Trusted Data Forever: Is AI the Answer?
Emanuele Frontoni 1, Marina Paolanti1, Tracey P. Lauriault2, Michael Stiber3, Luciana Duranti4
and Muhammad Abdul-Mageed5
1
VRAI Vision Robotics and Artificial Intelligence Lab, University of Macerata, Italy
Critical Media and Big Data Lab, Carleton University, Ottawa, ON K1S 5B6, Canada
3
Intelligent Networks Lab, University of Washington Bothell, WA, USA
4
InterPARES Lab, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
5
NLP and ML Lab, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
2
Abstract
Archival institutions and programs worldwide work to ensure that the records of governments, organizations, communities,
and individuals are preserved for future generations as cultural heritage, as sources of rights, and as vehicles for holding the
past accountable and to inform the future. This commitment is guaranteed through the adoption of strategic and technical
measures for the long-term preservation of digital assets in any medium and form — textual, visual, or aural. Public and
private archives are the largest providers of data big and small in the world and collectively host yottabytes of trusted
data, to be preserved forever. Several aspects of retention and preservation, arrangement and description, management and
administrations, and access and use are still open to improvement. In particular, recent advances in Artificial Intelligence (AI)
open the discussion as to whether AI can support the ongoing availability and accessibility of trustworthy public records. This
paper presents preliminary results of the InterPARES Trust AI (“I Trust AI") international research partnership, which aims to
(1) identify and develop specific AI technologies to address critical records and archives challenges; (2) determine the benefits
and risks of employing AI technologies on records and archives; (3) ensure that archival concepts and principles inform the
development of responsible AI; and (4) validate outcomes through a conglomerate of case studies and demonstrations.
Keywords
Artificial Intelligence, Machine Learning, Deep Learning, Archives, Trustworthiness
1. Introduction
Archival institutions and programs worldwide work to
ensure that the records of governments, organizations,
communities, and individuals are preserved for future
generations as cultural heritage, as sources of rights, to
hold the past accountable, and as evidence to inform future plans. A record – or archival document – is any
document (i.e. information affixed to a medium, with
stable content and fixed form) made or received in the
course of an activity, and kept for further action or reference. Because of the circumstances of its creation a
record is a natural by-product of activity, is related to
all the other records that participate in the same activity,
are impartial with respect to the questions that future
researchers will ask of them, and are authentic as instruments of activity. This is why records are inherently
Published in the Workshop Proceedings of the EDBT/ICDT 2022 Joint
Conference (March 29-April 1, 2022), Edinburgh, UK
emanuele.frontoni@unimc.it (E. Frontoni);
marina.paolanti@unimc.it (M. Paolanti);
Tracey.Lauriault@carleton.ca (T. P. Lauriault); stiber@uw.edu
(M. Stiber); Luciana.Duranti@ubc.ca (L. Duranti);
muhammad.mageed@ubc.ca (M. Abdul-Mageed)
0000-0002-8893-9244 (E. Frontoni); 0000-0002-5523-7174
(M. Paolanti); 0000-0003-1847-2738 (T. P. Lauriault);
0000-0002-1061-7667 (M. Stiber); 0000-0001-7895-1066 (L. Duranti);
0000-0002-8590-2040 (M. Abdul-Mageed)
© 2022 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
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trustworthy. Thus, their preservation must ensure that
any activity carried out on the records to identify, select,
organize, describe them and make them accessible to the
people at large must ensure that they remain trustworthy,
that is reliable (i.e. their content can be trusted), accurate
(i.e. the data in them are unchanged and unchangeable),
and authentic (i.e. their identity and integrity are intact).
This is particularly difficult in the digital environment
because the content, structure and form of records are
no longer inextricably linked as they used to be in the
traditional records environment [1] 1. The issue exists for
both digital and digitized records and is rendered more
serious by the sheer number and volume of records that
have accumulated overtime and are being created today
in a large variety of systems.
