Modern-world business problems require modern technological solutions. In the intensely competitive landscape, organizations seek the speedy assistance with Artificial Intelligence (AI) to increase productivity and ensure better decision-making. By automating repetitive tasks, optimizing operations, and predicting customer behaviour, AI has proved to be a boon for organizations across various sectors. Provided with the necessary pooled data, AI uses algorithms to come up with possible outcomes and helps business professionals make decisions for business growth.
In most instances, AI delivers accurate results. However, there are some instances where the results generated by the model may differ from what was predicted or expected. It is important to understand that accuracy depends on what knowledge and instructions were embedded into the AI system. Data scientists rely on model explainability and interpretation to investigate instances wherein the results are not efficient. Explainable AI (XAI) can be immensely helpful towards this end.
What is Explainable AI (XAI)?
Explainable Artificial Intelligence (XAI) is a specialized field within AI dedicated to creating approaches and strategies that clarify the inner workings of AI systems. Its objective is to interpret the factors that influence AI outputs, providing a clear understanding of the underlying logic, assumptions, and limitations of these models. XAI is instrumental in pinpointing potential biases, errors, or risks, allowing for improvements and corrections. Moreover, it serves as a valuable tool for effectively communicating the value and reliability of AI solutions to stakeholders, customers, and regulatory bodies.
Why does businesses need ‘White Box’ Model?
After the data is poured into the AI model, a result is generated that may or may not be accurate. When the result does not match the predictions, scientists and engineers try to evaluate the internal workings of the AI model. However, the AI model is known as a Black Box model because its code is opaque and difficult to understand.
For structuring effective decisions, decision scientists require access to trustworthy, rule-based explanations for accelerating faultless decision-making.
Therefore, there comes the need for a White Box or an Explainable AI model that is transparent and interpretable. XAI makes it easier for scientists to understand the internal workings of the AI model. Accordingly, the scientists can improvise the input data and achieve the desired results.
Why Do Businesses Need Explainable AI (XAI)?
Businesses rely on AI for critical decisions, emphasizing the need for explainability. Understanding how AI models reach conclusions is crucial for trust. Adopting basic tools is common, but unlocking AI’s full value demands a comprehensive strategy, focusing on a governance framework, best practices, and strategic tool investments.
With modelling techniques complicating explainability, prioritizing transparency becomes essential for meeting customer, employee, and regulatory needs. To understand the criticality of XAI for today’s organizations, it is important to take a look at two examples:
A multinational technology corporation had implemented a Chatbot to support requests from their social media profile visitors. However, the Chatbot was reported to have made certain biased response, triggering agitation among the users. Possibly, the Chatbot’s responses were based on the user comments prevalent on the platform. However, the management wanted to investigate this matter and find remedies. Finding answers as to why and how the Chatbot collected data from the online comments is not an easy task. The reason is that the algorithms used in generating answers are black box models that are extremely difficult to understand. Even experts can find it difficult to interpret.
In the healthcare domain, there’s apprehension among medical practitioners regarding the reliability of AI outcomes in cancer screening. Incorrect reports, whether false positives or false negatives, can result in misdiagnosis. Hence, it becomes essential to rationalize the black box model to prevent the generation of inexplicable and inappropriate answers.
There is a need to simplify the complex machine learning algorithms. Promoting interpretability, transparency, rationality, fairness, and trustworthiness is necessary, as wrong AI predictions can impact operations across all industries.
What are the Benefits of XAI for Businesses?
Explainable AI (XAI) provides businesses with a suite of advantages:
- Helps make better decisions
- Checks for unfair data
- Builds trust by being unbiased
- Stops harmful attacks
- Finds and fixes problems
- Makes AI work better for different situations
- Helps AI work faster and smoother
The significance of Explainable AI cannot be ignored, as standalone AI solutions are insufficient to generate constructive business decisions. The absence of explainability would undeniably impact the operational efficiency of businesses across various scales.
It’s Time to Unravel the AI Interpretability with AgreeYa
AgreeYa, as a global systems integrator, empowers businesses worldwide to overcome their most significant challenges through technology leading-edge implementations. We have helped various organizations across the globe strengthen their decision-making and reap benefits of AI. We can help adopt and succeed with new technology implementations and concepts. Contact us today!