Discover how XAI has significantly transformed the process of ML and AI engineering, enhancing the persuasiveness of the integration of these technologies for stakeholders and AI experts.
1. Why Is XAI Vital for AI Professionals?
2. Three Considerations for Explainable AI
2.1. Building Trust and Adoption
3. Establish AI Governance with XAI
Introduction
Currently, the two most dominant technologies in the world are machine learning (ML) and artificial intelligence (AI), as these aid numerous industries in resolving their business decisions. Therefore, to accelerate business-related decisions, IT professionals work on various business situations and develop data for AI and ML platforms.
The ML and AI platforms pick appropriate algorithms, provide answers based on predictions, and recommend solutions for your business; however, for the longest time, stakeholders have been worried about whether to trust AI and ML-based decisions, which has been a valid concern. Therefore, ML models are universally accepted as “black boxes,” as AI professionals could not once explain what happened to the data between the input and output.
However, the revolutionary concept of explainable AI (XAI) has transformed the way ML and AI engineering operate, making the process more convincing for stakeholders and AI professionals to implement these technologies into the business.
This article provides an overview of why explainable AI is important for IT professionals and the various explainability techniques for AI.
1. Why Is XAI Vital for AI Professionals?
Based on a report by Fair Isaac Corporation (FICO), more than 64% of IT professionals cannot explain how AI and ML models determine predictions and decision-making.
However, the Defense Advanced Research Project Agency (DARPA) resolved the queries of millions of AI professionals by developing “explainable AI” (XAI); the XAI explains the steps, from input to output, of the AI models, making the solutions more transparent and solving the problem of the black box.
Let’s consider an example. It has been noted that conventional ML algorithms can sometimes produce different results, which can make it challenging for IT professionals to understand how the AI system works and arrive at a particular conclusion.
After understanding the XAI framework, IT professionals got a clear and concise explanation of the factors that contribute to a specific output, enabling them to make better decisions by providing more transparency and accuracy into the underlying data and processes driving the organization.
With XAI, AI professionals can deal with numerous techniques that help them choose the correct algorithms and functions in an AI and ML lifecycle and explain the model’s outcome properly.
2. Three Considerations for Explainable AI
Mastering XAI helps IT professionals develop new technologies, streamline businesses, and provide transparency in data-driven decisions. Here are five exhibits on why you should consider XAI:
2.1. Building Trust and Adoption
The initial motive for considering XAI is to build the utmost trust between stakeholders; they need to feel confident that the AI model that generates consequential decisions is performing accurately and fairly. Professionals who are dependent on AI applications should be aware that the next best recommendations or actions that come from a black box should help them make the right decisions, and they can follow them confidently.
2.2. Increasing Productivity
There are sets of tools and frameworks for XAI that can quickly detect errors and areas for improvement, making it easy for MLOps professionals to supervise the AI systems, monitor them thoroughly, and effectively up and run them. For instance, understanding a specific feature of the AI system that leads to an accurate model output helps IT professionals confirm if the patterns identified by the model are applicable in different areas and if they would be relevant enough to predict the future with data.
2.3. Mitigating Regulatory
The most crucial part where XAI comes in as a savior to your company is mitigating risks. AI systems that operate on unethical norms, even unintentionally, can ignite intense regulatory scrutiny. There are government norms and regulations specifically on explainability and compliance steps that an organization needs to follow. In some sectors, XAI is compulsory; for instance, a statement issued by the California Department of Insurance made it mandatory for insurers to explain “adverse actions taken based on complex algorithms.” Even though in some sectors XAI is not mandated, companies using AI and ML models need to confirm any tools used to render actions.
3. Establish AI Governance with XAI
Establishing AI governance needs an AI committee that identifies the constituents of XAI. The explanation and risk assessment of AI use cases tend to be complex; however, it requires an understanding of the business objective, the user’s intended technology, and legal requirements. Therefore, organizations must convene a cross-functional set of experts, such as policymakers, IT experts, legal and risk personnel, and business leaders. This kind of diverse POV, both internally and externally, helps the company test and explain the development and support of AI models for different audiences.
The key function of the AI committee is to set standards for XAI; as part of the standard process, an effective AI governance committee can establish a risk taxonomy that can classify the sensitivity of different AI use cases. Further, the taxonomy can be clarified and escalated to the review board or legal head if required.
Conclusion
The concept of explainable AI, across all industries, is an innovative evolution of AI that offers companies opportunities to build trustworthy and transparent AI applications. As we continue to unravel the details of AI, the importance of accounting becomes more distinct.
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