svenson et al. (2014)
Outcome bias (n=1) Sezer et al. (2016)
Optimistic bias (n=3) Lipkus and Klein (2006),Weinstein
(1983),Brown and Imber (2003)
Comparative optimism
(n=1)
Rose (2012)
Stability biases
(n= 12)
“Stability biases make individuals
stick with established or
familiar decisions, even though
alternative information,
arguments, or conditions exist
that are objectively superior.”
Anchoring effect (n=6) Delgado et al. (2018),Lau and Coiera
(2009),Pitaloka et al. (2019),Ni
et al. (2019),Rastogi et al. (2022),
Tang et al. (2023)
Sunk-cost fallacy (n=1) Hamzagic et al. (2021)
Ambiguity aversion (n=1) Bhandari et al. (2008)
Loss aversion (n=2) Delgado et al. (2018),Zhang et al.
(2015)
Conservatism bias (n=1) Zhang et al. (2015)
End-anchoring (n=1) Duclos (2015)
Memory
biases (n=
3)
“Memory biases affect the
process of recalling
information that refers to the
past and thereby substantially
diminish the quality of this
information, which is later
used for decision-making.”
Relative encoding biases
(n=1)
Sharif and Oppenheimer (2021)
Hindsight bias (n=2) Smith and Greene (2005),Wu et al.
(2012)
Social biases
(n=4)
Social biases “arise from attitudes
shaped by the individual’s
relationship to other people.”
Desirability effect (n=1) van Swol et al. (2023)
Ideological bias (n=1) Solomon and Hall (2023)
Fundamental attribution
error (n=1)
Holder and Xiong (2023)
Information pooling bias
(n=1)
Rajivan and Cooke (2018)
Dharanikota et al. 9

Levels of Analysis: Individual Versus Group
Nine studies (11.3%) examined cognitive biases in
group judgements, though none of those were biases
that occur uniquely in groups such as groupthink—
groups’tendencies to maintain unanimity in their
beliefs or ideas even when faulty (Janis, 1971)—or
the bandwagon effect—individuals’tendency to
think and behave as others do and “join the crowd”
(Leibenstein, 1950, p. 184). Strategies addressed at
the group-level were ideological bias, illusion of
control bias, overconfidence bias, conjunction fal-
lacy, availability bias, confirmation bias, represen-
tativeness bias, hindsight bias and information
pooling bias. One study (O’Leary, 2011) compared
individuals’and groups’proneness to the
Figure 2. Frequency of debiasing strategies addressing each bias type (n= 114).
Figure 3. Frequency of debiasing investigations and their effectiveness as reported by the studies reviewed (n= 100).
10 Human Factors 0(0)

conjunction fallacy, concluding that groups are less
prone to the bias than individuals.
Outcome Measures
A range of outcome measures were utilised in the
identified studies to operationalise biased and
unbiased judgement and decision-making. The
majority studied probability or likelihood esti-
mates (e.g., Chandler et al., 1999;Küper et al.,
2022;Okan et al., 2018), judgement or decision
accuracy (e.g., Fink & Pinchovski, 2020;MacLean
& Read, 2019;Tang et al., 2023), absolute and
relative risk estimates (e.g., Garcia-Retamero &
Galesic, 2010b;Lipkus & Klein, 2006;Ubel et al.,
2010), choice or preference judgements (e.g.,
Abyankar et al., 2014;Bernstein et al., 1999;Jeong
et al., 2021) and information behaviour (e.g.,
Kostopoulou et al., 2021;Mojzisch et al., 2008).
Others reported confidence in judgement or de-
cision, and domain-specific measures such as guilt
assessments (Lid´
en et al., 2019;Schmittata et al.,
2022) and jury verdicts (Smith & Greene, 2005).
Discussion
Through systematically scoping the literature, we
identified 100 technological debiasing strategies
within 80 studies that were tested across domains
and organised them according to three distinct
novel theoretically derived categories based on the
distributed cognition framework. Building on the
well-established idea of extending the boundaries
of debiasing techniques to the decision environ-
ment, we found considerable scope to advance
empirical work based on the contributions of the
present review, the avenues for which are dis-
cussed below alongside considerations for prac-
tical applications.
