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Proceedings of the Human Factors and
Ergonomics Society Annual Meeting
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Copyright © 2023 Human Factors
and Ergonomics Society
DOI: 10.1177/21695067231193646
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Introduction
The proliferation of artificial intelligence (AI) and machine
learning (ML) available to organizations and the increasing
variety of ways in which these technologies can interact
with people (Amershi et al., 2014; Arrieta et al., 2020)
emphasizes the pressing need for reliable, practical, and sus-
tainable tools to help stakeholders quickly identify which
technologies are likely to be successful for them. This is
nontrivial because people overestimate machine capabilities
and how they interact with people so frequently that it has
been called a law (see “Robin Murphy’s Law” in Woods &
Hollnagel, 2006), which could be feasibly measured as an
“over-promise rate” (NASEM, 2021). Organizations need
additional support to guide the development of AI/ML tech-
nologies early in the design process in order to evaluate an
increasing volume of AI/ML options, counteract the ten-
dency to overestimate capabilities, and quickly discard dis-
advantageous options before the cost of change becomes
prohibitively expensive.
For this needed intervention to be impactful, its imple-
mentation must be grounded in strongly supported scientific
ideas, aligned with stakeholders’ mental models about what
is relevant and valuable, pragmatic relative to the costs of
implementation, compatible with the resources and actors
available, and sustainable as organizational goals and priori-
ties continue to change over time (Fitzgerald, 2019).
However, there are currently no available tools and methods
for designing and evaluating how well technologies can inte-
grate into a joint human-machine cognitive system that fully
satisfy these requirements for impactful implementations.
Although empirical evaluations, either before (Roth et al.,
1987) or after (Beede et al., 2020) full-scale implementation,
are perhaps the most reliable methods to uncover the ways in
which technologies can interact with people in surprising
(and detrimental) ways, they are neither sufficiently prag-
matic nor sustainable to have a large-scale impact on their
own. High-fidelity testing is costly. It typically involves
solutions late in their development lifecycle, reserved time
from end-users, and months or more of planning and
preparation from a dedicated study team. Because of these
1193646PROXXX10.1177/21695067231193646Proceedings of the Human Factors and Ergonomics Society Annual MeetingMorey et al.
research-article20232023
1The Ohio State University, Columbus, OH, USA
2Charles River Analytics, Cambridge, MA, USA
Corresponding Author:
Dane A. Morey, The Ohio State University, 1971 Neil Ave, Columbus,
OH 43210, USA.
Email: morey.38@osu.edu
Towards Joint Activity Design Heuristics:
Essentials for Human-Machine Teaming
Dane A. Morey1, Prerana Walli1, Kenneth S. Cassidy1,
Priyanka K. Tewani1, Morgan E. Reynolds1, Samantha Malone1,
Mohammadreza Jalaeian1, Michael F. Rayo1, and Nicolette M.
McGeorge2
Abstract
As machines increasingly behave more like active cognitive agents than passive tools, additional heuristics for supporting
joint human-machine activity are urgently needed to complement existing usability heuristics. Despite the rich and extensive
design guidance produced by forty years of cognitive systems engineering (CSE) and related fields, the lack of large-scale
impact can be attributed, in part, to insufficient translation of CSE principles and guidelines to language and tools that are
ready for designers and other decision-makers responsible for these automation-infused solutions. Towards this need, we
synthesized a partial and preliminary list of ten machine requirements intended to capture some of the essentials of joint activity.
We believe solidifying these essentials and their implications for machines is a first and necessary step towards deriving joint
activity design heuristics that are valuable, practical, and sustainable for operational personnel. Through iterative refinement,
we believe the combination of strong ideas and strong practicality in these tools can be the basis for a large-scale shift in the
design and evaluation of human-machine teams.
Keywords
Joint Activity, Human-Machine Teaming, Design Heuristics, Cognitive Systems Engineering, Design Requirements
2 Proceedings of the Human Factors and Ergonomics Society Annual Meeting
constraints, tests are often limited to a focused subset of can-
didate technologies. Therefore, empirical evaluations alone
are usually too costly (in money, time, and human resources)
to be pragmatic for a broader exploration of candidate tech-
nologies, or else too slow and limited to sustainably keep
pace with changing organizational needs. Though some
empirical testing is almost certainly necessary to mitigate the
risks of implementation surprise, complementary analytical
evaluations (e.g., heuristic reviews) will also be necessary to
satisfy the pragmatic and sustainable requirements of organi-
zational interventions.
