When novel AI systems are used to support decisions that impact the livelihood or well-being of people – decisions about loan qualification, health insurance rates, or hiring – unintended and unaccounted for biases or other negative outcomes can cause harm to customers, patients, employees, and other stakeholders.Īn organization employing these systems can also suffer significant damage. “We’ve all seen stories in the media of AI gone wrong,” Veillet said. Veillet and Bodkin opened by describing a major risk organizations should keep in mind when implementing AI systems, along with one of the major factors they’ve repeatedly seen exacerbate risk.ĪI and reputational risk. A divergence from expected performance should be investigated, as it may signal human error, a malicious attack, or unmodeled aspects of the environment.Īn underestimated cause and consequence of AI risk Robustness is the degree to which an AI system maintains its performance when using data that is different from its training dataset. Depending on the use case, interpretability and explainability can play key roles in maintaining trust and safety. Explainability refers to the degree to which we can understand why a system arrives at its prediction and in the manner it does. In basic terms, interpretability refers to the degree to which we can understand how a system arrives at its prediction. Both terms relate to degrees of understanding and transparency into how an AI system operates. There are many social and technical definitions, some of which conflict with one another. Determining what is ‘fair’ is no simple task. Without technical safeguards or humans-in-the-loop with a duty to catch and correct for this, these predictions can inform discriminatory decisions that unfairly harm people. AI systems that use biased datasets may produce biased predictions. #Fireside chat definition software#Due to AI’s novelty, complexity, and unique characteristics, AI model governance may require different controls from those used to govern traditional software models, including controls to monitor the lifecycle of AI models as their execution environment changes. A framework of controls designed to ensure that organizations comply with regulations, assign accountability, and safeguard against adverse outcomes can mitigate these risks.
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