Instrumentation & Measurement Magazine 25-7 - 5

of intersubjectively validated information and processes to tackle
complex problems. Given the infrastructural condition of measurement
[14], the contributions that metrology could provide
are manifold. A possible starting point, inherited from semiotics,
is the Weaver's interpretation of Shannon's information
theory [15], in which communication problems are understood
as being constituted of three " levels " of subproblems:
◗ " Level A (the technical problem): how accurately can the
symbols of communication be transmitted?
◗ Level B (the semantic problem): how precisely do the
transmitted symbols convey the desired meaning?
◗ Level C (the effectiveness problem): how effectively does
the received meaning affect conduct in the desired way? "
This interpretation may be applied and generalized to
problems of data (and information, and knowledge) treatment,
in terms of [1]:
◗ a syntactic (i.e., " level A " ) problem: are data formally
correct (and therefore, in particular, clean, not missing, ...)?
This question focuses on data as syntactic entities, only
able to be identified as different from each other (zeroes
need only to remain different from ones);
◗ a semantic (i.e., " level B " ) problem: is the information
obtained from data representative? This question assumes
that information is data that refer to something and therefore
to which a meaning has been assigned;
◗ a pragmatic (i.e., " level C " ) problem: is the knowledge
obtained from information useful? This question assumes
that knowledge is useful information in a context,
provided that knowledge is justified true belief [16].
The distinction between data, information, and knowledge
is crucial here: the " deluge of data " is now a fact, but it does
not imply that there is also a deluge of information, and even
less of knowledge. But information and knowledge-not only
data-are needed to solve problems effectively. On this matter,
measurement, a fundamental process for acquiring data and
information on empirical properties, has a lot to say, and on
this matter, some metrological culture could, and should, be a
shared background for all members of our society.
A core element of what we mean by metrological culture is
related to the way information on the measurand is produced
from the data provided by a measuring instrument (i.e., instrument
indications) such as an alcohol thermometer. Indeed,
the position of the upper surface of the alcohol in the capillary
has a value in metres (or millimeters), not in kelvins (or degrees
Celsius). This position value (i.e., the data) is mapped to
a temperature value (i.e., the information) by means of the instrument
calibration: without the knowledge provided by the
calibration (e.g., in the form of a calibration curve), indication
values cannot be related to measurand values. It is important
to note that this knowledge is not entirely empirical, given that
it includes a model of the behavior of the instrument, at least
sufficient to assume that the calibration curve is valid even
though the measurement and the calibration were performed
at different times. Of course, this is a " low level " model, as
related only to the stability over time of the behavior of the
adopt ed data acquisition tool. Nevertheless, it is a model, and
October 2022
metrologists are well aware that it is critical to decide when it is
not valid anymore (and therefore, when the instrument needs
to be recalibrated), a situation that in Data Science corresponds
to assessing whether the data-generating process is not stationary
anymore.
This highlights the essential " being in the middle " of metrology,
between data and models, and therefore, in some
sense, between dataism and post-truth ideology: on the one
hand, measurement acquires data and does it in a purely empirical
way through measuring instruments; on the other hand, it
produces information by interpreting the acquired data through
models. As far as metrology is viewed as an approach between
dataism and post-truth ideology, its fundamental feature is its
ability to provide justifiable information on empirical properties
[2]: for a process to be a measurement, it must be possible in
principle " to open the box " and allow everybody to check how
the results were obtained.
As the history of scientific, technical and economic evolution
demonstrates, the metrological culture, and in particular
the ability:
◗ to discriminate reliable information, as referred to
intersubjective references such as the definitions of
measurement units, from unreliable information, and
◗ to evaluate the quality of information, by identifying the
effects of the sources of uncertainty by which measurement
results are affected,
can then be crucial in situations in which the outcomes of decision-making
processes should be justifiable, a context we have
called Information-Enabled Decision Making [1]. That approach
is recently developing also in the context of Data Science in
terms of " interpretable Machine Learning " or " explainable
Artificial Intelligence, " with an explicit emphasis on causal
modeling [17]. Thus, the metrological culture can foster the
transition from an information society, characterized by pervasiveness
of new technologies, to a knowledge society, in which
information is a crucial factor of social development. While an
information society only generates and disseminates raw data
and information, a knowledge society transforms the widely
available information in knowledge useful to support effective
decision making and the improvement of human conditions.
Designing an educational path aimed at spreading the metrological
culture is an interesting, and we believe important,
challenge that all of us can consider for the benefit of society
as a whole.
References
[1] D. Petri, P. Carbone, and L. Mari, " Quality of measurement
information in decision-making, " IEEE Trans. Instrum. Meas., vol.
70, pp. 1-16, 2021.
[2] L. Mari, M. Wilson, and A. Maul, Measurement Across the Sciences
- Developing a Shared Concept System for Measurement. Cham,
Switzerland: Springer Nature, 2021.
[3] E. Tal, " Measurement in Science, " in The Stanford Encyclopedia of
Philosophy, E.N. Zalta, Ed., Fall 2020. [Online]. Available: https://
plato.stanford.edu/archives/fall2020/entries/measurementscience.
IEEE
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