The InterPARES (International research on Permanent
Authentic Records in Electronic System) project has addressed these issues since 1990, focusing on current and
emerging technologies as they evolve and developing theory, methods, and frameworks that allow for the ongoing
preservation of the records resulting from the use of such
technologies 2. The latest iteration of InterPARES, I Trust
AI, funded, as previous projects, by the Social Sciences
and Humanities Research Council of Canada, but differs
1
“The Concept of Record in Interactive, Experiential and Dynamic Environments: The View of InterPARES”. Archival Science 6,
26-33.
2
www.InterPARES.org
Figure 1: PergaNet DL pipeline. PergaNet DL pipeline consists of three stages: classification of parchments recto/verso, the
detection of text, then the detection and recognition of the “signum tabellionis”. Firstly, a VGG16 Network trained on a dataset
of scanned parchments is needed to solve a classification task: recto/verso. After, the text in the image is detected. Then,
YOLOv3 was used to predict bounding box locations and classify these locations in one pass.
as it is not concerned with the records produced by a specific technology, but has the purpose of using AI to carry
out archival functions for the control in the long term of
all records, on any medium, and from any age, and to do
so in such a way that the trustworthiness of the records
remains protected and verifiable, and that the tools and
processes are transparent, unbiased, equitable, inclusive,
responsible (i.e. protecting autonomy and privacy) and
sustainable 3. There have been several projects looking at
AI in archives, but they typically look at a particular tool
in a specific context or even a single set of records, and
they tend to use off the shelf tools. The research question
that I Trust AI is being asked here is: “what would AI
look like if archival concepts, principles and methods
were to inform the development of AI tools?” What is
lacking is comprehensive, systematic research into the
use of AI to carry out the different archival functions in
an integrated way and ensure the continuing availability
of verifiable trustworthy records to prevent the erosion
of accountability, evidence, history and cultural heritage.
Thus, we are addressing the technological issues from
the perspective of archival theory, by integrating the
technology with complex human-oriented tools. The
objectives of I Trust AI are to 1. Identify specific AI technologies that can address critical records and archives
3
www.interparestrustai.org
challenges; 2.Determine the benefits and risks of using
AI technologies on records and archives; 3. Ensure that
archival concepts and principles inform the development
of responsible AI; and 4. Validate outcomes from Objective 3 through case studies and demonstrations. Our
approach is two-pronged, comprising the practical and
immediate need to address large-scale existing problems,
and the longer-term need to have AI-based tools that are
reliably applicable to future problems. Our short-term
approach focuses on identifying high impact problems
and limitations in records and archives functions, and applying AI to improve the situation. This will be achieved
via collaboration between records and archival scientists and professionals and AI researchers and industry
experts. Our long-term approach focuses on identifying the tools that records and archives specialists will
need in the future to flexibly address their ever-changing
needs. This includes decision support and, once decisions
are made, rapid implementation of AI-based solutions to
those needs.
The I Trust AI project is a multinational interdisciplinary endeavour, and this means that our first effort
must be to understand each other, starting with the language we use. For example, archival professionals talk
about records, while computer scientists and AI professionals talk about data. To the former data are the small-
Table 1
Digitalised Heritage Data
Digitalised Heritage Data
Fondo Ufficio italiano brevetti e marchi, Trademarks series: volumes with trademark registrations
Official collection of laws and decrees
Fund A5G (First World War): files with various documents (reports, reports, correspondence)
Special collections (documents declassified under the Renzi and Prodi Directives): reports, reports, circulars
Judgments of military courts
Various photographic funds
Digitised study room inventories
National Archives of the US
est meaningful unit of information in a record. To an AI
specialist, data are arrangements of information (possibly
in a database), be these facts or not, regardless of their
size, nature and form.. Thus, for the purposes of this
paper, which is directed to data analytics specialists, we
will use the term data.
Public and private archives are the largest providers of
data big and small in the world as they collectively host
yottabytes of trusted data, to be preserved forever. Their
creators are organizations and individuals from myriad
sectors and disciplines, from public administration, to
academia and businesses of all kind (e.g. banking, engineering, architecture, gaming), and from Indigenous communities, civil society organizations, associations, and
virtual communities. Table 1 reports the data quantity.
