Keywords
explainability
civil liability
accountability
risk assessment
How to Cite
Abstract
The purpose of this paper is to elucidate the intersection of algorithmic explainability and civil liability, exploring the implications of complex algorithms on legal responsibility. Algorithms, while not inherently intelligent, depend on data to present probabilistic predictions, differing significantly from human intuition. The core of this study lies in examining the limits of liability for damages caused by sophisticated algorithms, considering their inherent unpredictability. By analyzing the accountability framework proposed by scholars like Frank Pasquale, Mark Coeckelbergh, and Giovanni Comandé, this paper highlights the importance of data-informed duties and relational justifications as elements of the principle of explainability. It underscores the need for a proactive approach to risk management and the establishment of ethical standards for trustworthy AI. The discussion includes the necessity of regulatory guidelines that balance technological innovation with preventive measures that ensures transparency, predictability, and accountability in the deployment of algorithmic systems.
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