Manage impact of “inherent” AI errors

Saleema Amersh makes good points in a workshop paper regarding how to think about AI systems failure.

1) Assume that even useful systems will have regular failures

AI models are our attempts to represent and operationalize key aspects of real world systems. By “attempt” I mean that it is difficult, if not impossible, for AI models to ever fully capture the complexity of the real world. Consequently, errors are essentially inherent to AI models by design. That is, many AI algorithms work to optimize some objective function, such as minimizing some notion of loss or cost. In doing so, and because AI models only partially represent reality, AI algorithms necessarily must trade off errors and sacrifice parts of the input space to produce functions that can generalize to new scenarios. Therefore, while efforts to avoid AI biases and harms are needed, ethical AI development must also recognize failures as inevitable and work towards systematically and proactively identifying, assessing, and mitigating harms that could be caused by such failures.

2) Think about entire “system” not just the ML model in isolation.  Accept that investments in transparency and explanations are worthwhile even if if it means a trade-off in somewhat lower model metrics.  Focus on the outcome delivered by the system not just optimization of the model metrics.

When thinking about AI failures, we need to think holistically about AI-based systems. AI-based systems include AI models (and the datasets and algorithms used to train them), the software and infrastructure supporting the operation of those models within an application, and the application interfaces between those models and the people directly interacting with or indirectly affected by them. AI-based failures therefore go beyond traditional notions of model errors (such as false positives and false negatives) and model accuracy measures (such as precision and recall) and include sociotechnical failures that arise when AI-based systems interact with people in the real world. For example, medical doctors or judges viewing AI-based recommendations to inform decision making may over- or under-estimate the capabilities of the AI components making those recommendations, to the potential detriment of patients and litigants. Acknowledging this type of sociotechnical failure has motivated an exciting and active area of research in algorithm transparency and explanations … types of sociotechnical AI failures may include expectation mismatches, careless model updates, and insufficient support for human oversight and control.

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