{'fraction_non_alphanumeric': 0.017346938775510204, 'fraction_numerical': 0.0, 'mean_word_length': 5.411764705882353, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Accountable Data Analytics Start with Accountable Data: The LiQuID Metadata Model

Insights based on data are omnipresent. However, in particular in modern data analytics applications, information about the underlying data often remain obscure, hindering accountable data analytics. Recent efforts have been put into better describing such data based on metadata, similarly to what has been done in various scientific disciplines for transparent and reproducible research. Based on a detailed study of various metadata standards and proposals, we observe that existing metadata models do not yet sufficiently cover information that is relevant for data accountability. To fill this gap, this paper proposes LiQuID, a novel metadata model to make datasets accountable throughout their life cycle. It is more general than existing metadata models, which can be mapped to LiQuID. We validate LiQuID for the purpose of dataset accountability based on a real-world workload we created.