AI Alignment: Ensuring AI Objectives Match Human Values

AI Alignment: Ensuring AI Objectives Match Human Values

 

 

Shivam Singh1

Bachelor of Technology (CSE)

Kalinga University

Raipur, India

singhs28450@gmail.com

 

Ashutosh Kumar3

Bachelor of Technology (CSE)

Kalinga University

Raipur, India

phantushkumar512@gmail.com

 

Avinash Jha2

Bachelor of Technology (CSE)

Kalinga University

Raipur, India

avirov87kumar@gmail.com

 

Nissi Jacob4

Bachelor of Technology (CSE)

Kalinga University

Raipur, India

jacobnissi3@gmail.com

 

Ms. Sonali Mondal

Assistant Professor

Faculty of CS & IT

Kalinga University

Raipur, India

Sonali.mondal@kalingauniversity.ac.in

 

 

 

Abstract :

As artificial intelligence systems grow more advanced and self-governing, the issue of AI alignment—ensuring that these systems follow objectives consistent with human values—has become one of the most pressing topics in AI safety and ethics. Even in well-constructed systems, misaligned goals can result in unexpected behaviors that might lead to harmful or ethically dubious outcomes. This research paper delves into the conceptual underpinnings, technical strategies, and societal impacts of AI alignment. The discussion starts by exploring the theoretical foundations of alignment, focusing on models related to human values, utility functions, and the learning of preferences. Following this, the paper evaluates existing approaches like inverse reinforcement learning, cooperative inverse reinforcement learning, and reward modeling, analyzing their advantages, drawbacks, and real-world applicability. By conducting a comparative analysis of case studies and simulations, the research underscores significant challenges in implementing human values, such as ambiguity in values, dependence on context, and the potential for specification gaming. It also stresses the necessity of integrating ethical pluralism and a variety of human viewpoints. Additionally, the study examines the significance of interpretability, transparency, and interdisciplinary collaboration in improving alignment results.

Research indicates that no single method provides a comprehensive solution; however, a hybrid, multi-dimensional strategy—rooted in human-centered design and ongoing feedback—appears most promising. The study emphasizes the pressing need for proactive alignment strategies as AI systems become more integrated into critical areas like healthcare, governance, and autonomous decision-making. Ultimately, achieving strong AI alignment is not merely a technical issue but a profoundly human challenge that necessitates contributions from technologists, ethicists, and society as a whole to ensure AI benefits the common good.

Keywords: AI Alignment, Human Values, Ethical Artificial Intelligence, Value Learning, Inverse Reinforcement Learning.