IEEE Computational Intelligence Magazine - November 2022 - 7

FIGURE 1 IEEE CIS Industrial and Governmental Activities Committee focused on Industry Engagement in Conferences. We showcase the six
verticals in the IEEE CAI 2023.
wildfire impact many industries, which
are then forced to manage the associated
risks. The Earth System has complex
processes on a wide range of length and
time scales, which makes AI a critical
tool to predict the impacts of these
complex events on industries such as:
❏ Power utilities: Energy system management
and optimization, generation, and
transmission.
❏ Agriculture: Management of crops to
maximize yield.
❏ Insurance: Management of risks associated
with natural disasters.
❏ Civil Engineering: Design of resilient
infrastructure, including transportation
and logistics.
(6) Social Implications ofAI/Privacy
To use AI ethically in human-critical
applications that could affect wellbeing,
environment, lifestyle, etc., we
need to trust the AI systems and those
who develop and implement them.
The National AI Initiative (https://
www.ai.gov/strategic-pillars/advancingtrustworthy-ai/)
notes:
" To be trustworthy, AI technologies
must appropriately reflect characteristics
such as accuracy, explainability and interpretability,
privacy, reliability, robustness, safety,
and security or resilience to attacks - and
ensure that bias is mitigated. "
This vertical will focus on these
characteristics, highlighting their technical
and business challenges. The following
definitions have been adapted
from many sources [4], [5], [6].
❏ Explainability and Interpretability.
Understanding is a requirement for
developing trust. Explanations are
necessary to enhance understanding,
trust, and informed decision making.
Explainability is the extent to which
the internal mechanics of a model
can be explained in human terms.
Interpretability is about the extent to
which a cause and effect can be
observed within a system.
❏ Transparency. The AI data, system,
and business models must be transparent.
Humans must be aware
when they are interacting with an AI
system, and must be informed of its
capabilities and limitations. Ultimately,
transparency improves traceability
and accountability.
❏ Privacy, Governance Risk, and Compliance.
Besides providing privacy and
data protection, data governance
mechanisms must also be used, to
guarantee data quality and integrity,
while ensuring legitimized access to
data.
❏ Robustness, Security/Resilience to attacks.
ML algorithms could be vulnerable to
novel adversarial inputs. Trustworthy
AI systems must have Robustness
(the ability to withstand the effects
of adversaries), including adversarial
threats and attacks.
❏ Fairness/Bias. Hidden biases could
lead to discrimination and exclusion
of underrepresented/vulnerable
groups. To prevent this, AI systems
must include proper safeguards
against bias and discrimination.
Except for when the bias is intrinsic
to the ML algorithm (Algorithm Bias),
biases are usually introduced in the
ML model through its training data
set: Sample Bias, Prejudice Bias, Measurement
Bias, and Exclusion Bias.
While each of the verticals is
interesting in its own right, their interconnectivity
is obvious. Energy minimization
is of great importance in
transportation systems. Privacy and
explainability are important in healthcare
decision-making. Cybersecurity in
industrial settings is just as critical in an
autonomous car as in a hospital, or the
control of smart energy grids. While
IEEE CAI 2023 aims to focus discussion
on these verticals, we also plan for discussion
and panels that will allow time
for the important cross-pollination
between them. We invite interested
readers to follow IEEE CAI 2023 developments
via https://cai.ieee.org/2023/,
submit papers for the poster sessions
when the CFP is released, and attend the
conference to benefit directly from the
interactions between industrial thoughtleaders,
practitioners, and researchers.
References
[1] P. P. Bonissone, " What is new in industry?[Industrial
and Governmental Activities], " IEEE Comput.
Intell. Mag., vol. 17, no. 3, pp. 5-6, Aug. 2022.
[2] J. Jiao, " Application and prospect ofartificial intelligence
in smart grid, " IOP Conf. Ser.: Earth Environ.
Sci., vol. 510, 2020, Art. no. 022012.
[3] Accessed: Sep. 8, 2022. [Online]. Available:
https://www.sap.com/insights/smart-grid-ai-inenergy-technologies.html
[4]
Accessed: Sep. 8, 2022. [Online]. Available:
https://www.onespan.com/blog/trustworthy-aiwhy-we-need-it-and-how-achieve-it
[5]
Accessed: Sep. 8, 2022. [Online]. Available:
https://www.technologyreview.com/2020/03/25/
950291/trustworthy-ai-is-a-framework-to-helpmanage-unique-risk/
[6]
Accessed: Sep. 8, 2022. [Online]. Available:
https://digital-strategy.ec.europa.eu/en/library/
ethics-guidelines-trustworthy-ai
NOVEMBER 2022 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 7

https://cai.ieee.org/2023/ https://www.ai.gov/strategic-pillars/advancing-trustworthy-ai/ https://www.ai.gov/strategic-pillars/advancing-trustworthy-ai/ https://www.ai.gov/strategic-pillars/advancing-trustworthy-ai/ https://www.sap.com/insights/smart-grid-ai-in-energy-technologies.html https://www.sap.com/insights/smart-grid-ai-in-energy-technologies.html https://www.onespan.com/blog/trustworthy-ai-why-we-need-it-and-how-achieve-it https://www.onespan.com/blog/trustworthy-ai-why-we-need-it-and-how-achieve-it https://www.technologyreview.com/2020/03/25/950291/trustworthy-ai-is-a-framework-to-help-manage-unique-risk/ https://www.technologyreview.com/2020/03/25/950291/trustworthy-ai-is-a-framework-to-help-manage-unique-risk/ https://www.technologyreview.com/2020/03/25/950291/trustworthy-ai-is-a-framework-to-help-manage-unique-risk/ https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

IEEE Computational Intelligence Magazine - November 2022

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