Risk Analysis of Data Misuse in Autonomous Robot Research: A Case Study of Violations of the Principles of Transparency and Algorithm Accountability

Abstract

The development of autonomous robots in the era of Industry 4.0 presents enormous opportunities as well as significant ethical risks, particularly in relation to data collection and use. The complexity of artificial intelligence (AI)-based algorithms has led to the emergence of the black box phenomenon, where decisionmaking processes are difficult to explain and verify. This condition creates violations of two key ethical principles, namely transparency and accountability, which have the potential to increase the risk of data misuse throughout the entire life cycle of autonomous robot research. This study systematically analyzes the relationship between violations of these principles and various threat scenarios such as algorithmic discrimination, invasive profiling, and sensor data manipulation. Using a qualitative case study approach and a four-phase risk analysis method, the study identifies two critical risks: decision discrimination due to black box models, and sensor data manipulation due to weak accountability and audit mechanisms. The results confirm that a lack of transparency hinders the detection of data bias, while weak accountability opens the door to third-party intervention. This study recommends the implementation of Explainable AI (XAI), training data audits, tamper-proof audit log systems, and rollback mechanisms as key mitigation measures to improve the security, reliability, and ethics of data use in autonomous robot research.