Analysis of the Performance of Different Accountability Strategies for Autonomous Robots
This academic paper presents a generic approach to developing an accountability system for autonomous robots consisting of a system event logger, message producer, distributed event streaming platform, and a database. The proposed solution is tested in assessment scenarios to define strategies that minimize the impact of auditing and logging tasks on robot performance. Published in September 2021 as a conference chapter, it analyzes performance strategies for accountability systems in robotics.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and developers seeking technical approaches to implement accountability and logging systems in robotics.
What the document contains
This paper addresses technical logging, auditing, and accountability architectures for autonomous robots to trace and anticipate system behavior while minimizing performance impacts.
The text does not provide regulatory compliance guidelines, legal definitions, or enforcement mechanisms.
- Efficient logging engines are necessary to determine causes of events and enforce human trust in robotic computing systems.
- The authors present a generic accountability system architecture comprising an event logger, message producer, distributed event streaming platform, and database.
- Testing different assessment scenarios helps define strategies to reduce the performance impact of auditing and logging tasks on robots.
- The work highlights limitations in traditional logging systems such as incomplete recording and inclusion of unhelpful data.
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
This work presents a generic approach to developing an accountability system that consists of four main components
Abstract
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
Abstract
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
Abstract
screening score · 2026-10-24
Human Oversight
1/5Humans remain in control of consequential decisions
Providing an efficient logging engine to determine the causes of an event is needed to enforce humans trust in computing systems.
Abstract
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
Abstract
screening score · 2026-10-24
Transparency
1/5Openness about how AI systems work and are used
characteristics such as incomplete recording of events, the inclusion of unhelpful data, or the impact on robot performance, make it necessary to conceive new accountability solutions
Abstract
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
Abstract
screening score · 2026-10-24
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
Abstract
screening score · 2026-10-24