Research Articles

On 4 July 2023, the Third Section of the European Court of Human Rights (ECtHR) delivered the first judgment on the compatibility of facial recognition technology with human rights in Glukhin v. Russia. The case concerned the use of facial recognition technology (FRT) against Mr Glukhin following his solo demonstration in the Moscow underground. The Court unanimously found a violation of Article 8 (right to respect for private life) and Article 10 (freedom of expression) of the European Convention of Human Rights (ECHR). Regarding FRT, the Court concluded that the use of highly intrusive technology is incompatible with the ideals and values of a democratic society governed by the rule of law. This case note analyses the judgment and shows its relevance in the current regulatory debate on Artificial Intelligence (AI) systems in Europe. Notwithstanding the importance of this decision, we argue that the Court has left crucial questions unanswered. (Read More)

This study explores Americans’ thoughts and perspectives regarding widespread use of facial recognition technology by law enforcement and beyond. It finds that majorities of the American public believe widespread use of facial recognition would likely help find missing persons and solve crimes, but majorities also think it is likely that police would use this technology to track everyone’s location and surveil Black and Hispanic communities more than others. In terms of potential impact, 46% of U.S. adults say widespread use of facial recognition technology by police would be a good idea for society while 27% believe it would be a bad idea. An additional 27% say they are unsure whether it would be a good or bad idea for police to widely use facial recognition technology. (Read More)

Face recognition is one of the useful tasks and can be used for many applications as security systems, it is necessary to find effective and low complexity facial classifier methods. In this paper, we proposed a new one-dimensional CNN deep convolutional neural network (1D-DCNN) classifier was combined with linear discriminative analysis (LDA) techniques to produce a new face recognition methodology. The contribution of this paper is generated of one-dimensional face feature set by LDA from original image database to training of 1D-DCNN classifier, that it contributed in the improvement of facial recognition performance. The model has been tested on MCUT dataset consisted of 3755 images for 276 classes. The results of the implementation of face recognition were accuracy of 100%, precision of 100%, recall of 100%, and F-measure of 100%. (Read More)

The advantages of employing text analysis to uncover policy positions, generate legal predictions, and inform or evaluate reform practices are multifold. Given the far-reaching effects of legislation at all levels of society these insights and their continued improvement are impactful. This research explores the use of natural language processing (NLP) and machine learning to predictively model U.S. Supreme Court case outcomes based on textual case facts. The final model achieved an F1-score of .324 and an AUC of .68. This suggests that the model can distinguish between the two target classes; however, further research is needed before machine learning models are used in the Supreme Court. (Read More)

Factual scenario analysis of a judgement is critical to judges during sentencing. With the increasing number of legal cases, professionals typically endure heavy workloads on a daily basis. Although a few previous studies have applied information technology to legal cases, according to our research, no prior studies have predicted a pending judgement using legal documents. In this article, we introduce an innovative solution to predict relevant rulings. The proposed approach employs text mining methods to extract features from precedents and applies a text classifier to automatically classify judgements according to sentiment analysis. This approach can assist legal experts or litigants in predicting possible judgements. Experimental results from a judgement data set reveal that our approach is a satisfactory method for judgement classification. (Read More)

A large amount of information is available on the internet. Topic modeling is a technique that can discover hidden patterns from these documents. The paper aims to find those hidden patterns from the legal documents and then apply sentiment analysis. This paper discusses the importance of topic modeling and sentiment analysis to deduce the document’s meaning. It also discusses the application of topic modeling on legal documents. (Read More)