POLARIS: A framework to guide the development of Trustworthy AI systems
<|reference_start|>POLARIS: A framework to guide the development of Trustworthy AI systems: In the ever-expanding landscape of Artificial Intelligence (AI), where innovation thrives and new products and services are continuously being delivered, ensuring that AI systems are designed and developed responsibly throughout their entire lifecycle is crucial. To this end, several AI ethics principles and guidelines have been issued to which AI systems should conform. Nevertheless, relying solely on high-level AI ethics principles is far from sufficient to ensure the responsible engineering of AI systems. In this field, AI professionals often navigate by sight. Indeed, while recommendations promoting Trustworthy AI (TAI) exist, these are often high-level statements that are difficult to translate into concrete implementation strategies. There is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals. To address this challenge, our work presents an experience report where we develop a novel holistic framework for Trustworthy AI - designed to bridge the gap between theory and practice - and report insights from its application in an industrial case study. The framework is built on the result of a systematic review of the state of the practice, a survey, and think-aloud interviews with 34 AI practitioners. The framework, unlike most of those already in the literature, is designed to provide actionable guidelines and tools to support different types of stakeholders throughout the entire Software Development Life Cycle (SDLC). Our goal is to empower AI professionals to confidently navigate the ethical dimensions of TAI through practical insights, ensuring that the vast potential of AI is exploited responsibly for the benefit of society as a whole.<|reference_end|>
@article{baldassarre2024polaris:,
    title={POLARIS: A framework to guide the development of Trustworthy AI systems},
    author={Maria Teresa Baldassarre, Domenico Gigante, Marcos Kalinowski, Azzurra
  Ragone},
    journal={Conference on AI Engineering Software Engineering for AI (CAIN
  2024), April 14--15, 2024, Lisbon, Portugal},
    year={2024},
    doi={10.1145/3644815.3644947},
    archivePrefix={arXiv},
    eprint={2402.05340},
    primaryClass={cs.SE}
}