Knowledge Capture for the Design of a Technology Assessment Tool
Daniela Oliveira
and Kimiz Dalkir
Independent Researcher
School of Information Studies, McGill, Montreal, Canada
Keywords: Knowledge Management, Artificial Intelligence, Technology Assessment, Artificial Intelligence Assessment,
Applied Knowledge Management, Artificial Intelligence Documentation.
Abstract: The design of technology assessment tools is an important Knowledge Management endeavour. Technology
assessment is a subject where consensus is far from being achieved. Any project intended to create a
technology assessment tool is expected to generate a lot of discussion or criticism. Among the most critical
kinds of technology, Artificial Intelligence (AI) is a highly polemic kind of technology. Its impacts are
important and multidisciplinary. Moreover, the technology evolves quickly and so do the attitudes toward that
technology. Therefore, business owners intending to produce an AI assessment tool should expect extensive
discussion of different points of view, but also support the continuation of the discussion throughout time and
with different stakeholders. Surprisingly, technology assessment tools developed by business owners have
been particularly neglected in the coalescent discussion about AI documentation, not to mention the support
to create those tools. To foster a continuous innovation flow, business owners should pay particular attention
to how discussions are captured. This paper explores the foundations of knowledge management initiatives
to support the design of an artificial intelligence assessment tool at the business owner, in a process that
supports continuous discussion and innovation. This article also suggests project aspects and supporting
document structure.
Making people exchange ideas, validate knowledge,
and create new knowledge together are some of the
challenges from the Knowledge Management field.
The field is also interested in helping experts produce
consumable documents and, for that, going through
the process of deciding which information should be
retained and which should be left out. A challenge
that does not spare documentation efforts around
Artificial Intelligence (AI). “Determining what
information to include and how to collect that
information is not a simple task”, argued Richards et
al. (2020,p. 1), while designing a document structure
intended to support reporting about AI services.
Documentation challenges involve identifying
what knowledge is mature enough to be written down
(such as a new methodology that has been tried out
enough times to have an article written about it) and
articulating knowledge that has not been yet
expressed (such as the acknowledgement of different
roles and expectations in a new process).
Documentation is particularly challenging when it
involves knowledge about the design phase. Design
is a project phase where several ideas are articulated.
In this perspective, Design is also a knowledge
process. In knowledge processes, some ideas are
retained, others are not (McElroy, 2011). Design is a
process very rich in terms of information about the
end product. In this phase, the values surrounding the
project take shape and the motivation behind the
project defines itself. This definition phenomenon is
maybe more visible when the discussions touch
different knowledge domains, as it happens to be the
case for AI evaluation tools.
AI academicians and practitioners are in the
beginning of some sort of consensus regarding what
to evaluate in AI systems, and when and by whom.
The design of an AI evaluation tool is therefore a
good candidate for heated discussions involving the
interaction of concepts from different fields and
Oliveira, D. and Dalkir, K.
Knowledge Capture for the Design of a Technology Assessment Tool.
DOI: 10.5220/0011551400003335
In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 2: KEOD, pages 185-192
ISBN: 978-989-758-614-9; ISSN: 2184-3228
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
having different impact levels on different people at
different stages of the AI lifecycle. This study intends
to help the creation of a support structure to foster the
design of a technology assessment tool. It can be
particularly helpful for business owners to develop AI
assessment tools.
Documentation in the design phase may capture
knowledge at its state-of-the-art portrait at that time.
It may capture motivation definition. The capture of
the state of the knowledge at the design phase might
help the end product evolve throughout time, as that
knowledge also evolves, because dependencies on
outdated knowledge can be more quickly identified.
The capture of the motivation behind the project, in
addition to increasing its transparency levels, might
also help the end product evolve, as this product
acceptability increases, for instance. In this sense,
documentation in the design phase might help
awarding the end product a continuous innovation
flow, where incremental developments have their
barriers lowered.
In the AI ecosystem, technology and approaches
evolve quickly and so does the acceptability of the
resulting products. The field is the perfect candidate
for the adoption of documentation facilitating a
continuous innovation flow.
Richards et al. (2020) argue that the diversity of
information needs that different stakeholders might
have makes it impossible that one single document
addresses all needs in a consumable format, even
within the same domain or organization.
For example, Mitchell et al. (2019) have
suggested a documentation paradigm to describe a
machine learning model. In the short documents
produced according to this documentation paradigm,
named “model cards”, performing characteristics of
the model should be conveyed so that potential users
can understand the systematic impacts of the model
before its deployment. Information such as type of
model, intended use cases, attributes for which model
performance may vary, measures of model
performance, as well as the motivation behind chosen
performance metrics, group definitions, and other
relevant factors should be included. Mitchell et al.
