RESPONSIBLE AI challenge and apply AI responsibly. Fear, he says, often stems from misunderstanding, making education the most effective antidote.
Identifying and mitigating risk In industrial AI, ethical risks often emerge when systems encounter the complexity of the physical world in ways not fully represented during development. Realworld conditions introduce variability that only becomes visible once AI is connected to physical assets or human activity.
Johannes offers a solution:“ Technologies such as spatial models and digital twins provide a way to continuously compare what an AI system predicts with what is actually happening on the ground. This ongoing validation makes it easier to spot bias, drift or unintended effects early.
“ Where AI outputs influence decisions with lasting consequences, human judgement must remain central. Feedback from live operational environments becomes essential for maintaining trust and ensuring systems continue to behave as intended over time.”
Greg places emphasis on governance and data quality, stating that ethical risk mitigation must begin with embedding clear guidelines and accountability into every stage of AI development.
“ Strong governance structures, including oversight, ownership and auditability, create a foundation of trust and transparency,” Greg notes. Organisations must therefore understand where data comes from, how it is used and how it changes over time. aimagazine. com
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