While the concept of human-centric artificial intelligence (AI) has emerged as a key principle to govern AI systems, two obstacles for its implementation remain largely understated. First, the excessive focus on accountability at the design stage of AI systems, overshadowing the fact that human values can be affected at different stages across the AI life cycle. Second, the market-driven approach of current regulatory initiatives, limited in their ability to actively promote human values. In this article, we argue for a twofold approach to tackle these limitations. On one hand, we propose a co-evolutionary and life cycle approach to tackle the lack of accountability of AI systems, showing that this approach can help ensure accountability beyond the design stage by enabling meaningful human control and human-AI interaction across the entire lifecycle of the system. On the other hand, we propose that regulatory initiatives should balance the market-driven approach by giving a more predominant role to human rights and by introducing explicitly the notion of proportionality test. This rebalancing would serve to handle conflicts between the objectives pursued by AI systems circulating in the markets and the need for an effective protection of human rights.

The General Data Protection Regulation (GDPR) requires data controllers to implement end-to-end compliance. Controllers must therefore ensure that the terms agreed with the data subject and their own obligations under GDPR are respected in the data flows from data subject to controllers, processors and sub processors (i.e. data supply chain). This paper seeks to contribute to bridge both ends of compliance checking through a two-pronged study. First, we conceptualize a framework to implement a document-centric approach to compliance checking in the data supply chain. Second, we develop specific methods to automate compliance checking of privacy policies. We test a two-modules system, where the first module relies on NLP to extract data practices from privacy policies. The second module encodes GDPR rules to check the presence of mandatory information. The results show that the text-to-text approach outperforms local classifiers and enables the extraction of both coarse-grained and fine-grained information with only one model. We implement full evaluation of our system on a dataset of 30 privacy policies annotated by legal experts. We conclude that this approach could be generalized to other documents in the data supply as a means to improve end-to-end compliance.

In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i.e. contexts). Mechanisms for utilizing linguistic resources outside of their original context have significant potential benefits in AI & Law because differences between legal systems, languages, or traditions often block wider adoption of research outcomes. We analyze the use of Language-Agnostic Sentence Representations in sequence labeling models using Gated Recurrent Units (GRUs) that are transferable across languages. To investigate transfer between different contexts we developed an annotation scheme for functional segmentation of adjudicatory decisions. We found that models generalize beyond the contexts on which they were trained (e.g., a model trained on administrative decisions from the US can be applied to criminal law decisions from Italy). Further, we found that training the models on multiple contexts increases robustness and improves overall performance when evaluating on previously unseen contexts. Finally, we found that pooling the training data from all the contexts enhances the models' in-context performance.