Quality of data sets that feed AI and big data applications for law enforcement

Abstract

In the era of big data and artificial intelligence (AI), where aggregated data is used to learn about patterns and for decision-making, quality of input data seems to be of paramount importance. Poor data quality may lead not only to wrong outcomes, which will simply render the application useless, but more importantly to fundamental rights breaches and undermined trust in the public authorities using such applications. In law enforcement as in other sectors the question of how to ensure that data used for the development of big data and AI applications meet quality standards remains. This paper provides an overview of this topic, reporting selected issues stemming from big data, nonpersonal data and regulatory contexts. It concludes that the topic is still underexplored and sets areas for further research.