(2019) state the tool intends to help stakeholders to
compare candidate models, understand the limits of
each model and better decide on which model is more
suitable for a given situation. In practice, the
definition of stakeholders in this case is somehow
limited. While the tool should “aid policy makers and
regulators on questions to ask of a model, and known
benchmarks around the suitability of a model in a
given setting” ([p.2]), the target audience is
developers, particularly those interested in including
the model in a larger technological solution. For
Richards et al. (2020), if the documentation is to be
useful, it has to be tailored to their target audience and
to the use this target audience is to make of the
product. Indeed, while in Mitchell et al. (2019) there
is a concern regarding the length of the document (the
models cards should be “short”), in Richards et al.
(2020), the perspective of reporting is changed to suit
the needs of developers that would include models in
a larger technological solution: instead of reporting
characteristics of the model, characteristics of the AI
services, that could include many models, are
It is therefore reasonable to expect that the
documentation support needed for reporting
characteristics of an AI model or an AI service should
be different from the documentation support needed
for assessing the suitability of that AI model or
service. This assessment should evaluate the
alignment of that model or service with other criteria,
for example, the policies and practices in an
No documentation approach seems to cover the
whole machine learning cycle, neither to address all
the needs of all audiences (Garbin & Marques, 2022;
Laato et al., 2021).
2.1 Technology Assessment
Technology assessment involves, but is not limited to,
approaches and tools that allow:
The evaluation of the suitability of a
technological solution to a particular situation or
business need;
As mentioned, the alignment of the
technological solution with the policies and
practices of the organization;
The evaluation of positive and negative,
intended and unintended, current and future
impact on the situation, people, environment
and other technological solutions;
The comparison of one technological solution to
In a larger spectrum, the comparison among
approaches or technologies.
KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development
2.2 Artificial Intelligence Applications
AI applications may have high positive impact, but
they also have a great risk of generating negative
impact. AI applications may “violate privacy,
discriminate, avoid accountability, manipulate and
misinform public opinion, and be used for
surveillance” (Janssen et al., 2020), among other
Every professional involved in the AI cycle has a
responsibility towards increasing AI transparency
and limiting its potential to cause harm, be they AI
producers, regulators or executive board members
and managers of organizations of organizations
making use of AI (Laato et al., 2021). On one hand, it
is necessary for AI producers to better report the
characteristics, uses and limitations of AI models and
services. On the other, well-designed and well-suited
governance approaches to AI are necessary, to define
and monitor its potential negative implications and
limit those implications with effective and timely
responses to incidents.
AI risk assessments are necessary at many levels.
At the level of application domains and at the
institutional level (Winfield & Jirotka, 2018) and at
the level of individual systems (Janssen et al., 2020;
Winfield & Jirotka, 2018). Even if the AI solution in
question has an explainable AI approach, what to
explain and how to explain it might differ from one
domain to another and from one organization to
another (Laato et al., 2021). In addition, solutions
containing explainable AI modules still must be
monitored, as “blindly trusting findings from any
usability research in the XAI field would be
counterproductive due to the novelty and formative
state of the research area” (Laato et al., 2021, p. 20)
In each domain or organization, regulations,
culture and then policies, principles and procedures
are mechanisms for the establishment of thresholds of
acceptable behavior, mechanisms that both influence
and are influenced by societal expectations, norms
and values (Janssen et al., 2020). How can these
mechanisms be used for the creation of AI technology
solutions assessment tools? An approach and a tool
example from Knowledge Management research and
practice follows.
Knowledge Management concerns all questions
regarding the acquisition, the development, the
sharing, the exploitation, and protection of
knowledge (Dalkir, 2011).
Applied Knowledge Management is about the
development and tailoring of initiatives and tools
from Knowledge Management regarding a particular
field or activity.
3.1 Applied Knowledge Management
and Technology Assessment
The creation of approaches and tools is some ways no
different from other creativity endeavour in a
business environment. The ideas to be generated need
motivation, expertise and creative skills at their origin
and are required to be “appropriate, useful and
actionable” (Amabile, 1998, p. 79). Creative work is
often expected to be developed in groups (Hennessey
& Amabile, 2010) and work involving knowledge is
often linked to connectedness (Nahapiet, 2009). It is
possible that collaborative work increases the
flexibility and robustness of the solution. In any case,
it includes different perspectives and has the potential
to increase buy-in (Oliveira, 2022).
Applied Knowledge Management can support this
process by removing possible roadblocks and
otherwise creating conducive conditions so that better
goals can be attained more quickly, in addition to
providing individuals, groups and organizations with
a positive experience.