Information design strategies were the most
commonly deployed technological strategies, which
is unsurprising given that errors due to misrepre-
sentation of information, both internally and exter-
nally, are widely documented. Most of these
strategies were reported as being effective in (i)
substituting or complementing text with graphical
representations and visualisations, (ii) enhancing
salience of information presented (e.g., Frydman &
Rangel, 2014;Sharif & Oppenheimer, 2021), (iii)
modifying information framing (e.g., Garcia-
Retamero & Galesic, 2010a) or (iv) selective
provision of information (e.g., Schmittata et al.,
2022), including provision of information about
others judgements and decision processes (e.g.,
Weinstein, 1983). Widely studied, these strate-
gies can aid the design of user interfaces, for
instance, through the manipulation of informa-
tion order and salience, or presentation of in-
formation in appropriate graphical formats.
Procedural debiasing strategies identified
involved techniques that (i) modify task sequence
(Svenson et al., 2014), (ii) segment decisions tasks
(e.g., Arkes et al., 2022), a type of “planned in-
terruption”(Soll et al., 2015, p. 937), (iii) modify
time allocated to task (Rastogi et al., 2022), (iv)
modify the mode in which decision tasks are
performed (e.g., communication medium and
computationally-assisted; Benbasat & Lim, 2000)
and (v) involve simultaneous processing of dif-
ferent types of information (e.g., gaze overlay
support; e.g., Wu et al., 2012). Utilising procedural
debiasing strategies; tools and systems that
structure decision-making processes may be em-
ployed to minimise different biases, particularly in
complex multi-step decision tasks. Alongside re-
cent advances in artificial augmented intelligence
(Zhou et al., 2021), is the emergence of procedural
debiasing strategies for situations where the
boundaries of the decision problem and potential
solutions are well-defined (Marlin, 2018). How-
ever, these strategies must be employed with
caution. Using algorithmic support in decision-
making may introduce a varied set of biases that
are either built into the algorithm or occur as a
result of humans’interaction with AI and its model
predictions (e.g., van Berkel et al., 2023;Kliegr
et al., 2021).
Group composition and structure was the
least explored type of debiasing strategy in the
reviewed studies. That only seven studies inves-
tigated this approach signals a significant gap and
opportunity to empirically advance team design as
a debiasing tool. However, this also raises the
question of whether this type of strategy is as
effective in practice as it may appear in theory. The
paucity of studies identified may be due to the
unique challenges associated with studying group
cognition over individual cognition or a lack of
significant results, that is, there may be unpub-
lished articles that have attempted it but did not
Dharanikota et al. 11

find any debiasing effects. In this review, we
identified the following types of strategies under
this class, namely, (i) replacing individuals with
teams (O’Leary, 2011), (ii) modifying team di-
versity or heterogeneity (Meissner et al., 2018;
Meissner & Wulf, 2017;Mojzisch et al., 2008;
Solomon & Hall, 2023) and (iii) modifying team
cognition through group tenure (Meissner et al.,
2018). Thus far, evidence on reported effectiveness
of these strategies points towards the need for
teams to achieve a conscious balance of hetero-
geneity and homogeneity across variables for
optimal decision outcomes.
Hinsz (2015) proposed that teams are a type of
technology. Defining technology as “the specific
methods, materials and devices used to solve
practical problems,”he suggested that individual
decision-makers utilise team members as “cogni-
tive technology”(p. 219) to solve problems as they
extend the capacity of an individual’s cognition.
By viewing teams as technology, the strengths of
team cognition can be systematically leveraged,
and weaknesses minimised to enhance task per-
formance. This “teams-as-technology”perspective
aligns with a fundamental principle of the dis-
tributed cognition framework, that cognition oc-
curs in a system of interdependent individuals. By
integrating evidence on individual and team
cognition, team composition can be systematically
modified to fit the task. In practice, this may in-
volve assessing individual and group attributes to
form teams that can minimise bias-driven judge-
ment errors.
Gaps and Future Research
The present review covered debiasing investiga-
tions across multiple domains including healthcare-
related, legal, organisational, and financial decision-
making. All but two studies (Kostopoulou et al.,
2021;Solomon & Hall, 2023) were based in lab-
based and otherwise controlled environments, with
a majority involving student samples, which limits
conclusions that can be drawn with respect to ap-
plicability in naturalistic performance settings. More
work establishing ecological validity through rigor-
ous quasi-experimental investigations or utilising
synthetic task environments to approximate the re-
alism of real-world conditions of performance is
required to establish efficacy of debiasing strategies
in human factors practice. Specificneedsforthefield
involve (i) including more representative samples in
future studies, (ii) inquiry into experts’openness to
technological debiasing strategies, (iii) measuring the
efficacy of the strategies and (iv) evaluating impact of
strategies on cognitive load or time to complete tasks.
