Ethics and Artificial Intelligence: A Theoretical Framework for Ethical Decision Making in the Digital Era

Ethics and Artificial Intelligence: A Theoretical Framework for Ethical Decision Making in the Digital Era

Yashpal Azad, Amit Kumar
Copyright: © 2024 |Pages: 41
DOI: 10.4018/979-8-3693-1762-4.ch012
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Abstract

In the rapidly evolving technological landscape, the pervasive integration of artificial intelligence (AI) has brought to light the pressing need to address the ethical dimensions associated with its widespread adoption. This study comprehensively explores AI ethics for ethical decision-making in the digital era. It offers a structured guide for aligning AI with ethical principles, emphasizing transparency, bias mitigation, and interdisciplinary collaboration in AI deployment. Additionally, it delves into the evolving AI landscape, highlighting potential societal impacts. It calls upon policymakers and stakeholders to engage in persistent dialogue and to remain adaptable in the face of a continuously transforming technological environment, advocating for the continuous refinement and adaptation of regulatory frameworks. This framework acts as a compass for ethically sound AI decisions, fostering a responsible, human-centric approach. It aims to forge a symbiotic relationship where AI uplifts society while upholding ethical values, making it a tool for societal betterment.
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Understanding The Concept Of Ai

The comprehensive overview of artificial intelligence (AI) begins by elucidating the broad capabilities of AI systems, which can mimic human cognitive functions such as interpreting speech, playing games, and pattern recognition. The learning process of AI involves massive data processing, enabling the system to make decisions based on patterns it identifies. While some AI systems require human supervision during their learning phase, others are designed to learn autonomously, like mastering a video game through repeated plays (Mitchell, 2019).

The Concept of Strong AI vs. Weak AI

AI experts distinguishes between strong AI and weak AI, to define the machine intelligence, strong AI refers to the artificial general intelligence, which aspiring to replicate human-like problem-solving across various tasks (such as learning from a novel situation, learn from experience, and perform any intellectual task that human can do), while on the other hand, weak AI sometimes referred to narrow AI or specialized AI, operates within specific contexts, excelling in narrowly defined problems (such as voice activated personal assistants, providing weather updates, setting reminders, or answering general knowledge questions etc.) (Flowers, 2019).

The Concept of Machine Learning and Deep Learning

The differentiation between machine learning (ML) and deep learning is important to understand, emphasizing that deep learning is a subset of machine learning and machine learning is a sub-field of AI. Machine learning involves algorithms learning from data without explicit programming, encompassing supervised and unsupervised learning. On the other hand, deep learning employs biologically inspired neural network architectures with hidden layers, allowing for in-depth learning and complex pattern recognition (Janiesch, Zschech & Heinrich, 2021).

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