Review Paper
Year: 2020 | Month: November | Volume: 7 | Issue: 11 | Pages: 608-611
Shedding Light on AI: Exploring Explainable AI Techniques
Deekshitha Kosaraju
Independent Researcher, Texas, USA
ABSTRACT
The emergence of Artificial Intelligence (AI) tools has influenced sectors creating a need, for reliable and understandable systems that stakeholders can rely on. Explainable AI (XAI) strives to AI operations enhance transparency and strengthen the trustworthiness of AI systems. This study explores the significance of XAI in fields, such as healthcare and finance where AI driven decisions can carry substantial consequences. Through the integration of XAI these sectors can ensure that AI decisions are reasonable and understandable to those they affect. We delve into XAI approaches, their practical uses, and the inherent challenges in creating AI systems that are both efficient and interpretable. Moreover, we discuss future paths for XAI while highlighting the importance of ongoing innovation to keep up with advancements in AI technology and its applications. This summary lays the groundwork for a conversation on how XAI not only enhances the performance of AI systems but also aligns them with ethical norms and regulatory standards ultimately fostering deeper trust, between humans and machines.
Keywords: Explainable AI (XAI), AI Transparency, AI Ethics, Model Interpretability, AI Regulation, Decision Making in AI, Machine Learning Algorithms, AI in Healthcare, AI in Finance, Real-time AI Systems
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