The Italian State Central Archives, for example, has Data
quantity (67 TB) of “digital objects” stored by the ACS
linked to the typology (digitised heritage data (in TIFF)).
The National Archives of the US has 1,323 terabytes of
electronic data. This paper presents some preliminary
results of I Trust AI international research partnership
and is organized as follows: Section 2 provides a general
discussion of AI and its subsets; Section 3 describes three
of the about forty studies that are now in course; and
Section 4 presents an overview of the kind of studies that
are being pursued at this time and a conclusion.
2. Artificial Intelligence and Deep
Learning
There are various definitions of what AI is. For example, Russel and Norvig [2] define AI as a field focused
on the study of intelligent agents that perceive their environment and take actions to maximize their chance of
success at some goal. In recent times, however, AI has
been used much more widely to refer to any technology
where there is some level of automation especially resulting from the application of deep learning in various
domains. Deep learning (DL) is a sub-field of machine
learning (ML), which are both sub-fields of AI. Similar to
AI, ML is defined in several ways. A classical definition
Size
30 TB
15 TB
1 TB
2 TB
3 TB
2 TB
15 TB
1323 TB
of ML comes from Mitchell and Learning [3] who provide a procedural definition maintaining that a computer
program is said to learn from experience E with respect to
some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with
experience E. As to DL [4, 5], it is a class of ML methods
inspired by information processing in the human brain.
What makes DL powerful is its ability to automatically
learn useful representations from data. DL algorithms,
unlike classical ML methods, are not only able to learn
the mapping from representation to output but also to
learn the representation itself [6], thus alleviating the
need for costly human expertise in crafting features for
models. DL has achieved success in recent years in a
wider variety of applications in many domains involving
various types of data modalities such as language, speech,
image, and video.
As mentioned, DL mimics information processing in
the brain. This is possible by designing artificial neural
networks arranged in multiple layers (and hence the term
deep) that take input, attempt to learn a good representation of it, and map it to an output decision (e.g., is this
text in Greek or Latin?). The way these networks are
designed can vary, thus, various types of deep learning
architectures have been proposed. Two main types of
DL architectures have been quite successful. These are
recurrent neural networks (RNNs) (e.g., [7]), a family of
networks specializing in processing sequential data, and
convolutional neural networks (CNNs) [8], an architecture
specializing in data with a ‘grid-like’ typology (e.g., image data) [5]. More recent advances, however, abstract
away from these two main types toward more dynamic
networks such as the Transformer [9].
In general, ML and DL methods learn best when given
large amounts of labeled data (e.g., for a model that detects sensitive information, labels can be from the set
sensitive, not-sensitive). DL in particular is data-hungry
and tends to learn best given large amounts of labeled
data. This type of learning with labeled data is called supervised learning. It is also possible to work with smaller
labeled datasets. In these cases, training samples can
be grown iteratively exploiting unlabeled data based on
decisions from an initial model (self-training) or using
decisions from various initial models (co-training). This
is called semi-supervised learning [10, 11, 12]. The third
main-type of ML methods is unsupervised learning where
a model usually tries to cluster the data without access
to any labels. There are other paradigms such as distant
supervision where a model attempts to learn from surrogate cues in the data in absence of high-quality labels
(e.g., [13]). Self-supervised learning where real-world data
are turned into labeled data by masking certain regions
(e.g., removing some words or parts of an image) and
tasking a model to predict the identity of these masked
regions is currently a very successful approach. These
various methods of supervision can also be combined to
solve downstream tasks. For example, Zhang and AbdulMageed [14] combine self-supervised language models
with classical self-training methods to solve text classification problems. The next section will introduce three I
Trust AI studies.
from another, and examining how various data analytics
techniques might be usefully applied — understanding
what characteristics of these data might be usable to researchers Specifically, we are addressing the following
research questions:
What real-world and simulation ESCS data are
available to be preserved for access by researchers?
