Peeking into the "black box"with Explainability
As AI becomes more engrained in our daily lives, from healthcare to quality inspections, we are finding the need for technology that can both perform its primary function exceptionally well, as well as explain why. It turns out, most of us have little visibility and knowledge on how AI systems make decisions. While little visibility may be okay for some, it is insufficient for companies that operate in industries like manufacturing. As the applications of AI grow, so do the number of critical decisions made.In many industrial use cases these AI systems are making decisions that, if incorrect, can result in products riddled with defects, costing the manufacturer money, downtime and reputation. Understanding why an AI system makes a decision, taking a peek into the ‘black box”, is crucial for a successful AI application and why we recently announced the addition of our explainability feature in our inspection software, Neurala VIA.
With the introduction of explainability , manufacturers can derive more actionable insights from datasets, identifying whether an image truly is anomalous, or if the error is a false-positive resulting from other conditions in the environment, such as lighting. This gives manufacturers a more precise understanding of what went wrong, and where, in the production process, and allows them to take proper action – whether to fix an issue in the production flow or improve image quality.
Manufacturers can utilize Neurala’s explainability feature with either Classification or Anomaly Recognition models. Explainability highlights the area of an image causing the vision AI model to make a specific decision about a defect. In the case of Classification, this includes making a specific class decision on how to categorize an object; or in the case of Anomaly Recognition, it reveals whether an object is normal or anomalous. Armed with this detailed understanding of the workings of the AI model and its decision-making, manufacturers can build better performing models that continuously improve quality inspection processes and efficiencies.
Interested in VIA’s explainability feature? Contact a member of the Neurala VIA team.