![]() ![]() ![]() justice system for predicting likelihood of future arrests, is no more accurate than a simple predictive model that only looks at age and criminal history. For example, it has been repeatedly demonstrated that COMPAS, the complicated black box tool that’s widely used in the U.S. This is consistent with other emerging research exploring the potential of explainable AI models, as well as our own experience working on case studies and projects with companies across diverse industries, geographies, and use cases. In other words, more often than not, there was no tradeoff between accuracy and explainability: A more-explainable model could be used without sacrificing accuracy. We found that for almost 70% of the datasets, the black box and white box models produced similarly accurate results. ![]() To explore this question, we conducted a rigorous, large-scale analysis of how black and white-box models performed on a broad array of nearly 100 representative datasets (known as benchmark classification datasets), spanning domains such as pricing, medical diagnosis, bankruptcy prediction, and purchasing behavior. But does their complexity necessarily make black-box models more accurate? Debunking the Accuracy-Explainability Tradeoff Cognitive load theory has shown that humans can only comprehend models with up to about seven rules or nodes, making it functionally impossible for observers to explain the decisions made by black-box systems. In contrast, black-box models use hundreds or even thousands of decision trees (known as “random forests”), or billions of parameters (as deep learning models do), to inform their outputs. Because of the small number of rules or parameters, the processes behind these algorithms can typically be understood by humans. Specifically, data scientists draw a distinction between so-called black-box and white-box AI models: White-box models typically include just a few simple rules, presented for example as a decision tree or a simple linear model with limited parameters. This perception is known as the accuracy-explainability tradeoff: Tech leaders have historically assumed that the better a human can understand an algorithm, the less accurate it will be. Research has shown that a lack of explainability is both one of executives’ most common concerns related to AI and has a substantial impact on users’ trust in and willingness to use AI products - not to mention their safety.Īnd yet, despite the downsides, many organizations continue to invest in these systems, because decision-makers assume that unexplainable algorithms are intrinsically superior to simpler, explainable ones. From credit approvals to customized product or promotion recommendations to resume readers to fault detection for infrastructure maintenance, organizations across a wide range of industries are investing in automated tools whose decisions are often acted upon with little to no insight into how they are made. Today, more and more decisions are made by opaque, unexplainable algorithms like this - often with similarly problematic results. When she complained, Apple representatives reportedly told her, “I don’t know why, but I swear we’re not discriminating. 9.In 2019, Apple’s credit card business came under fire for offering a woman one twentieth the credit limit offered to her husband.Data on File at Align Technology as of October 12, 2017. These impressions are then scanned by Invisalign technicians to create the 3D image for use in the ClinCheck® software and manufacturing aligners. Some doctors still take physical impressions using a soft putty. Data on file at Align Technology, as of September 9, 2020. Based on mild to moderate malocclusion (defined as crowding and spacing up to 6mm, and overjet and overbite up to 6mm and assuming one week wear), treatment times vary depending on Invisalign product type, wear time, case complexity and must be determined by your doctor. Treatment times vary depending on the complexity of your case and must be determined by your doctor. Coverage amount averaged $1,772 USD, with 92% qualifying for up to $3,000 USD and 77% qualifying for up to $2,000 USD in coverage for orthodontic treatment. 4. Data from OrthoFi for calendar year 2021, N = 112,243, US patients with dental insurance coverage who used OrthoFi insurance verification tool.Compared to Invisalign aligners previously made from single-layer (EX30) material. Buschang, Discomfort associated with Invisalign and traditional brackets: A randomized, prospective trial. Am J Orthod Dentofacial Orthop February 2017, 151 259-66 Buschang, P et al Comparative time efficiency of aligner therapy and conventional edgewise braces. Evaluation of Invisalign treatment effectiveness and efficiency compared with conventional fixed appliances using the Peer Assessment Rating index. ![]()
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