Instrumentation & Measurement Magazine 23-3 - 6

that can be employed in every situation. Instead, approaches
often need to be adapted to fit the needs of the specific technology being considered. Additionally, from a metrological
point-of-view, in whatever method is used to manage data,
an understanding of the uncertainty of the data and how this
uncertainty can affect the confidence in the conclusions and
decisions is needed [65].
Big data is continuously becoming more extensive and is
impacting more and more application realms. Another Trend
of Consequence that goes hand in hand with Big Data is privacy
and security [66]. I&M enables many identification technologies, such as biometric sensors and image recognition systems,
which can be used for security purposes [67], [68]. Beyond
serving this security need, the I&M community must increasingly concern itself with the security of the systems it
creates and their consequences for privacy of individuals. This
concern has been exacerbated by the growing trends of connectivity, automation, and consequently Big Data: IoT devices
can be vulnerable to hacking as discussed previously and so
can automated systems. Additionally, people are increasingly
concerned about their privacy in a growing digital world [69],
[70]. Thus, privacy and security are considered to be Trends of
Consequence.
Different types of vulnerabilities can allow devices like
medical implants, systems that rely on precise time synchronization (e.g., financial transaction systems and cloud service
providers), and industrial control systems to fall victim to attacks [71]-[73]. With so many different avenues of possible
attacks, instrumentation designers must be increasingly vigilant and anticipate the potential risk and consequence of
hacking. This provides an extra layer of design requirements,
increasing design challenges. As I&M spreads through the
Trends of Choice discussed previously, designers and researchers will have to address the challenges introduced by higher
security and privacy demands.
While the Trends of Consequence of Big Data and privacy/
security are mostly seen as challenges that need to be addressed, there can also be positive Trends of Consequence. One of
these is the equity that can be enabled by I&M.
In the case of forensics, measurements, such as those
from medical devices or DNA analysis, can be used to catch
criminals and bring justice to victims. However, these measurements are not infallible. Just like any measurement, they
have an amount of uncertainty associated with them, which
needs to be minimized so through careful measurement procedure, calibration, and data processing so that the results can
be trusted [74]-[77].
To ensure that equity can be enabled by I&M and in I&M,
the carefulness necessary for forensic measurements also
needs to be extended to ensure that personal biases do not
affect the operation of technologies and their outcomes. Technologies are the product of humans and humans hold biases.
These biases can purposefully or unintentionally influence the
"behavior" of the technologies they create, from the design
phase through the implementation phase. An example of this
is often seen in AI where unintentional inappropriate framing
6	

of the problem trying to be solved and bad training data can
lead to gender, racial, or ideological biases being propagated
by the AI itself which can then lead to injustices in processes
like hiring or criminal justice [78]. Additional bias can enter the
process in the data collection phase, as well; the data needs to
be representative of the cases the AI might be faced with in its
operation so that it is properly trained to evaluate those cases.
Additionally, there needs to be trust in measurement outcomes
that are performed so that the AI learns from reliable data [78]-
[80]. It should be noted, that the consequences of bias are well
understood in the AI community and caution is being taken to
minimize the role bias plays in AI.
While I&M can be used to create accountability and equity,
there is a fine line that is walked as bias and human error can
produce untrue/false results. In order for this trend to uphold
its potential, care needs to be taken to ensure that bias and inaccuracy do not taint and adversely impact fairness.

Trends of Both
Not all trends are purely beneficial or growing challenges that
must be addressed. Some, straddle these two categories to be
Trends of Both. One example of this is AI. AI can help facilitate
automation and even connectivity, and help extract meaning
from large data sets, making it extremely useful. Because of
these capabilities, it has become very fashionable to utilize it in
numerous application realms, including telecommunications,
healthcare, banking, and disaster relief. As has been seen, AI
can be subject to bias which is a major challenge that needs
to be addressed. Additionally, AI is being necessitated by Big
Data. Since it is both beneficial and necessitated, AI can be considered a trend of both choice and consequence.
Another example of a Trend of Both is safety. Due to the push
by consumers for technologies to be safer, producers consequently need to meet these demands. In some application
realms, however, safety can be challenging to achieve. One
such example is autonomous vehicles. On the road, there are
many potential and unpredictable hazards. Therefore, ensuring that a vehicle can reliably react to a wide variety of hazards,
while ensuring the safety of all parties, is extremely important
and challenging. In the I&M area, safety is important to protect
both the measurement maker and the instrumentation. For this
reason, it is also beneficial for manufacturers to incorporate
safety into their systems in order to retain consumer trust. This
challenging yet beneficial and essential nature of safety, qualifies it as a Trend of Both.
Other Trends of Both are adaptability and sustainability. Technology is rapidly changing and various resources are becoming
more constrained. While being adaptable and sustainable can
help weather these changes, it can also be helpful for everyday
operation. An example of this are cognitive radar systems that
can adapt frequency for anti-jamming purposes [81].
With miniaturization, connectivity, and accessibility comes
an increase in the number of devices, each of which consumes
power and natural resources. As these devices become less expensive, the attitude of considering them as disposables or
replaceable becomes more prevalent. This attitude can put a

IEEE Instrumentation & Measurement Magazine	

May 2020



Instrumentation & Measurement Magazine 23-3

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 23-3

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