Date: 18 Sep 2024
Presented by: Zero Code Learning
In the world of growing technology, where organizations are entangled with the realm of AI, the need for a demarcated explainability arises. From WhatsApp messenger to Integrated Development Environments (IDEs), AI has enhanced customer experience while also democratizing AI tools designed by the respective organizations. With the proper knowledge to develop and deliver, organizations have learned to supply AI powered services and products for the customers on an ever-increasing scale. On an individual scale, AI is known to be used for text generation, image generation and data analytics etc. Keeping the necessity of AI in mind, one must yearn to be able to explain how an AI tool works. Without the explainability of the tool under use, AI’s impact on the world becomes futile.
AI tools perform on the basis of the data and the algorithms they are trained with. Trusting these models solely on the outputs they render would not be preferrable to any organization or individual. Along with the output, proper reasoning for the output generated and the process involved in obtaining it stand equally important. Integration of these aspects into the AI tools endorses their reliability in the real-world applications. Such a set of tools or processes that validate and propel the AI model’s reliability comprise what is defined as Explainable AI(XAI). XAI explains the potential advantwages and disadvantages of using certain AI tools.
An organization that develops an AI tool should be able to trust it, to feed data and use algorithms that are confidential to the organization. When these AI tools are used in the decision-making process, their transparency in the data they use and the trustworthiness they exhibit are paramount to the organization’s needs. Along with being trustworthy, the AI tools need to follow the legal and regulatory requirements which the organization sets out before the AI tool is finished developing. To the customers and stakeholders, it needs to be emphasized that the AI tools do not provide biased answers and are fair to all castes, genders and societal norms. XAI enables this for the business with its specialized knowledge and applicability in diverse situations, by explaining the choices made for choosing the features, techniques used for the identification of biases, limitations of the model and the regulations that need be followed by the AI model.
So far, AI tools have been prominently used in Finance sector for risk assessment, fraud detection and obtaining investment strategies; in healthcare for disease diagnosis, treatment recommendations and patient management; in Marketing for customer segmentation, personalized advertisements and campaign optimization; in Human Resource for hiring process, employee evaluation and retention strategies. Considering the speed with which they are advancing, their application in every field is increasing exponentially and beyond boundaries assumed impractical. XAI enhances their utilization by the explanation of the reasons behind every decision taken by the AI tool. This improves the transparency, trust and the decision-making process of the AI tools.
During the implementation of Explainable AI in AI tools, there arise certain challenges that require dealing with precision and care. While integrating XAI into AI tools, the complexity and the capability of the tool should not be compromised to inculcate better explainability. The integration sometimes demands the compromise of the efficiency but should not be submitted. Also, the specialized technical knowledge it demands should not diverge the customer or the stakeholders away from the main purpose of the AI tool. In other words, the XAI aspect of the AI tool should not become the primary aspect of its functioning.
XAI is becoming increasingly predominant in AI applications, governing the usage of tools and the techniques that are used to develop them. Their conformity to ethical and practical considerations can be enhanced by XAI, reducing the necessity of involving humans in the process and making it more efficient in operability. Overall, XAI is propelling the use of AI tools in business operations and in alluring customers into seeing the brighter side of AI. Developers should take into consideration the element of XAI while developing the AI tools, enabling knowledge and information to all users.
-Ananya Apurv Yakkundi
