Toward supporting decision-making under uncertainty in digital humanities with progressive visualization

Abstract

Digital Humanities (DH) research and practice is subject to uncertainty during the life cycle of any project. Even in non dataoriented cases, analysts and other stakeholders need to make decisions without being aware of the level of uncertainty associated to the data being transformed by the computational tools used to enable the kind of novel work of humanists pursued within DH. We examine in this paper the literature that have characterized the types and sources of uncertainty in other fields, with the intent of establishing a foundation upon which build novel computational tools supporting the decision-making under uncertainty processes that DH is currently facing. We propose the use of progressive visual analytics as a feasible means to manage decision-making under uncertainty, which may help tackling some challenges related to the elimination or mitigation of uncertainty in DH, that otherwise would tamper the quality of the yielded results.

Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

“…Uncertainty Sources: Given these different datafication techniques, uncertainty is seeping into cultural collection databases either through the historical object information itself (i.e., through the accumulated guesses, estimates, or omissions of former collectors and curators), it can be introduced through digitization procedures of analogue object information (OCR, transcription, database creation), uncertainty can be introduced by feature extraction (due to largely probabilistic algorithmic recognition and identification methods), or through processes of human sensemaking, interpretation, and categorical annotation. Later on, this uncertainty "propagates" through the representational system, and can get both omitted or further amplified by design choices of data modelling, data processing, data visualization, and obviously also by complex procedures of human interpretation [21][22][23][24].…”

Towards A Taxonomy of Uncertainties: Analysing Sources of Spatio-Temporal Uncertainty on the Example of Non-Standard German Corpora

“…While some have addressed the specific possibilities of encoding and modelling uncertainty in humanities data [41], others have discussed the differentiation between humanistic "fact" and interpretation that shapes the nature of humanistic research questions and attitudes towards sources [42]. Further issues relevant to the DH field were also discussed in different studies in a special track on "Uncertainty in Digital Humanities" at the recent Conference of Technological Ecosystems for Enhancing Multiculturality (TEEM) in 2018 [43] (see also [25,42,44,45], etc.). The concept of uncertainty in relation to data driven innovation (DDI)-the production of innovative outputs from data-has more-specifically been addressed in [46], who urge the re-thinking and re-organising of views on data collection in the light of open science and cross-organisational collaborations to enable new designs of DDI networks for dealing with aspects of heterogeneity in data.…”

“…[23]), who are developing their own set of metrics based on a distinction of epistemic uncertainty (systematic uncertainty; reducible) and aleatory (inherent uncertainty; irreducible) as proposed by [24] (cf. [25]).…”

“…The ProvideDH project ( [23,25]) is implemented at the University of Salamanca (GrialUSAL) (ES) in collaboration with exploration space @ ACDH-OeAW Austrian Academy of Sciences (AT), Trinity College Dublin (IE) and the Supercomputing Centre Poznan (PL). The project is also run within a digital humanities environment and seeks to propose and develop innovative progressive visualisation tools that track and convey the degree of uncertainty of a given (humanities) dataset during its evolution, and how these data are affected by different computational models applied.…”

“…The different sources of uncertainty introduced over the course of time and affecting the DH practice are made visible and can be assessed by the researcher, ultimately supporting the decision-making process (cf. [25]). Tools are developed for different use-cases based on humanities data (XML/TEI) (the 1641 depositions) and scenarios (according the Open Innovation Research Infrastructure Design © ewv 2017), and aim to enable collaborative and visual annotation in an uncertainty-aware context (cf.…”