IIC Journal of Innovation 11th Edition | Page 92

AI Trustworthiness Challenges and Opportunities Related to IIoT
resilience: redundancy, resistance, reliability
and response/recovery. 22 AI can speed and
assist with response and recovery. For
example, AI could be used to predict
cascading failures in conjunction with a
system model/digital twin, allowing
response efforts to be focused on preventing
a wider spread failure.
is to project too much faith into the
capability of intelligent computer systems,
thereby decreasing the overall effectiveness
of the system as it takes longer to identify
areas of improvement due to improper AI
decisions. The potential manipulation of
input data in an attempt to achieve an
improper side-effect is identified as a critical
aspect that required careful attention from a
security perspective. The technology may be
vulnerable to malicious manipulation, but it
may also be used proactively to identify and
mitigate weaknesses in the security, privacy,
safety, etc. which may have eluded human
designers for years. Without proper
consideration of the side effects that AI
introduces into an IIoT system, there may be
a considerable delay in the adoption of these
technologies.
C ONCLUSION
This paper has described the importance of
trust in IoT systems, some issues related to
AI and how the characteristics of IoT
trustworthiness (safety, security, privacy,
reliability, resilience) are impacted by AI.
AI may increase trust in a system but also can
raise new concerns, so the interactions and
use of AI must be understood. Human nature
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22
This is rephrased from “The Right AI for Resilience in Complex Systems”, by Aaron Forshaw, as is the concept of using AI to
enhance response/recovery to enable resilience. See https://cosmotech.com/right-ai-resilience-complex-systems/
IIC Journal of Innovation
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