The answer to this varies greatly from locale to locale,
depending on technology in use, public policy, and controlling agency procedures. Moreover, for such data to
be available, we must understand privacy and security
risks associated with transferring them from their current
owners to a research environment, along with the risk of
misinterpreting them if they are decontextualized from
whatever tacit knowledge might exist within the owning
organizations. We are also considering pragmatic issues,
such as building a knowledge base of legal restrictions on
collection in various jurisdictions, formal processes for
collecting these data (such as data sharing agreements),
variation in the culture of practice surrounding such data,
3. Case Studies in I Trust AI
potential biases that might result from systematic differences in different areas’ capacity to collect and share data
3.1. Data from Emergency Services
(such as might arise from regional funding differences),
Communications Systems
and understanding the metadata and other information
One of the cornerstones of of public safety and societal (such as ESCS physical and operational structure and,
wellbeing is a reliable and comprehensive emergency generally, the policies and practices that determine what
services communications system (ESCS, such as 9-1-1 in and how data are generated and collected).
the US and Canada). Such systems can be considered to
encompass the organizations, electronic infrastructure, What are the challenges and benefits of discoverand policies and procedures that enable answering and ing knowledge patterns from historical ESCS data?
responding to emergency phone calls [15]. As might be These patterns will serve as clues to developing protocols
expected of systems that originate in analog, switched for ESCS managers to follow regarding data collection,
telephony, ESCS evolution into a digital system has re- and as clues for how these data can be applied for reuse.
sulted in a haphazard conglomeration of subsystems and We will consult with external stakeholders to seek advice
generally needs re-imagining as a modern technological and to run thought experiments using surveys, thinksolution. In the US, this change has been termed the aloud exercises, retrospective first-hand accounts, etc.
We will also examine historical records of disasters for
“Next Generation 911” (NG911) project.
A transformation such as NG911 once again re-casts which the preserved data are more complete than basic
ESCS as keystone information and communication tech- ESCS datasets.
nology, subject to all of the concerns of such systems:
cybersecurity, privacy, crisis preparedness, strategic and What other data/metadata associated with emeroperational decisions, etc. At the same time, it opens gency events are not part of the ESCS data stream?
up possibilities for data analytics to improve ESCS per- From our preliminary examination, typical ESCS data
formance, inform funding decisions, monitor the health currently involve lists of individual calls and information
of societies and their infrastructures, and serve as early directly associated with such calls (perhaps including full
warnings for natural and human made crises. This study or partial phone numbers, call categorization, GPS coordiconnects large- scale simulations of ESCS to historical nates, responder information, response times, etc.). What
data from ESCS operations to develop and document an these datasets do not directly include are events and data
understanding of how to preserve authentic, reliable data that are external to the call stream but are the reason for
that can be used for applications such as re-creation of such calls (traffic, weather, geopolitical events, and so on).
past events (as might be done to support training or to Some of these additional data may be present in other
explore the effects of changes in policies and procedures), sources of information in a format that can be reasonably
testing system operation in one locale based on data collected in tandem with call data. On the other hand, it
may be the case that other causal information must be
inferred from available data (and, of course, it may be the
case that a combination of inference and extraction from
other data streams could be useful). For the inferencing
task, we propose using an examination of simulation results and simulation artifact provenance information as
exemplars to develop a set of specifications for what an
AI-driven system would need to accomplish [16].
What are the roles of the disciplines of Archival
Science and Artificial Intelligence in building a central repository for ESCS data? Both the individual
fields of archival science and artificial intelligence, plus
their overlap or combination that could be considered
to fall within the realm of data science, have a number
of roles in the organization and interpretation of ESCS
data. We provide examples below from the application
of real-world data to simulations:
• Generating requirements for simulator design so
that simulation output matches real data in terms
of format, metadata, etc.
• Analyzing and comparing simulation output with
real- world data.
• Synthesizing ESCS data that match features of
real-world data as part of an overall ESCS simulation.
• Using real-world call data to drive a simulation
of an emergency response system, for example,
to allow a “replay” of a previous disaster or to
investigate how modifications to such a system
might produce different outcomes.