3.2 Knowledge Management and
Artificial Intelligence
Knowledge management around the evaluation of
technological solutions using artificial intelligence
tends to raise challenges that may not be raised in the
evaluation of other technologies. Some of these
challenges are:
The multidisciplinarity of fields required for the
evaluation. Portraits of Artificial intelligence
solutions have raised social, economic,
technological, linguistic, ethical, legal,
management and philosophical issues, to name
only some;
The global nature of collaboration: research
from academia and from companies around the
world are mutually influenced by new
developments in the field;
The field is still in its early stages.
Knowledge management initiatives supporting the
development of technology evaluation approaches
and tools for technological solutions involving
artificial intelligence must then take into account
Knowledge Capture for the Design of a Technology Assessment Tool
collaborative work among professionals with a
plurality of backgrounds and a high level of
knowledge acquisition and development.
3.3 Knowledge Development and
Development, sharing and exploitation of knowledge
are processes strongly related. Knowledge
development is associated with innovation, as the
creation of new knowledge has the potential to propel
the organization into new venues. While the
development of knowledge or of new ideas can be
done individually, more and more frequently this
process is undertaken in groups (Carrier & Gélinas,
2011; Fisher & Amabile, 2008). Knowledge sharing
is then a process that influences knowledge
development. Among other reasons, knowers might
share developed knowledge in order to validate this
knowledge (Mokyr, 2000), a process that also occurs
with knowledge acquired by an individual outside the
organization. Knowledge validation is necessary for
the subsequent application of this knowledge. Once
the knowledge has been embedded in processes,
services or products, it can be said to be exploited. In
the case of the evaluation of technological solutions
involving Artificial intelligence, knowledge
surrounding artificial intelligence, technology
evaluation and relating themes must be sought outside
the organization or developed internally and then
validated. These processes might occur before or
during the process of design of an actual technology
evaluation approach or tool.
3.3.1 Supporting Knowledge Acquisition
Knowledge from outside the organization can be
acquired through a structured organizational
initiative, but it can also enter the organization
through an employee that acquired that knowledge on
their own (Shoham & Hasgall, 2005). This employee
may act as a sponsor of this knowledge and advocate
its integration into the organizational knowledge.
There are many initiatives that can support
knowledge acquisition. Direct support can include
providing access to academic resources or training
and allowing employees time to explore those
resources. The knowledge acquisition process can
also be supported indirectly through the
organizational endorsement of the whole knowledge
management cycle, particularly development, sharing
and exploitation stages. If employees are allowed and
encouraged to validate knowledge externally
acquired, they will feel also encouraged to seek future
acquisition of knowledge.
One important element when validating
knowledge that was acquired outside the organization
is to acknowledge its provenance. Provenance holds
a symbolic weight that might be useful when
advocating for the acquired knowledge. This
symbolic weight might indicate, among other aspects,
maturity of scholarship, interdisciplinary points of
view, importance of the subject or the practical
potential of the knowledge in question. It is therefore
interesting to include provenance in knowledge
management tools designed to support the validation
of acquired knowledge.
3.3.2 Supporting Knowledge Development
Knowledge developed inside the organization might
combine acquired knowledge with previously
internally developed knowledge. Therefore,
knowledge management tools supporting knowledge
development should include the elaboration of the
new idea or statement and the possibility of
mentioning the provenance of both externally
acquired and internally developed knowledge.
3.3.3 Supporting Knowledge Application
Once a particular knowledge claim has been validated
(McElroy, 2011), it is then time to validate the
application of this same knowledge claim. Statements
explaining the application of knowledge tend to be
prescriptive and respect practical constraints. They
are therefore different in nature from the statements
describing the knowledge at their origins, which can
be more abstract or general.
In the design of an evaluation tool, the
presentation of the knowledge acquisition or
development statement beside the knowledge
application statement allows the reader to understand
the reasoning behind the application statement and
imply the organizational constraints that were
considered along with the knowledge acquired or
developed. It is the knowledge about knowledge, or
metaknowledge, helping the understanding of the
knowledge itself.
The promotion of the understanding of the design
process, beyond the end result, is one of the most
important elements in supporting the creation of
evaluation approaches and tools of technological
solutions involving artificial intelligence.
As the technology evolves and more of its impacts
and possible mitigation solutions are known, it is
important to facilitate the identification of which
areas of the evaluation tool are less current or
KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development
applicable and should be revised. Therefore, while the
understanding of the design process as a whole is to
be supported, the understanding of the design process
of each application statement is equally important. It
is also important to consider that the team charged
with the revision of the evaluation tool might be
considerably different from the team that developed
the tool. Facilitating the granular understanding of the
design process supports the updating process of the
evaluation tool in general and the increasing diversity
in the revision team in particular.