Further, a majority of the studies identified in
the present review addressed biases at the indi-
vidual level. Seven investigations aimed to debias
decision-making at the group level. There were no
studies examining biases that occur only at the
group-level. Given that most real-world high-
stakes decisions are made by teams, testing de-
biasing strategies at the group-level is critical for
advancing application of debiasing in human
factors.
The digital and computational nature of many
contemporary real-world tasks means that de-
biasing strategies must evolve rapidly to keep
pace. At the intersection of information design and
procedural debiasing strategies are avenues for
novel research advancing real-time visual feed-
back (Shaikh, 2022), including customizable and
interactive visualisations (Tsai et al., 2011). The
feasibility of computationally aided detection of
biases, based on objective behavioural interaction
metrics using visual analytic platforms and tools
has already been demonstrated (Crowley et al.,
2013;Wall et al., 2017). In addition to assessing
bias probability in real-time, utilising these sys-
tems can enable feedback and customisability of
digital interfaces to support human cognition.
An important distinction to note is between
mitigating cognitive bias and mitigating the neg-
ative effects of cognitive bias. Not all biased
decision-making is undesirable or inaccurate.
Specific biases in certain types of decision-making
can also lead to ideal outcomes, while saving time
and cognitive resources. This means that some-
times, debiasing can look like “rebiasing,”that is,
inducing one bias to offset the effects of another
(Larrick, 2004), or leveraging an existing adaptive
heuristic to guide the decision-making towards the
optimal outcome. For example, manipulating the
order of information presented to allow decision-
makers to assign greater weight to the initial chunk
of information (leveraging the anchoring bias), may
be helpful to offset the confirmation bias when
either the initial information contains evidence that
disconfirms prior beliefs or is objectively the most
12 Human Factors 0(0)

important factor in each decision situation. Re-
biasing involves making trade-offs between the
risks associated with either bias, and prioritising the
one with most upside or least downside. As such,
this requires robust experimental testing before
implementing in human factors practice for real-
world consequential decisions.
Practical Implications
To improve judgement and decision-making
(JDM) performance, one may (i) leverage biased
tendencies, (ii) minimise impact of bias on out-
comes or (iii) eradicate impact of bias on out-
comes. With respect to their utilisation outside of
experimental settings and in practice, we draw on
the extant literature and Soll’s (2015) recom-
mendations for choosing debiasing strategies, to
propose a set of criteria to be considered when
assessing applicability of a technological debias-
ing strategy for a given decision situation. These
include (i) risks associated with bias, (ii) nature
and sources of bias, (iii) judgement or decision task
properties, (iv) properties of the decision envi-
ronment, (v) team properties (for team-based
tasks) and (vi) individual psychological and
physiological factors, all described below.
Risk Associated. Considerations for risks associated
involve establishing the (i) potential for harm
(What is the potential for harm if a cognitive bias is
left unchecked?) and (ii) margin of error (What is
the margin of error that defines the threshold for
unacceptable risk?).
Nature and Sources of Bias. To specify the target of
debiasing strategies and assess the source or nature
of cognitive bias, several classification systems
including Fleischmann et al. (2014) have been
suggested in literature. Another notable classifi-
cation is Arkes’(1991) which categorises biases
into three types: association-based (due to reliance
on accessibility of information in memory),
strategy-based (due to use of suboptimal reasoning
strategies or misapplication of decision rules) and
psychophysically-based (due to improper trans-
lation or accessing of information stimuli in the
environment). The source of bias can determine
which features of the decision environment can be
modified to minimise its undesired effects.
Judgement and Decision-Making (JDM) Task
Properties. Task properties relevant to debiasing
include (i) task type (What type of decision task is
being performed? E.g., probability or likelihood
assessment, hypothesis evaluation, choice and risk
estimation), (ii) task frequency (How frequently is
this task performed? Is the task performed non-
routinely or regularly and repetitively?), (iii) task
complexity (How complex is the task, in terms of the
number of distinct informationitemsandactsin-
volved, the degree of item integration required to
perform it and how variable the task components and
their relationships are?) (Wood, 1986 ), (iv) value
criteria (What is considered an optimal outcome?
