Instrumentation & Measurement Magazine 25-7 - 4

indicators, not rarely the only elements used to decide about
scientific career advancement or research funding.
Post-truth Ideology
The ideology of post-truth-the term " post-truth " is relatively
new and was chosen as the " word of the year " in 2016
by the Oxford English Dictionary [10]-can be considered opposite
to dataism. A positive and constructive understanding
of this position, that is not the trivially self-contradictory " everything
is relative, " might be devised from the consideration
that no available evidence, however specific it is, provides a
certain support to a unique theory. Rather, different and incompatible
theories may account for the available data. This
position, of under-determination
of theory by evidence,
is sometimes presented as the
" Duhem-Quine thesis " [11]
and is in sharp contrast with
the dataist position according
to which " data speak for
themselves. " Starting from
the acknowledgment that absolute
truth is not accessible
to us, post-truth advocates
make the radical move to
claim that there is no truth at all. According to them, opinions
dominate on facts and emotions prevail on rationality.
Or perhaps, even more radically, post-truth ideology assumes
that there is nothing like facts free of interpretation, and that
rationality is only a superstructure of a particular culture developed
in a particular historical period: scientific knowledge
would only be a contingent manifestation of a societal organization
that has promoted competition through research. The
outcome is that scientific evidence is denied as such, according
to the principle that " there are no facts, only interpretations, "
or, as Goodman put it, that " the given [is] acknowledged as
taken " [12]. In this context, " alternative facts " and " fake news "
are generated, while technical competence is assimilated to an
ideology so that an expert conclusion is worth as much as that
of anyone else. While this kind of attitude is plausibly not new
in human societies, the phenomenon is now exponentially
growing, also due to the decline of traditional media and the
pervasiveness of social networks that promote social interest,
not truth, as the key condition to be widely followed.
In short, in response to the dataists claiming that models are
no longer necessary, thanks to the large amounts of data available,
the post-truth advocates argue essentially the opposite:
everything is a model.
A Possible Societal Evolution: The
Good, The Bad and The Ugly
Given the situation described above, three limit scenarios
could help us to better frame the possible society evolution:
a " bad " scenario, in which decisions are uncritically made by
data only; an " ugly " scenario, in which facts are neglected, the
very concept of evidence is refused, and opinions assume a
4
supreme value; and a " good " scenario, in which the data deluge
empowers individuals and society.
Despite their radical differences, both dataism and postMetrology
plays a strategic role as
a culture to appropriately obtain
information and knowledge
from data, while avoiding the
extremisms of dataism and
post-truth ideologies.
truth ideology can be seen as expressions of what we could
consider the dark side of decision making: apparently quick and
easy, and therefore seductive, ways to solve complex problems,
but that can actually cause even more serious problems
[6], [13]. In either case-only data or no data at all are used to
make decisions-by endorsing one of these positions, people
are pushed to find simplistic answers to complex problems.
And that our society is tackling really complex problems-
pandemic and climate change are only two examples-is hard
to deny. For sure, the uncertainty generated by complexity is
anxiogenic, but complexity
cannot be effectively dealt
with by reinterpreting the
objective conditions of the
problems: simplification is
effective only in the face of
complication, that is in the
subject, not complexity, that
is in the object.
In this light, the main flaw
of dataism is the black-box
structure of the decisionmaking
processes it deals with: they are effective until the
data are representative of the distribution from which the
data themselves are obtained, i.e., they are unbiased, and the
system that generates them is stationary, so that the training
data set and the newly acquired data set can be assumed to be
extracted from the same distribution. But both are bold conditions
to be guaranteed without some explicit knowledge
of the system. This is the role traditionally played by causal
explanations, that are not only highly efficient data compression
devices (compare the number of bits required to store a
formula representing a physical law with the size of a dataset
from which the same information provided by the formula
can be inferred by regression), but also tools able to provide
socially understandable and criticizable justifications for the
decisions that are made. Vice versa, the main flaw of post-truth
ideology is its lack of any intersubjective element: if opinions
are everything that has worth, truth loses any role as a quality
criterion, and the effectiveness of individual power remains
the only justification. Centuries of scientific and technological
development show that another way has been successfully
pursued and is still possible.
The Role of the Metrological Culture
Between Dataism and Post-truth
Ideology
Metrology is not only a scientific and technological body of
knowledge, but also, and first of all, a culture, related to a complex
and structured way to conceive the connection between
the empirical world and the information we produce about it.
The metrological culture [5] can, and should, play a positive
role in developing a widespread awareness about the relevance
IEEE Instrumentation & Measurement Magazine
October 2022
Instrumentation & Measurement Magazine 25-7

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