3.1.1. Progress
This work-in-progress is in its initial stages, preparing
for the point in time when we can begin collecting ESCS
data. Specifically, we are:
• working with a small set of external partners to
develop a general understanding of ESCS operations, policies, and procedures, as well as identifying which data exist;
• developing a process within our project for working with a selected ESCS management organization to build an understanding of their specific
operations and data;
• fleshing out a model data sharing agreement to
serve as a starting point for discussions surrounding transferring data to our research environment;
• consulting with our institutional review board
regarding the use of this particular set of human
data;
• and configuring a secure internal data storage
system.
Subsequently, we will prepare an initial case study in
which we will apply the above to a single locale: we
will go through all of the steps of understanding the
ESCS processes that produced the data, developing a data
sharing agreement, and collecting data and metadata.
3.2. Learning from Parchments
The digitization of historical parchments is extraordinarily convenient, as it allows easy access to the documents
from remote locations and removes the need for the possible adverse effects of their physical management and
access [17]. This arrangement is particularly suitable
to archives and museums who preserve such invaluable
historical documents whose contents are unpublished
and which, if damaged, cannot be fully restored by conventional tools, are difficult to read on the original, due
to high levels of damage and the delicate nature of the
material. Damaged parchments are notably prevalent
in archives all over the world [18]. Their digital representations reduce both damage and access issues by
providing users with the possibility of reading their contents at any moment, from remote locations, and without
necessitating the potentially harmful physical handling
of the document. Thus, the automatic analysis of digitized parchments has become an important research
topic in the fields of image and pattern recognition. It
has also been a considerable research issue for several
years, gaining attention recently because of the value that
maybe be unlocked by extracting the information stored
in historical documents [19]. Interest in applying AI/ML
to ancient image data analysis is becoming widespread,
and scientists are increasingly using this method as a
powerful and complex process for statistical inference.
Computer-based image analysis provides an objective
method of identifying visual content independently of
subjective personal interpretation, while potentially being more sensitive, consistent and accurate than physical
human analysis. Learned representations often result in
much better performance than hand-designed representations when it comes to these types of texts. Until now,
parchment analysis has required physical user interaction, which is very time consuming. Hence, the effective
automatic feature extraction competence of Deep Neural
Networks (DNNs) decreases the demand for a personal
physical extraction processes.
Considering the above, PergaNet is a lightweight DLbased system for the historical reconstructions of ancient
parchments is specifically designed and developed for
this type of analysis. The aim of PergaNet is to automate the analysis and processing of large volumes of
scanned parchments. This problem has not yet been
deeply investigated by the computer vision community
as parchment scanning technology is still novel, but it
has proven to be extremely effective for data recovery
from historical documents whose content is inaccessible
due to the deterioration of the medium. The proposed
approach aims to reduce hand-operated analysis while
using manual annotations as a form of continuous learning. The whole system however requires digital labour,
such as the manual tagging of large training data. Up
until now, large datasets remain necessary to boost the
performance of DL models, and manually verified data
will be used as continuous learning and maintained as
training datasets. PergaNet comprises three important
phases: the classification of parchments recto/verso, the
detection of text, and the detection and recognition of
the “sig,num tabellionis”. (i.e. the identifier of the author). PergaNet concerns not only the recognition and
classification of the objects present in the images, but
also their location. This I Trust AI study expands the
implementation of AI guided by archival institutions and
programs as this method could be used by many other
archives for different types of documents. The analysis
is based on data about the ordinary use by researchers
of this type of material and does not involve altering or
manipulating techniques aimed to generate data. This
provides actionable insights that are helpful to identify
text as documentary form and not as reading.
The DL pipeline is depicted in Figure 1. We chose
VGG16 Network [20] for its suitability and effectiveness
in image classification tasks and were inspired by the
work of Zhou et al. [21], in the way in which PergaNet
detects the text in the image. This phase allows for the
exclusion of the text on the parchment in the phase of
recognition of the signa. The DNN model chosen is EAST
for word detection [21]. Finally, a Convolutional Neural
Network has been employed for the signa detection. Our
approach uses YOLOv3 [22], an algorithm that processes
images in real time. We chose this algorithm because of
its efficiency in computational terms and for its precision
to detect and classify objects. The network is pre-trained
using COCO4, a publicly available dataset; this was a
choice made to reduce the need for a large amount of
training data, that would come with a high computational
cost.
