By Nikhil Sonawane - March 29, 2023 6 Mins Read
Artificial intelligence (AI) augments various facets of decision-making across every aspect of business operations. This robust technology has enabled enterprises to transform their business operations and other aspects of human life.
AI algorithms can make real-time decisions without needing a human to intervene. However, multiple decisions need human-machine symbiosis to make accurate decisions. Humans need to understand how the machine reached a specific conclusion or prediction, and the machine needs to understand humans to have a better human-machine symbiosis.
For instance, hiring a new resource need more human involvement rather than depending on AI completely to make a final decision.
Explainable AI (XAI) is a way for AI systems to reshape how users understand information. It is one of the most efficient tools that help to facilitate understanding by improving the interpretability of the framework. XAI explains the outcomes to the decision-maker in a human-understandable manner. Hence, with the help of explainable artificial intelligence, decision-makers can make more transparent and fair decisions that facilitate growth.
Regulatory bodies worldwide expect organizations to explain their machine-driven decisions to ensure compliance.
According to the GDPR (General Data Protection Regulation), users can ask for an explanation for the algorithm’s output. As a result, decision-makers need to transform their decision-making tools from a black box to a glass box. To improve the explainability and interpretability, XAI techniques are spread across two wide spectrums:
Model-specific XAI: This XAI technique incorporates interpretability in the inherent framework of the learning model.
Model-agnostic XAI: This XAI technique leverages the learning model as an input to generate an explanation.
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Let’s see the impact of explainable artificial intelligence:
Leveraging XAI enables enterprises to develop interpretable and inclusive AI systems from scratch with inbuilt tools designed to identify and resolve bias and other data and model gaps.
AI Explanations offer data scientists more valuable insight required to enhance datasets or model architecture and debug model performance. This What-if tool enables organizations to evaluate model behavior at a glance.
End-users develop trust and improve transparency with human-understandable explanations of machine learning models. While launching a model on AutoML Tables or AI Platform, organizations get predictions and results in real time, showing how much a particular factor impacts the final results.
Even though explanations will not reveal fundamental relationships in the entire data sample or population, they will help to reflect the patterns observed in the data.
Explainable AI enables enterprises to streamline their processes and enhance their capabilities to manage machine learning models. Moreover, it is one of the most effective ways to simplify organizational performance monitoring and training. XAI models monitor the forecasts done by the model on the AI platforms. The constant analysis feature of XAI allows organizations to compare the model predictions with the set truth tables to get continuous feedback and improve the model performance.
Enterprises that embrace the XAI model will benefit the organization immensely by developing interpretable AI systems. Additionally, leveraging this model enables enterprises to handle regulation pressures more effectively and adopt the best practices based on accountability and ethics. Businesses that invest in XAI today will get a competitive advantage and Increases user confidence, accelerates adoption, and helps turn the vision of AI into reality. Following are a few benefits of XAI:
Industries like Medicine, Finance, Legal, and others need accurate predictions because these are decision-sensitive fields. Wrong predictions in these industries can have disastrous impacts and might even have legal litigations. Constantly Monitoring the results reduces the impact of errors in the results and determines the root cause of the bottleneck to improve the underlying model.
XAI tends to improve the system’s confidence. A few user-critical devices like Medical Diagnosis, the Finance sector, and others require high code confidence from the user for optimized utilization. The regulatory bodied globally are forcing businesses to adopt XAI to ensure compliance with the rules and regulations.
One of the significant positive outcomes of embracing explainable AI is increased human engagement. Resources that understand the reasons for the recommendation can use their expertise to make the final decision.
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Businesses leverage machine learning applications to automate decision-making. Usually, they want to leverage these models primarily for analytical insights.
For instance, enterprises can train a model to forecast sales across a large retail chain by leveraging demographic data such as location, opening hours, weather, outlet size, and other factors. Developing an XAI model makes it easier for businesses to identify and utilize main sales drivers to boost revenues.
The best way to optimize performance is by identifying potential weaknesses and strengths organizations with an in-depth understanding of how the models work and why they fail to make optimizing easier. The provision of feature-based explanations paves the way to make XAI a powerful tool to detect flaws in the model and biases in the data that develop users’ trust.
Explainable artificial intelligence is an effective way to validate predictions, optimize model performance and get valuable insights into operational bottlenecks and challenges. Identifying model or dataset biases makes it easier when businesses have an in-depth understanding of what the model does and what factors make it arrives at its predictions.
Regardless of the organization’s type, size, and industry, explainable AI should be essential for all AI strategies. It must be a key consideration when businesses embrace AI models. XAI cannot be thought of once the AI or ML model is integrated into the business operations; it must be well thought out before implementing such models.
It is crucial to ensure that managers have an in-depth understanding of the risks and the restrictions of unexplained models and should take accountability for risks.
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Nikhil Sonawane is a Tech Journalist with OnDot Media. He has 4+ years of technical expertise in drafting content strategies for Blockchain, Supply Chain Management, Digital Transformation, Artificial Intelligence, Big Data, SaaS, PaaS, cloud computing, Data analytics, Enterprise Resource Planning (ERP) solutions, and other emerging enterprise technologies and trends.With eclectic experience in working and writing about complex enterprise systems, he has an impressive track record of success. Through his specialized knowledge of thoughtful and compelling writing styles, he covers a wide range of topics that delve into organizational effectiveness, successful change, and innovation management.His Commitment to ongoing learning and improvement helps him to deliver thought-provoking insights and analysis on complex technologies and tools that are revolutionizing modern enterprises.He brings his eye for editorial detail and keen sense of language skills to every article he writes. If traveling was free, it would have been difficult to trace him.
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