ASME 2026 Competition: Waste-Sorting Robot; Ethical Modeling for Autonomous Vehicle Decision Making and its Societal Implications
This undergraduate thesis portfolio from the University of Virginia combines a technical capstone project on a waste-sorting robot for an ASME competition with an STS research paper examining ethical decision-making models for autonomous vehicles. The research paper advocates for a law-based ethical framework over a utilitarian one, evaluating it on technical feasibility, ethical fairness, and public transparency. Together, the works explore how engineering solutions intersect with broader societal, ethical, and legal implications.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, students, and engineers interested in robotics design and the ethical implications of autonomous vehicles.
What the document contains
The document covers an undergraduate thesis containing a mechanical engineering capstone project on a waste-sorting robot and an STS research paper analyzing ethical modeling, legal outcomes, and societal implications of autonomous vehicles.
It does not provide binding regulatory standards, commercial deployment guidelines, or official legislative policy.
- The technical project details the design and testing of a waste collection and sorting robot for the 2025-2026 ASME Student Design Competition.
- The STS research paper investigates how AI in autonomous vehicles should be programmed to make ethical decisions in unavoidable accident scenarios.
- The paper argues for a law-based ethical framework over a utilitarian framework, citing better accountability and practical implementation.
- The evaluation criteria for the ethical framework consist of technical feasibility, ethical fairness, and public transparency.
· Technical Capstone Project Summary · STS Research Paper Summary
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
3/5Clear responsibility for AI outcomes
The analysis suggests that law-based systems provide clearer accountability and are easier to define, making them more practical for real-world implementation.
STS Research Paper Summary
screening score · 2026-10-23
Fairness
3/5Non-discrimination and equitable treatment
This framework is evaluated on three main criteria: technical feasibility, ethical fairness, and public transparency.
STS Research Paper Summary
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
Abstract
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
Abstract
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
Abstract
screening score · 2026-10-23
Transparency
3/5Openness about how AI systems work and are used
This framework is evaluated on three main criteria: technical feasibility, ethical fairness, and public transparency.
STS Research Paper Summary
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
Abstract
screening score · 2026-10-23
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
The STS research paper aims to answer the question: “How should AI in autonomous vehicles be programmed to make ethical decisions in unavoidable accident scenarios?”
STS Research Paper Summary
screening score · 2026-10-23