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AI Ethics: A Comprehensive Analysis and Key Issues


Core Concepts
AI ethics has evolved through distinct phases, from incubation to making AI human-like machines and progressing towards human-centric AI systems.
Abstract

This article delves into the evolution of AI ethics over the past two decades, highlighting key issues and gaps in the field. It conducts a comprehensive bibliometric analysis, identifies three development phases, presents seven pivotal debates in AI ethics, and discusses two significant research gaps.

Directory:

  • Introduction to AI Ethics Evolution
    • Emergence of AI Ethics Literature
    • Three Development Phases Identified
  • Key Issues in AI Ethics
    • Collingridge Dilemma
    • AI Status Debate
    • Transparency and Explainability Challenges
    • Privacy Protection Concerns
    • Justice and Fairness Considerations
    • Algocracy and Human Enfeeblement Risks
    • Superintelligence Implications
  • Research Gaps in AI Ethics
    • Large Ethics Model (LEM)
    • AI Identification
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Stats
The term "AI ethics" first appeared in literature keywords in 2008. In the partial year of 2023, "AI ethics" has already appeared 114 times.
Quotes
"The study of AI ethics is the study of the ethical and responsible development and deployment of artificial intelligence technology." "AI ethics research may leverage the approaches used by large language models (LLM) to get away from conventional approaches."

Key Insights Distilled From

by Di Kevin Gao... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14681.pdf
AI Ethics

Deeper Inquiries

How can policymakers address the challenges posed by algocracy?

Algocracy presents significant ethical and societal challenges, particularly in terms of decision-making processes and power dynamics. Policymakers can address these challenges by implementing regulations that ensure transparency, accountability, and fairness in algorithmic governance. This may involve creating oversight bodies or regulatory frameworks specifically focused on monitoring the use of AI algorithms in decision-making processes. Additionally, policymakers could promote public awareness campaigns to educate citizens about the implications of algocracy and engage stakeholders in discussions about its potential risks.

What are the potential consequences of developing superintelligent machines?

The development of superintelligent machines poses various potential consequences, including ethical dilemmas, societal disruptions, and existential risks. One major concern is the asymmetrical power dynamic between humans and superintelligent entities, which could lead to issues related to control and autonomy. There is also a risk of job displacement due to automation at an unprecedented scale, impacting various industries and sectors globally. Furthermore, there are existential risks associated with superintelligence if not managed properly, such as catastrophic outcomes for humanity if these machines surpass human intelligence beyond our ability to control or understand.

How can a standardized system for AI identification benefit society?

Implementing a standardized system for AI identification can bring several benefits to society. Firstly, it would enable better tracking and categorization of different AI products or systems used across various domains. This would enhance transparency for users interacting with AI technologies by providing clear information about their nature and origin. Additionally, an AI identification system could facilitate ethical evaluations by assigning unique IDs to AI instances based on predefined criteria set forth by regulatory bodies or industry standards organizations. Overall, this system would contribute to increased accountability among developers while empowering consumers with more informed choices when engaging with AI technology.
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