3.3. Digital Twin Study
Spatial media [23] and spatial data infrastructures
(SDI) [24] have normalized as complex interconnected
global, regional, national, and personalized social and
technological systems of systems. Simply consider the
monitoring of climate at a global scale to inform the logistics of production chains, or to predict, preempt and
prevent disaster resulting from natural calamities on local
physical infrastructure or simply to report the temperature and humidity levels outside to your smart phone
4
https://cocodataset.org/#home
to help you plan your day. This occurs seamlessly in the
background of our daily activities and involves a vast
complex of sensors; databases; cloud computing centres;
telecommunication networks and the internet; standards;
code, software and platforms; people and institutions;
laws, regulation and policies; communication systems
and of course AI/ML [25].
An emerging subset of these spatial data infrastructures are digital twins (DTs). A DT is an ecosystem of
multi-dimensional and interoperable subsystems made
up of physical things in the real-world, digital versions
of those real things, synchronized data connections between them and the people, organizations and institutions involved in creating, managing, and using these5.
In terms of physical and real things consider a building
or a car manufacturing plant; a digital representation
of those things in a digital platform or an interactive
virtual reality game engine; with an internet of things
(IoT) system of sensors and databases that communicates
between the buildings or manufacturing processes in the
plant in real and near real time and the people and institutions that own and operate these. Contemporary
examples of DTs are the modeling and managing of the
construction of Sweden’s new high speed rail systems 6;
Hyundai car and ship manufacturing plants 7; and as part
of smart city strategies (see the submissions to the Infrastructure Canada Smart City Challenge 8). DTs originate
in the aerospace industry, first with NASA’s Apollo 13 in
the 1970s, although in that case it was a physical replica
to help troubleshoot issues of a ship in flight; and were
predominantly used in manufacturing and logistics [26]
Increasingly, DTs involve building information modeling (BIM) such as REvit a proprietary platform or the
OS BlenderBIM; whereby a building will be conceptualized and rendered into a 3-dimensional drawing with
attributes captured in a database; often replacing typical
blue prints. The BIM informs the construction of the
building; and is a record of it once completed. BIM renderings are increasingly being submitted as part of the
building permit approval process (BuildingSmart, 2020,
e-submission common guidelines for introducing BIM to
the building process 9) and are updated into as is BIMs for
ongoing operations. BIMs are also used to estimate mate5
CIMS, 2021,About page, what is a digital twin
https://canadasdigitaltwin.ca/about-2-2/
6
Pimental, K. 2019, Visualizing Sweden’s first high-speed railway with real-time technology, https://www.unrealengine.com/enUS/spotlights/visualizing-sweden-s-first-high-speed-railway-withreal-time-technology
7
Chang-Won, L., 2022, Hyundai Motor works with Unity to
build digital-twin of factory supported by metaverse platform;
https://www.ajudaily.com/view/20220107083928529
8
https://www.infrastructure.gc.ca/sc-vi/map-applications.php
9
https://www.buildingsmart.org/wpcontent/uploads/2020/08/e-submission-guidelines-PublishedTechnical-Report-RR-2020-1015-TR-1.pdf
Figure 2: Integrating diverse databases into BIM (Image created by Nico Arellano, CIMS 2021).
rial costs as they are interconnected with material vendor
databases and electrical and heating systems. BIMs interrelate with smart asset management systems (ASM)
which inform building maintenance and operations, and
monitor internal climate such as temperature, humidity,
air flow and quality; which are inputs for the AI/ML systems that remotely manage heating and cooling systems;
electricity use and consumption as well as maintenance
schedules and inform ongoing decision making.
