Publications
A Framework for Humane AI Interaction
This paper introduces a new framework for designing AI systems that prioritize human values and well-being. We argue that by focusing on transparency, controllability, and user agency, we can create AI that is not only powerful but also humane.
The Illusion of Reasoning in Large Language Models
While large language models demonstrate impressive capabilities, their reasoning abilities are often superficial. This paper explores the limitations of current models and suggests pathways toward more robust and interpretable reasoning.
Past Publications
Designing Ethical AI Systems: A Historical Perspective
Drawing from the history of computing and ethical frameworks, this paper proposes guidelines for developing AI systems that align with human values and societal needs.
Understanding Bias in Machine Learning Models
This study examines various sources of bias in machine learning models and proposes methods for detection and mitigation to ensure fair and equitable AI systems.
The Role of Human Oversight in Automated Decision Making
As AI systems become more prevalent in decision-making processes, this paper explores the critical importance of maintaining human oversight and control.