How is the success or failure of a decision evalu-
ated?) and (v) time (Under what time constraints is
the task being performed? Is the time allocated to a
task fixed or flexible?).
Properties of the Decision Environment. The prop-
erties of the decision environment include (i) role
(Who is involved in performing the JDM task?) (ii)
information needs (What information is required
and utilised to perform the task?), (iii) cognitive
artefacts (What artefacts are currently utilised to
perform the task?) and (iv) information flow (How
does information currently flow, and how should
information flow in the system from one element to
another? E.g., information flow direction, trans-
formation, facilitators and barriers).
Team Properties (For Team-Based Tasks). For team-
based tasks, team properties to be considered in-
clude (i) role distribution (What are the established
roles and responsibilities in the team? How rigid or
fluid are they?), (ii) Cognitive diversity and
compatibility (Across what characteristics should
teams be homogeneous and heterogeneous to
ensure adequate information pool and compre-
hensive information elaboration and synthesis?)
(Mello & Rentsch, 2015;Wang et al., 2024), (iii)
team dynamics (What team dynamics are facili-
tating or hindering effective problem-solving?
E.g., cohesiveness, hierarchical rigidity and team
familiarity) and (iv) integration of any nonhuman
teammates (How can autonomous or nonautono-
mous agents such as artificial intelligence over-
come or aid experts in overcoming the limitations
of human cognition to enhance performance?)
(Vold, 2024).
Dharanikota et al. 13

Psychological and Physiological Factors. Psychological
and physiological considerations for debiasing in-
clude (i) cognitive load (How much intrinsic and
extrinsic cognitive load are individuals under? Would
the debiasing strategy reduce or add to the cognitive
load required to perform tasks?), (ii) fatigue (What
level of fatigue are individuals experiencing? For
instance, one may consider prioritising minimising
errors due to bias under conditions where fatigue is
likely such as decisions made at the end of a shift
during a handover in a hospital environment.) and
(iii) individual differences (How do individuals re-
sponsibleforthetaskfareon individual difference
attributes such as personality, need for cognition and
working memory capacity?).
Limitations of the Review
Whilst the results of this scoping review are en-
couraging and point to a growing body of literature
on debiasing using distributed cognition, our
findings are to be interpreted in light of several
limitations. First, given the fragmented nature of
research in the area and the use of a wide range of
terms to describe the same phenomena, it is pos-
sible that our review may have excluded some
relevant studies. The lack of consistency in ter-
minology, definitions and conceptualisations of
bias or debiasing allows for limited comparability.
Certain ambiguities are also apparent; some de-
biasing strategies can fall into more than one
category since distributed cognition is a complex
process comprising interwoven subprocesses. For
instance, debiasing techniques involving “choice
architecture”can be considered procedural de-
biasing or information design. While the debiasing
categorisation presented here is not absolute, this
scoping review lays the groundwork for advancing
debiasing processes and interventions upon which
future studies may build. Further, due to differing
perspectives on the nature and sources of biases,
the neat assignment of cognitive biases to cate-
gories has been an ongoing challenge in literature.
Fleischmann et al.’s (2014) taxonomy offers a
categorisation relevant to human factors. However,
these bias categories are not definitive and to be
regarded as a starting point towards the develop-
ment of debiasing hypotheses in context.
Second, most of the studies addressed in this
review were reported to be effective in either
completely eliminating the impact of bias on the
decision outcome, reducing the impact of bias or
reducing the undesirable effects of bias on the
judgement or decision outcome. The strategies
were reported as effective or ineffective relative to
the conditions specified in the heterogeneous set of
experimental studies, limiting the comparison of
the effectiveness of each of the three technological
debiasing strategy types. Given this heterogeneity
in study domains, experimental manipulations,
and outcomes studied, a meta-analysis was outside
the scope of this scoping review (Lipsey & Wilson,
2001).
