From High-Level Ethics to Actionable Measures

The active evolution of technology has led to new changes, and one of the trends that has become topical and actively discussed within the academic community and society in general is Artificial Intelligence (AI). Nevertheless, it has turned out that the data on which AI systems are built is not always neutral. This has resulted in cases where AI systems replicate and reinforce existing societal biases. At this point, no specific case law exists to cover the issues related to AI, so ethical frameworks have been developed. Such frameworks typically define top-level principles aimed at guaranteeing the credibility and justice of AI-based systems. However, principles such as explainability and accountability are frequently criticised because they are too abstract, as they do not provide any tangible measures that would guarantee their proper adoption. To develop recommendations on actions that can be taken to address these two principles, a qualitative literature review focusing on these themes has been conducted.