Despite widespread adoption, usability heuristics alone
are conceptually insufficient to reliably evaluate technolo-
gies that behave more like active cognitive agents than pas-
sive tools (Rayo, 2017). The prominence, popularity, and
consistency of principles like Nielsen’s 10 usability heuris-
tics (Nielsen, 1994, 2020) is a testament to their model align-
ment, pragmatism, and sustainability relative to the interface
design community. Nevertheless, as technologies become
capable of making inferences and interjections, there remains
concern that usability alone does not sufficiently address the
critical need to support macrocognitive and coordinative
functions like event detection, sensemaking, replanning,
forming a basic compact, and common grounding (Rayo,
2017). There is strong agreement among experts that concep-
tualizing AI/ML technologies like teammates or team play-
ers is valid and valuable beyond conceptualizing these
technologies simply like tools (NASEM, 2021). Therefore,
tool-like usability heuristics alone do not appear sufficiently
grounded in the supported scientific ideas to be impactful
interventions.
A growing body of literature from cognitive systems engi-
neering and other related fields is providing guidance beyond
usability alone for designing and evaluating AI/ML technol-
ogies for human-machine teaming (NASEM, 2021); how-
ever, despite forty years of research, this literature has yet to
have the large-scale impact desired beyond the human fac-
tors community (Dominguez et al., 2021). This shortcoming
may in part stem from the wide variety of perspectives, each
with their own terminology, from which relevant AI/ML
guidelines can be derived, including: joint activity (Klein
et al., 2005), teamwork (Feigh & Pritchett, 2014), interde-
pendence (Johnson et al., 2014), situation awareness
(Endsley, 2017), macrocognition (Klein et al., 2003; Rayo,
2017), distributed problem solving (Smith, 2018), joint cog-
nitive systems (Woods & Hollnagel, 2006), human-AI inter-
action (Amershi et al., 2019), and resilience (Woods, 2019).
Furthermore, most of this guidance was written for human
factors professionals, often too vague or inaccessible for a
broader audience (e.g., Rayo, 2017). In either case, the
inconsistency and inaccessibility of guidance suggests that
this body of literature as a whole, regardless of its potential
value, has insufficient alignment and/or pragmatics to engage
the broader set of organizational actors (beyond the limited
number of human factors professionals) required to keep
pace with the scale and tempo of AI/ML proliferation.
The aim of this work is to begin to bridge the gap between
the scientific literature on joint human-machine activity and
the actors available to make large-scale impacts. Our end
goal is to compile, synthesize, and translate guidance rele-
vant to designing for and evaluating joint activity in a way
that is understandable, pragmatic, and sustainable for opera-
tional personnel. We chose to specifically target operational
personnel because they are likely to be the people in organi-
zations who (1) are most familiar with the technologies and
demands of work, (2) have a direct stake in the outcome, and
(3) comprise a large workforce capable of a large-scale
impact. However, before we can address this end, we first
seek to solidify the essentials of joint activity from which
subsequent design heuristics can be derived. We report our
partial and preliminary findings, focused specifically on the
subset of joint human-machine activity, as a set of ten
machine requirements stemming from joint activity essen-
tials. We expect this partial list to grow and change as we
expand beyond human-machine activity to joint activity in
general and as we shift from machine-focused requirements
to system-focused essentials.
Methods
We began with a broad review of the literature relevant to
human-machine joint activity, including but not limited to
the literature bases mentioned previously. For each article,
we compiled a list of all takeaways relevant to designing for
and/or evaluating joint human-machine activity and catego-
rized each takeaway as either a problem (to be avoided) or
guideline (to be design for). We used these problems and
guidelines as the basis for thematic analysis. In alignment
with the constant comparative analysis method (Fram, 2013),
we first constructed open codes to reflect the individual lan-
guage of the authors of each article. We then proceeded
through iterative axial coding to group our open codes into
clusters of common concepts. Informed by what we were
finding, we continued to review the literature until we had
reached saturation.
From these axial codes, we next constructed a set of
machine requirements for satisfying joint activity essentials.
First, we composed one or more concise evocative state-
ments to represent each of our axial codes. Importantly, we
chose language intended to resonate with operational person-
nel, which was not necessarily the language found in the aca-
demic literature, to better align with their mental models.
Second, we expounded upon each of these evocative state-
ments with a short 1-3 sentence synopsis. Finally, we elabo-
rated on each heuristic even more with 1-3 paragraphs of
further details, a toy example to illustrate the core concept(s),
and a list of tips and tricks for effective use. We modeled this
progressive description of each heuristic after Nielsen (2020)
Morey et al. 3
and expected that this would help to engage operational per-
sonnel in a way that is meaningful, but not overwhelming.
