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What is Explainable AI (XAI)

Exploring the Ethical Implications of Generative AI
AI whose logic can be explained and is understandable to humans. (sw. explainable AI)
Published in Chapter:
Harnessing the Power of Artificial Intelligence in Law Enforcement: A Comprehensive Review of Opportunities and Ethical Challenges
Akash Bag (Amity University, India), Souvik Roy (Adamas University, India), and Ashutosh Pandey (Adamas University, India)
Copyright: © 2024 |Pages: 25
DOI: 10.4018/979-8-3693-1565-1.ch008
Abstract
Law enforcement is joining the fast-growing artificial intelligence (AI) research field. The chapter tries to fix that. This chapter utilized a “systematic literature review.” The authors gathered research papers on using algorithms and AI in police work. This was done with Scopus, a fancy academic database. They searched for papers on “law enforcement,” “policing,” “crime prevention,” “crime reduction,” and “surveillance.” Combine these terms with “algorithm” or “artificial intelligence.” They found that AI has great potential to aid law enforcement. It can recognize faces, forecast crimes, and track people. These AI tools usually analyze photos, behavior, language, or a combination. However, there are significant “but” ethical issues that exist. AI can cause unjust treatment, confusion about responsibility, oversurveillance, and privacy invasion. AI's benefits and cool abilities are often highlighted over its drawbacks. Another observation is that writings on the same topics agree on what AI can achieve, its potential, and what we should explore next.
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More Results
Causality: The Next Step in Artificial Intelligence
An approach in artificial intelligence that focuses on developing machine learning models and systems that provide transparent and interpretable explanations for their decisions and predictions, enhancing trust and understanding of AI systems by humans.
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Generative AI in Higher Education
This type of AI is designed to make its functioning transparent and understandable to humans. XAI aims to explain how and why AI systems make certain decisions.
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Open Challenges and Research Issues of XAI in Modern Smart Cities
An approach to designing AI models that can provide clear and understandable explanations of their decision-making process to human users.
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Systematic Literature Review: XAI and Clinical Decision Support
A suite of machine learning techniques that produce more explainable models, while maintaining a high level of learning performance and enable users to understand, trust and effectively manage AI models.
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Introduction to Machine Learning as a New Methodological Framework for Performance Assessment
An emerging approach where one artificial intelligence system evaluates the underlying code of another artificial intelligence or machine learning model and generates a human-interpretable explanation of the outcomes.
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