Whitebox AI solutions improve the transparency of business decisions by revealing the details of the AI solution. The level of transparency gained through the Whitebox approach can enable businesses to improve operational goals, customer service, and financial objectives.
AI is becoming increasingly more pervasive in business-critical decisions. Organizations are investing in AI for improved analytics, operational efficiencies, customer retention, and business resilience, an International Data Corporation (IDC) study reports.
However, organizations face difficulty in interpreting AI decisions and understanding the underlying factors influencing AI decisions. The logic and explainability of AI decisions become difficult to comprehend, which we refer to it as a Blackbox AI system.
In the case of black-box AI systems, automated decision-making may map user features as a class predicting customer behavioral traits such as health status, credit risk, and more. However, it fails to reveal the reason for this. This is problematic because the algorithms may inherit biases from human prejudices or hidden artifacts in the training data, resulting in suboptimal decisions. Thus, making it difficult to determine the cause of errors.
In summary, the black-box nature of AI solutions raises the possibility of operational inefficiencies, reputational damage, decreased performance, and even legal repercussions. Technologists and policymakers raised concerns about the lack of accountability and biases associated with AI-driven decisions.
As an answer, we can now address these concerns and risks in the decision made by the models by following the Whitebox AI approach. Whitebox AI systems can transcend the black box problem by explaining the ‘why’ behind every prediction. Understanding the ‘why’ is key for any industry- could be a startup or a highly regulated industry.
Whitebox solution for AI adoption
It is a simple model where the workings are transparent and interpretable. It allows the data scientists/analysts to test the design, internal structure, coding, and the like. It gives a wide range of information before arriving at a conclusion. It authenticates the input and outflow, improving the AI functionalities and explainability.
It assists organizations to realize the full potential of AI adoption and manage their technology implementation. Demonstrating the ‘why’ factor helps to maintain customer trust and comply with regulations as well. As a result, this is becoming the industry best practice today, allowing businesses to eliminate cognitive biases, human biases, and hidden artifacts.
The significance of this quote lies in the idea of paying attention to every seemingly ‘insignificant’ or ‘explanatory’ detail that businesses believe has no effect on the profit graph. In fact, it is the small details that drive signals of business change.
Organizations can now better understand AI programs and look for tangible ways to improve their workflows. Transparency in decision-making increases the end-trust users and reliability too.
Here is a simple illustration to understand how data is interpreted using the Whitebox approach.
Testcase: Property valuation
When evaluating a property, the AI functionality works behind the scenes and mentions the value of a given property. For instance, Assume AI determined the value of a property to be 1 crore. The real estate industry is baffled as to why AI estimated the property value at 1 crore. On the contrary, Whitebox explains the features – both indoor and outdoor- that it considered when determining the property value.
Interpretability help businesses stay accountable for their data-driven decisions. With the white box AI approach, data-driven decisions can remain explainable, accountable, and actionable.
Empower your business decisions with white box AI solutions and execute complex projects with certainty.
If you like to monitor and improve decision-making strategy, lessen the investment cost, and run hassle-free AI deployments, write to us at firstname.lastname@example.org.