Machine learning models have historically been perceived as black boxes and for good reason. Developers feed their models data resulting in certain predictions or recommendations. However, engineers are often hard pressed to explain the outputs of their models.
As stakes rise, so must explainability
As ML and AI gain decision making proficiency, explainability becomes more important both to engineers, and end users and governing bodies who demand accountability and transparency. AI models power everything from Netflix recommendations to life altering diagnoses, and some applications require higher levels of explainability than others. High stakes applications such as medicine, autonomous vehicles, insurance pricing, university admissions, and fraud detection require more in depth explainability than product recommendation engines. This is where explainable AI (XAI) software comes in.
Why engineers need XAI
Explainable AI (XAI) software, tools and frameworks that help engineers interpret the outputs of their models and build trust in their products, helps provide these answers. XAI software is offered by IBM, Google Cloud, Microsoft, and others.
With the help of explainable AI, engineers can better debug and improve model performance by detecting bias and drift. Explainability helps engineers uncover patterns in their data that influence the outputs of their models and assess and mitigate risk. Thus equipped, engineers are better able to communicate model behavior, exercise transparency regarding the results and accuracy of their models and justify their predictions to users, fostering trust in the product.
Balancing explainability with complexity
Balancing explainability and model complexity is another challenge: as performance increases, explainability decreases. Decision trees and Bayesian classifiers are easier to understand and compute but far less robust than the neural network. On top of this, engineers are under pressure to balance transparency and privacy (both end user demands) with model accuracy. The key to user buy-in is trust.
As AI takes on projects with increasing impact, explainability will make or break how much people want to use them. Explainable AI will help increase model adoption as trust builds, allowing models to have larger impact in research and decision making. Indeed, certain legislative restrictions may explicitly require explainable models, and they should. People deserve to understand what goes into the decisions affecting many aspects of their lives from shopping and entertainment to even job search.
Engineers have a responsibility to understand and communicate their models and be held accountable for the decisions they make. Especially for those who are hesitant to trust AI, understanding that in certain cases AI-based models make better decisions than humans could be a game changer.
For more perspectives on the importance of understanding how AI works and how it’s perceived, check out Machines and More With Two AI Entrepreneurs, the FuseBytes episode with our CEO Sameer Maskey and Igor Jablokov, CEO of Pryon.
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