Third, to make the task of reviewing the liter-
ature manageable, our inclusion criteria specified
studies that only explicitly aimed at debiasing
decision-making and judgement. However, ex-
perimental studies on any cognitive bias where one
condition displayed effects of bias and the other
did not, may also be evidence of debiasing. De-
spite this limitation, our work has exemplified the
application of a framework of distributed cognition
principles along which practitioners and re-
searchers can assess current evidence on debiasing
(whether explicit or not) for future work.
Finally, ironically, even in the research litera-
ture on bias, there is potential for publication bias.
This is well-documented in academia and psy-
chological literature (e.g., Gaillard & Devine,
2022;Ioannidis et al., 2014;Maier et al., 2022;
Siegel et al., 2022) wherein evidence supporting
the effectiveness of a strategy may be more likely
to be published than evidence to the contrary (e.g.,
the filing cabinet effect). Given the expansive
nature of work in this area, it was not possible to
access unpublished data from all research groups
studying this phenomenon.
Conclusion
Cognitive processes are not confined to the minds
of individuals. In scoping the literature on tech-
nological debiasing, this review highlights the
potential of adopting a distributed cognition ap-
proach to debiasing judgements through the uti-
lisation of technological strategies. Our review
established a link between distributed cognition
approaches to human factors and cognitive bias
mitigation, applying these principles to real-world
practice. The distributed cognition-based
14 Human Factors 0(0)

classification can aid human factors practitioners
and researchers in identifying debiasing strategies
relevant to their domains of interest and provides a
structured basis to guide practice and future
research. Any domain that requires simultaneous,
constant, and collaborative processing of infor-
mation, particularly under conditions of uncer-
tainty, information overload or time pressure can
benefit from integrating debiasing considerations
into the design of cognitive systems. Technolog-
ical debiasing may be particularly impactful for
teams who must collaborate to make consequential
decisions in uncertain contexts ranging from
spaceflight to surgery.
Key Points
·Negatively consequential cognitive biases in
high-stakes decision-making can be consid-
ered a product of inappropriate flow and
representation of information in the decision
environment.
·We propose that the distributed cognition
theory provides a new perspective on un-
derstanding and implementing bias mitiga-
tion strategies, called technological debiasing
strategies, in-context.
·The 80 papers analysed here provide evidence
supporting the applicability of technological
debiasing strategies across domains, with sig-
nificant gaps and opportunities.
·Real-world application of these strategies re-
quires a holistic human factors consideration of
decision environments and further conceptual
and empirical work.
Declaration of Conflicting Interests
The author(s) declared the following potential conflicts
of interest with respect to the research, authorship, and/
or publication of this article: Dr Yule reports research
grants from the National Institutes for Health, Canadian
Department of National Defense, National Aeronautics
and Space Administration, Melville Trust for Care and
Cure of Cancer and Royal College of Surgeons of
Edinburgh, outside the submitted work.
Funding
The author(s) disclosed receipt of the following financial
support for the research, authorship, and/or publication
of this article: This work was supported by The Melville
Trust for Care and Cure of Cancer (R47363).
ORCID iDs
Harini Dharanikota https://orcid.org/0000-0002-
5201-9753
Steven Yule https://orcid.org/0000-0001-9889-9090
Supplemental Material
Supplemental material for this article is available online.
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Author Biographies
Harini Dharanikota is a PhD candidate at the Usher
Institute, University of Edinburgh. She received
her MSc in psychological research from the
University of Edinburgh in 2021.
Emma Howie is a surgical registrar and clinical
research fellow at the University of Edinburgh.
She received her MBChB from the University of
Glasgow in 2014 and BSc (Hons) in medicine
from the University of St. Andrews in 2011.
Lorraine Hope is a professor of applied cognitive
psychology at the University of Portsmouth. She
received her PhD in psychology from the Uni-
versity of Aberdeen in 2004.
Stephen J Wigmore is the Regius Chair of Surgery
and Head of the Department of Clinical Surgery at
the University of Edinburgh, Royal Infirmary of
Edinburgh. He received his MD from the Uni-
versity of Edinburgh in 1998.
Richard Skipworth is a consultant surgeon, Hon-
orary Reader and NHS Research Scotland Clinician
at the Royal Infirmary of Edinburgh. He received his
MD from the University of Edinburgh in 2011.
Steven Yule is a professor and chair of behavioural
sciences at the Usher Institute, University of Ed-
inburgh. He received his PhD in psychology from
the University of Aberdeen in 2003.
Dharanikota et al. 21