In this I Trust AI Digital Twin case study we collaborate
with researchers working on the Imagining the Canada
Digital Twin (ICDT)10 project funded by the Canadian
New Frontiers in Research Fund 11 which proposes a national, inclusive, and multidisciplinary research consortium for the creation of a technical, cultural, and ethical
framework to build and govern the technology, data and
institutional arrangements of Canada’s DT. ICDT focuses
on the built environment, concentrating on the Architecture, Engineering, Construction, and Owner Operator
(AECOO) industry. ICDT is led by the Carleton Immersive Media Studio (CIMS) 12 developing a DT prototype of
the Montréal-Ottawa-Toronto corridor using a simulated,
distributed server network. The research study involves
an interdisciplinary research team of architects, data scientists, engineers, building scientists, archival professionals and critical data studies scholars, from Carleton
University CA, Luleå University of Technology SE, the
Swedish Transport Administration and the University of
Florence IT who will develop a Use and Creation preservation case study. The Study aims to preserve the DT of
campus buildings and structures created as part of SUSTAIN (https://cims.carleton.ca/#/projects/Sustain) and
10
https://canadasdigitaltwin.ca/
https://www.sshrc-crsh.gc.ca/funding-financement/nfrffnfr/index-eng.aspx
12
https://cims.carleton.ca/#/home
11
the Carleton Digital Campus Innovation (DCI) 13 project
integrating Building Performance Simulation (BPS) technologies with BIM on a campus scale; building information management systems (BIM), Asset Management
Systems (AMS), visualizations of the digital structures in
the Unreal Game Engine, VR and modelling, AI/ML, and
Real-time data for decision making.
The Carleton Campus DT data of seven buildings belong to the University that must preserve these as official
records, which are used for operations by Facilities, Management and Planning (FMP) and are part of research
and development for the CIMS and SUSTAIN projects;
thus involving research data that must be managed and
deposited in a trusted digital repository.
The implications of this research are important to the
archival community who will increasingly have to ingest
complex record sets such as these, as well as create an
archival package to maintain the integrity of these complex interlinked DT systems through time. This study
will be one of the first globally to examine the preservation of a DT. Its research questions are: Can a digital
twin be preserved and what is required at the point of
creation to ensure that it can be? Can information about
the AI tools, automation and real time data involved in
this complex data, social and technological system be preserved, and how? And, what might be the role of AI/ML
be in terms of creating an archival package to ingest a
digital twin? The outputs of this research will provide
empirical data to meet the objectives of the I Trust AI
Project; and also provide Carleton University with the opportunity to test the preservation of Campus DT records
in its institutional archives. In the process, it will inform
the technology sectors involved in the creation of DTs,
so that they may build-in at the point of creation, the
13
https://www.cims.carleton.ca/#/projects/DigitalCampusInnovation
necessary bread crumbs for long term preservation.
4. Conclusions and Future Works
have been numerous calls to action to systematically explore the application of AI techniques to the records
and archives field, AI also currently faces major ethical
challenges that will benefit from an archival theory perspective, for instance in dealing with bias and personal
information. By exploring further the connections between AI and archives, this project is and will contribute
to the intellectual progress of both fields. The I Trust
AI project has generated a great amount of enthusiasm
among participant researchers (about 200) and partner
organizations (87), as well as organizations that do not
have the capacity to participate but look forward to outcomes they can use, because it deals with issues that are
already dramatically changing the way we act, behave
and think. We have a unique and essential contribution
to make, because we have the means of creating knowledge ensuring that digital data and records are controlled
and made accessible in a trustworthy, authentic form
wherever they are located; are promptly available when
needed; duly destroyed when required; and accessed only
by those who have a right to do so.
The studies presented above are only 3 of about 40 inprogress studies which cover a wide range of subjects and
issues, such as enterprise master data management, the
preservation of AI techniques as paradata, modelling an
AI-assisted digitization project, gamification of archival
experience for users, declassification of personal information using AI tools, and user approaches and behaviours
in accessing records and archives in the perspective of
AI. The challenges we are addressing with this project
have never before been systematically and globally dealt
with; it is enormous and fraught, but critical. While the
risks of using AI to solve the problems of managing the
ever-growing, ever-more-diverse bodies of public and
private records throughout their lifecycle, from creation
to preservation and access, are unknown, the risks of not
acting in concert to do so are unacceptable: loss of the
ability to secure people’s rights, of evidence of past acts
and facts to serve as a foundation for decision making,
and of historical memory.
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