IEEE Systems, Man and Cybernetics Magazine - July 2022 - 33

Generally, uncertainty can be categorized into two
main groups: epistemic and aleatoric uncertainty [53],
[54]. Hullermeier and Waegeman [55] have comprehensively
studied both aleatoric and epistemic uncertainties.
Epistemic uncertainty (also known as systematic)
relates to uncertainty occurring due to variability in
the model parameters for a specific data set. This
uncertainty can be mitigated by using more data. This
type of uncertainty is absent in a real production environment
because the deployment stage usually covers
the choice of a single solution, i.e., the model that
yields the maximum posterior probability for a given
data set. The second category is aleatoric uncertainty
(also known as statistical), which relates to the inherent
uncertainty in the data. This category of uncertainty
cannot be mitigated by applying more data.
Therefore, it can be said that aleatoric uncertainty
measures noise, which is a fundamental uncertainty,
while unlike aleatoric, epistemic is a reducible uncertainty.
Figure 3 represents a schematic view of different
uncertainties occurring in handcrafted conventional
ML and DL during medical image classification.
As indicated in Figure 3, medical predictions should
be rationalized and given various circumstances by adding
an extra layer to explore uncertainty. Given the
uncertainty of their predictive predictions, the assessment
of handcrafted conventional ML- and DL-based
CDSSs provide compliance with professional principles
of medical outcome analysis. This can help CDSSs refrain
from giving predictions with high uncertainty or show
places in data and models where uncertainty is high.
Decision Boundary
Epistemic
Uncertainty
(a)
(c)
(a)
(b)
Malignant
(b)
Aleatoric
Uncertainty
Therefore, UQ can deal with some concerns, such as bias
and robustness of the proposed new techniques resulting
from the preliminary design of these new technologies.
Consequently, it can be concluded that having methods
that deal with uncertainty and increasing the level of
trust of clinical groups can solve technical challenges.
Thus, we require an actionable approach to formulate
these uncertainties. Uncertainty is an obvious candidate,
either for accepting or rejecting while making predictions.
As a result, we might refrain from providing an
answer (a choice) when predictions are very uncertain
[54]. There are various rejection functions, even applying
uncertainty as to the rejector. The prime disadvantage of
this technique is that it is essential to train the rejector
with the classification method and requires admission to
the model's internals. Although actionable uncertainty is
a path to pursuing algorithmic accuracy accountability,
this becomes a setback when one is not in control of all
components and/or steps in the data product. Recently,
large applications based on DL made the reinterpretation
of prevailing techniques, including dropout or stochastic
mechanisms such as Monte Carlo strategies [56], broaden
the utilization of such procedures to address uncertainty
in DL models. Therefore, applying various UQ methods
enhances the quality of decisions.
Uncertainties in ML/DL
Using a wide variety of AI methods can lead to having
automated decision-making processes. As clearly discussed
in [15], the possibility of explaining how an automated
decision is made to users may reduce their
Benign
(d)
(c)
Label Uncertainty
Out-of-Distribution Sample
Figure 3. A schematic view of different uncertainties that can occur in handcrafted conventional ML- and
DL-based medical image classification (pictured is an example of skin cancer): (a) benign, (b) malignant,
(c) malignant, and (d) radiation burn [96].
July 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 33
(d)

IEEE Systems, Man and Cybernetics Magazine - July 2022

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