Results
Our literature review covered a total of 113 articles. From
these articles, we identified 590 problems and/or guidelines
relevant to the design and evaluation of joint human-machine
activity. From these open codes, we identified nine axial
codes: jointness or teamwork, escalation and resilience,
observability, directability, limits and boundaries, informa-
tiveness, redirecting attention, interjection strength, and
diversity. The following section includes the partial and pre-
liminary ten machine requirements we composed to convey
to operational personnel the core concepts underlying these
nine axial codes. Due to space constraints, we report only the
tagline (in bold) and a consolidated synopsis of the remain-
der of related materials for each requirement.
Ten Machine Requirements To Satisfy
Essentials Of Joint Activity
The machine changes and augments what
people can do, rather than replaces them
Machines can never replace people, but they can change
people’s roles and the kinds of the work people do (Woods &
Hollnagel, 2006). No matter how capable machines become
to take actions on their own, people will ultimately be held
responsible for the actions of machines (Murphy & Woods,
2009). At minimum, this requires people to monitor or super-
vise machines, which often creates additional problems
(Sarter et al., 1997). Not only do machines that attempt to
replace people fail to do so, they also miss out on the benefits
of being part of a team. Effective teams enable all members
to contribute to the work so that the team accomplishes more
than any of its members could accomplish alone. Likewise,
machines should augment, amplify, or contribute in a way
that aids, not replaces, the work of people (Woods, 1985).
The machine interacts with people: they
cannotremain separate or invisible
Effective teams are well-coordinated, which requires team
members to interact with others in ways that help the team
align their activities. These interactions include making
actions and intentions visible to others, monitoring others’
actions and intentions, synchronizing actions, aligning goals,
shifting roles, and minimizing the burden that these interac-
tions have on others in the team (Klein et al., 2005). Machines
attempting to remain separate from or invisible to people do
not eliminate the need for people to coordinate with machines,
but rather make this coordination difficult or impossible to
accomplish (Roth et al., 1987). Instead, machines should
expand, rather than reduce, the ways in which they can inter-
act with people to coordinate activities (Johnson et al., 2020).
The machine clearly indicates to people its past,
present, and future status and intentions
To be well-coordinated, teams need to have a shared under-
standing of the overall task and the progress being made
towards achieving it (Klein et al., 2005). Team members
need to clearly communicate their activities, status, and
intentions so that others can seamlessly and efficiently coor-
dinate their current and future actions without surprises. For
machines, this requires more than simply making actions or
computations visible. People must understand what the
machine has done, what it is doing, and what it is going to do
next (Sarter et al., 1997).
The machine conveys to people the ways it is
misaligned to the world, even when it is unaware
that it is misaligned to the world
Machines are literal-minded, meaning that they will only
function in accordance with their programming and resultant
models of the world. However, a machine cannot tell if its
model of the world is in fact the world it is in (Woods &
Hollnagel, 2006). Consequently, machines will often take the
right actions (according to their models of the world) in the
wrong world. People need to help keep machines aligned to
the current situation and make sure they are not operating
outside their limits (Hoffman et al., 2002). To help people do
this, machines must send signals or clues to people that con-
vey when, how, and why they are operating outside their lim-
its. However, like any team member, machines have limits to
how well they can understand their own limits (Woods,
2018). Therefore, machines will be unreliable at communi-
cating their own limits and need help from people. At mini-
mum, this requires some way for people to simultaneously
understand the world, how the machine is “seeing” the world
through its model, and how well the machine’s view of the
world is aligned to the world itself (Rayo et al., 2020).
The machine changes its actions, plans, and
priorities based on people’s directions
Effective teammates are able to direct the actions of others
and be directed by others (Klein et al., 2005). However,
unlike human teams which share responsibility, people are
always responsible for the outcomes of machine actions
(Murphy & Woods, 2009). Therefore, people must always be
in control. Otherwise, people will find ways to indirectly exert
their influence, like turning off the machine (Christoffersen &
Woods, 2002), or else machines can cause catastrophic acci-
dents, like the two Boeing 737 MAX crashes which killed a
total of 346 people (FAA, 2020). The ability to comply with
4 Proceedings of the Human Factors and Ergonomics Society Annual Meeting
people’s directions is a compulsory machine design require-
ment (Johnson et al., 2020).
