Ethics and Accountability Frameworks for AI Systems

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Mr.Sidharth Sharma

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Abstract : Intense discussions concerning the hazards and ethical ramifications of artificial intelligence were sparked by its introduction and broad societal adoption. Traditional discriminative machine learning carries hazards that are frequently different from these risks. A scoping review on the ethics of artificial intelligence, with a focus on big language models and text-to-image models, was carried out in order to compile the recent discourse and map its normative notions. Enforcing accountability, responsibility, and adherence to moral and legal standards will become more challenging as artificial intelligence systems get more adept at making decisions on their own. Here, a user-centered, realism-inspired method is suggested to close the gap between abstract concepts and routine research procedures. It lists five particular objectives for the moral application of AI: 1) comprehending model training and output, including bias mitigation techniques; 2) protecting copyright, privacy, and secrecy; 3) avoiding plagiarism and policy infractions; 4) applying AI in a way that is advantageous over alternatives; and 5) employing AI in a transparent and repeatable manner. Every objective is supported by workable plans, real-world examples of abuse, and remedial actions. This paper will discuss the nature of an accountability framework and related concerns in order to enable the organized responsibility for assignment and proof of AI systems. The suggested architecture for regulating AI incorporates crucial components like transparency, human oversight, and adaptability to address the issues with accountability that have been brought to light. Some crucial suggestions for putting the framework into practice and growing it were also provided through industrial case studies, guaranteeing that companies increase compliance, trust, and responsible AI technology adoption.

Keyword Artificial intelligence, Accountability, AI, Ethics

Reference:

Hunt, E. B. (2014). Artificial intelligence. Academic Press.
Holmes, J., Sacchi, L., & Bellazzi, R. (2004). Artificial intelligence in medicine. Ann R Coll Surg Engl, 86, 334-8.
Winston, P. H. (1992). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc..
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Abstract

The rapid rise of Artificial Intelligence (AI) has sparked intense global debate about its ethical risks and societal impact. As AI systems—particularly large language and text-to-image models—grow more powerful, enforcing accountability and ethical use becomes more difficult. These technologies often operate independently, making decisions without human oversight. This raises concerns about bias, legal responsibility, and trust.Responsible AI development

To address these issues, we propose a user-centered ethical framework that bridges theory and real-world practice. It includes five key goals:

Understand model training and reduce bias.
Safeguard privacy, copyright, and confidentiality.
Prevent plagiarism and policy violations.
Ensure AI offers clear advantages over alternatives.
Promote transparency and reproducibility in AI use.

Each goal is backed by practical strategies, real-world case studies, and examples of misuse. Our framework also outlines a clear accountability structure. It emphasizes human oversight, transparency, and adaptability. This paper includes recommendations for applying the framework in real-world industries to increase compliance and trust. By doing so, it supports responsible AI innovation and ethical integration.

Introduction

Artificial Intelligence (AI) is one of the most powerful technological advances of our time. Its growing use in healthcare, finance, and insurance has created major opportunities—yet also serious risks. One of the biggest concerns is decision-making without human involvement. When AI makes mistakes, it’s often unclear who should be held accountable.Responsible AI development

The complexity of AI algorithms makes their decisions hard to explain. As a result, people may lose trust, especially when bias or errors go unchecked. This lack of transparency can also lead to legal issues and ethical challenges. Therefore, clear rules and oversight are essential.

To fill this gap, this paper offers a detailed ethical framework for AI development and use. Unlike traditional models, this framework focuses on real-world application. It helps researchers, developers, and decision-makers align AI use with laws and societal norms. Additionally, we introduce a method to detect AI-generated text. This reduces misinformation and builds public trust in digital content.

Overall, our research provides practical solutions to guide ethical AI development. It empowers industries to adopt responsible technology while minimizing harm and maximizing transparency.