The support to the granular understanding of the
design process reduces the need for the revision team
to understand all the aspects of the project. This
facilitates the collaboration with experts in different
domains and reduces the pressure on the tacit
knowledge of the team members.
3.3.4 Supporting Explicit and Tacit Kinds of
The term “tacit knowledge” refers to knowledge that
has not been expressed in any kind of language
(Nonaka & Takeuchi, 1995). “Explicit knowledge”
addresses the knowledge that has been articulated to
the extent that it can be understood without needing
direct access to the holder of that knowledge. If a
continuum of the media holding knowledge is
considered, human minds would be on one extreme
whereas documents would be on the other (Oliveira
et al., 2021).
Explicit knowledge can be supported through
fields where the provenance of the knowledge claim
can be indicated. Supporting tacit knowledge requires
different strategies. One of those is a field where the
name of an employee sponsoring the knowledge can
be codified, as well as the name of an employee
endorsing the proposition. This strategy credits
employees with their efforts in the design of the
evaluation tool, for instance, and carries the symbolic
weight of their expertise.
Documentation, in most technological solutions,
focuses on the resulting tool. It is intended to
accompany the tool and help client developers make
good use of the tool. This documentation will usually
cover only the application of the knowledge claims
that have been validated in the design of the tool. The
documentation would not articulate the knowledge
claims nor describe the knowledge and processes
involved in validating those claims. In other words, it
would only present the knowledge itself, and not the
metaknowledge surrounding the technological
solution. After all, the aim of the documentation is to
support use of the tool, not necessarily the
development of the knowledge involved in designing
the tool.
Transparency in AI applications require that
codification efforts for the client developer go a little
further, both in terms of the knowledge and of the
metaknowledge surrounding the solution. In terms of
the knowledge, AI documentation developed by its
producer should cover how the evaluation the AI
model or service went through before it was made
available (Mitchell et al., 2019; Richards et al., 2020),
as a part of its quality control or application limits
definition. In terms of metaknowledge, the
documentation should cover the motivation behind
the choice of the metrics used to evaluate the AI
application (Mitchell et al., 2019). There is then a
need to promote codification efforts of this
metaknowledge, even for the documentation intended
for client developers’ use. The process supported by
the documentation suggested in this communication,
however, is the assessment of AI applications
performed by the business owner, or the first client of
AI applications.
4.1 The Business Owner’s Assessment
The business owner’s assessment team should be
composed of developers and members from other
functions in the business working together. Some
portions of the assessment would require more
technical expertise, while other portions would need
a broader view of the business to assess impact and
alignment, but most of the assessment will need
collaboration among professionals of different
backgrounds. The AI application would be evaluated
and compared with other applications with respect to
its technical approaches, but the alignment of the AI
application with the domain and the business culture
and with the policies and regulations surrounding the
business and the intended application environment
must also be ensured.
Reaching coherent decisions in this diverse
knowledge environment is a challenge, particularly if
the members of the assessment team change during
the assessment process. Documentation supporting
the knowledge acquisition, development and
Knowledge Capture for the Design of a Technology Assessment Tool
application processes would help the assessment team
remain coherent throughout the assessment. Along
the same lines, this documentation can be especially
helpful when the organization is ready to transform
the ad-hoc AI assessment process into a structured
and more established one and starts the design of an
AI assessment tool to guide the process.
4.2 Documentation Granularity at the
Level of Knowledge Claims
When a technology assessment tool is designed, the
knowledge claims surrounding the tool are validated
or rejected. The validated claims will join the design
process, and they will most likely be embedded in the
resulting tool.
In the case of the design of an AI assessment tool,
one approach would be to have the documentation
cover the assessment tool as a whole. This approach
gives a broad view of the assessment tool as is
probably the best option for the purposes of training
of new members of the assessment team or to present
the work of the assessment team to other departments
of the organization.
A more granular approach is however necessary
when knowledge surrounding AI evolves and
knowledge claims have to be revaluated. In this case,
it is interesting to quickly and unambiguously identify
the knowledge claims that might be affected by new
knowledge. In this way, barriers for a continuous
innovation flow are lowered and the updating process
of the assessment tool is facilitated.
In these codification efforts aiming at a
continuous innovation flow, the connection between
knowledge and metaknowledge must be made clear.