The machine helps people adapt how it works to
keep the system working
As situations escalate from normal to exceptional, machines
often get in the way more than they help (Woods & Patterson,
2000). Machines stick to their set of rules, but successfully
responding to these exceptional situations often requires
changing procedures, sacrificing some goals, or otherwise
breaking the normal rules (Chuang et al., 2019). Machines
must allow (or even help) people break the machine’s rules
during exceptional circumstances; otherwise, machines are
likely to exacerbate the situation with additional burden
(Woods & Patterson, 2000).
The machine helps guide, but does not force,
people’s flow of attention to what is important
Attention is limited. Effective teams send signals to help
each other shift and focus their attention on what is impor-
tant; however, machines often struggle to understand what
is important (Woods, 1995a). Machines that frequently
send unhelpful alerts can annoy or even impair people’s
ability to figure out what is important (Rayo & Moffatt-
Bruce, 2015). Therefore, people must be able to determine
whether the signals machines send are important without
having to fully shift their attention from what they are cur-
rently doing (Woods, 1995a).
The machine conveys to people changes or
events without disrupting their workflow
Effective teammates assess whether their message or action
is important enough to interrupt what another is doing, which
depends upon both the importance of the interruption and the
importance of the other’s current actions (Klein et al., 2005).
However, machines are often poor at gauging interruptibility,
which can exacerbate periods of high workload with addi-
tional unhelpful disruptions (Woods, 1995a). Machines
should alert people to important changes in the situation in
the least disruptive manner possible, without loss of informa-
tion or urgency, so that managing the interruptions them-
selves does not become an additional burden.
The machine simultaneously helps people
understand the details and the bigger picture
Though machines can detect, process, and display massive
volumes of data, they struggle to reliably make sense of what
that data means, especially when something is novel or unex-
pected. For example, a substantial proportion of AI/ML
research continues to focus on image classification (Arrieta
et al., 2020), something so basic for people that we do not
consider it to be a decision. Machines still largely rely upon
people to understand the bigger picture, but data overload
becomes a problem. The volume of data machines display
can overwhelm and inhibit people from seeing the bigger
picture, yet machines that reduce this volume risk removing
crucial data, which also prevents people from seeing the big-
ger picture (Woods et al., 2002). Effective machines must
organize, but not reduce, the data available in a way that
helps people simultaneously see the bigger picture without
getting lost in the details and see the details without losing
sight of the bigger picture (Woods, 1995b).
The machine enables and encourages people to
consider multiple different perspectives
Every perspective is limited, both revealing and hiding cer-
tain aspects of the current situation (Hoffman & Woods,
2011). Effective teams overcome the limits of each perspec-
tive by supporting people in seeing and contrasting multiple
viewpoints. Machines need to be explicitly designed to sup-
port switching, comparing, and combining different view-
points. Otherwise, switching between viewpoints can be too
costly or disruptive (Woods, 1984). Machines must provide
low-cost ways for people to shift perspectives and highlight
when it is valuable to do so.
Discussion
Synthesizing the rich and extensive literature on joint human-
machine cognitive systems in a way that is valuable, practi-
cal, and sustainable for operational personnel is ambitious.
We chose colloquial (rather than academic) language and
phrases to align and connect with practitioner mental mod-
els. We limited the scope and level of detail in communicat-
ing concepts to enhance the pragmatics and explainability of
these requirements. This operationalization, like any, risks
oversimplification of the true complexity underlying these
concepts (Feltovich et al., 2004). However, we tried to miti-
gate this risk by composing statements that would evoke
rather than mask the underlying complexity. Ultimately, we
believe this operationalization of concepts specifically for
operational personnel is a necessary endeavor if the literature
on joint cognitive systems is to have the large-scale impact
that is desired (Dominguez et al., 2021).
However, the utility of these machine requirements,
essentials of joint activity, and eventual design heuristics can
only be fully evaluated by the impact they have for opera-
tional personnel. To this end, each should be empirically
evaluated to assess how and how well operational personnel
can utilize them. This involves, at minimum, the reliability,
validity, and pragmatism of using these tools to design and/
or evaluate candidate technologies. An effective intervention
like joint activity design heuristics will require both strong
Morey et al. 5
ideas and strong practicality to have a large-scale and benefi-
cial impact.
Acknowledgments
This material is based upon work supported by the US Air Force
Research Laboratory 711th Human Performance Wing (US AFRL
711 HPW) under Contract No. FA8650-22-C-6453. Any opinions,
findings, and conclusions or recommendations expressed in this
material are those of the authors and do not necessarily reflect the
view of the US AFRL.
ORCID iDs
Dane A. Morey https://orcid.org/0000-0002-9859-7719
Prerana Walli https://orcid.org/0009-0007-6036-4999
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