Trust or Bust: Ensuring Trustworthiness in Autonomous Weapon Systems
It addresses the critical issue of trust in AWS for military applications, but it is incremental as it reviews literature and proposes collaborative solutions without new empirical results.
This paper tackles the problem of ensuring trustworthiness in Autonomous Weapon Systems (AWS) by identifying gaps in trust dynamics and advocating for a collaborative approach to mitigate risks like bias and operational failures, aiming to contribute to ethical discourse and trustworthy AI in defense.
The integration of Autonomous Weapon Systems (AWS) into military operations presents both significant opportunities and challenges. This paper explores the multifaceted nature of trust in AWS, emphasising the necessity of establishing reliable and transparent systems to mitigate risks associated with bias, operational failures, and accountability. Despite advancements in Artificial Intelligence (AI), the trustworthiness of these systems, especially in high-stakes military applications, remains a critical issue. Through a systematic review of existing literature, this research identifies gaps in the understanding of trust dynamics during the development and deployment phases of AWS. It advocates for a collaborative approach that includes technologists, ethicists, and military strategists to address these ongoing challenges. The findings underscore the importance of Human-Machine teaming and enhancing system intelligibility to ensure accountability and adherence to International Humanitarian Law. Ultimately, this paper aims to contribute to the ongoing discourse on the ethical implications of AWS and the imperative for trustworthy AI in defense contexts.