The documentation efforts should present which
portions of the end product are connected to which
knowledge claims, and the knowledge surrounding
the validation of those claims. Some documentation
of the social capital, “the sum of the actual and
potential resources embedded within, available
through, and derived from the network of
relationships possessed by an individual or social
unit” (Nahapiet & Ghoshal, 1998, p. 243), involved
would also help understand the importance of
knowledge claims. The documentation suggested in
this communication is part of the codification efforts
that support the evolution of the knowledge
surrounding AI applications. The documentation
suggested enforces codification efforts of the
knowledge claims validation process, covering the
knowledge management phases of acquisition,
development and application and both tacit and
explicit knowledge.
An AI assessment tool encompasses a number of
criteria used to identify the degree in which a
particular AI application presents interesting features.
Each criterion comes from a knowledge claim that
was validated. The documentation suggested captures
aspects of the knowledge claim validation process.
The principle is the expression of the knowledge
The motivation shows the context in which the
knowledge claim was acquired or developed in the
The academic / legal references present artifacts
of external knowledge, such as journal articles and
proceedings, books and book chapters, law text and
somehow captured (in documents, emails, audio or
video files, for example) legal advice.
The mentions field provides a space for a
description of the knowledge claim internal
validation process: the mention of the knowledge
claim in conferences, formal or informal discussions
in which members of the assessment team or
executive board members took part.
The previous use field offers the possibility to
codify the identification of the knowledge claim in
benchmarking efforts, either internal or external to
the organization.
The criterion is the short sentence that is an
actual part of the assessment tool. It aims to assess a
particular aspect of the AI application. It is the actual
expression of the knowledge claim in the assessment
The application field offers a space for
alternative ways to express practical aspects of the
knowledge claim.
The fields proposed by and seconded by capture
a little of the social capital surrounding the
knowledge claim validation process, as they should
present the names of assessment team members that
sponsored the knowledge claim.
The decision field adds to the organizational
memory as it captures the result of the knowledge
claim validation process, whether it was retained,
rejected or if the group has not reached a decision
about it yet.
KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development
Table 1: Fields of the suggested documentation.
sheet field
KM cycle process or
kind of knowledge
Principle The knowledge
acquisition or
Motivation The inspiration or
reasoning behind
the knowledge
development or
Academic /
artifacts supporting
the knowledge
acquisition or
Mentions Mentions of the
knowledge claim
in the media,
conferences or
acquisition or
Previous use Presence of the
knowledge claim
in a benchmarked
assessment tool
Criterion The knowledge
claim applied to a
Application More context
about the
application of the
knowledge claim
Proposed by Employee
advocating for the
knowledge claim
Tacit knowledge
Seconded by Employee
supporting the
e claim
Tacit knowledge
Decision The result of the
knowledge claim
Documentation helps to foster transparency. To build
trust in Artificial Intelligence solutions in general,
documentation is needed in many levels (Winfield &
Jirotka, 2018) and in many steps of the AI cycle
(Richards et al., 2020).
This communication explored the documentation
to be developed by the business owner regarding the
assessment of AI applications. Technological
assessment, in general, is influenced by
characteristics of the application domain, of the
organization and of the category of the solution in
question. Intellectual work is then necessary to make
sure these characteristics are included in the
assessment tool design. This intellectual work has to
also be collaborative, as expertise from different
backgrounds is necessary to evaluate technological
solutions not only from a technological viewpoint,
but also from the organization’s mission perspective,
in addition to a management perspective.
The negotiation process of what to assess and how
can be seen as a knowledge process. In this
knowledge process, a knowledge claim is advocated,
supported, defended, discussed, sponsored, rejected
or, in some cases, just left aside until a consensus
among team members can be reached.
Supporting the validation process of knowledge
claims during the design of assessment tools has the
benefit of providing a map of the knowledge
dependencies of the end product, in this case, the
technology assessment tool.
The diversity of knowledge involved in the design
of a technology assessment tool already justifies the
documentation support of the validation process of
knowledge claims. However, the design of a
technology assessment tool for an AI application also
involves the consideration of multifaceted impacts,
that have to be considered under the light of different
disciplines, which adds to the complexity of the
knowledge claim validation process. In addition, AI
technology and the understanding of its impacts
evolve quickly. In this scenario, being able to quickly
identify knowledge dependencies on outdated
knowledge helps to keep documentation updated, by
triggering a new knowledge claim validation process.
For these reasons, a template to codify aspects of
the knowledge claim validation process is suggested.
The document provides fields that capture elements
of the knowledge management steps of knowledge
acquisition, development and application. It also
fosters ways to capture the transformation of tacit
knowledge into explicit knowledge and of the social
capital involved in the knowledge claim validation
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