Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Programming artificial intelligence (AI) to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair (e.g., slur, insult) or fair (e.g., thank, appreciate). It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector (FairVec). The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec - which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out.This research was funded by the European Union’s Horizon 2020 research and innovation programme under the Next Generation Internet TRUST grant agreement no. 825618

The Golden Rule as a Heuristic to Measure the Fairness of Texts Using Machine Learning

In this paper we present a natural language programming framework to consider how the fairness of acts can be measured. For the purposes of the paper, a fair act is defined as one that one would be accepting of if it were done to oneself. The approach is based on an implementation of the golden rule (GR) in the digital domain. Despite the GRs prevalence as an axiom throughout history, no transfer of this moral philosophy into computational systems exists. In this paper we consider how to algorithmically operationalise this rule so that it may be used to measure sentences such as: the boy harmed the girl, and categorise them as fair or unfair. A review and reply to criticisms of the GR is made. A suggestion of how the technology may be implemented to avoid unfair biases in word embeddings is made - given that individuals would typically not wish to be on the receiving end of an unfair act, such as racism, irrespective of whether the corpus being used deems such discrimination as praiseworthy

Effect of Anodal-tDCS on Event-Related Potentials:A Controlled Study

We aim to measure the postintervention effects of A-tDCS (anodal-tDCS) on brain potentials commonly used in BCI applications, namely, Event-Related Desynchronization (ERD), Event-Related Synchronization (ERS), and P300. Ten subjects were given sham and 1.5 mA A-tDCS for 15 minutes on two separate experiments in a double-blind, randomized order. Postintervention EEG was recorded while subjects were asked to perform a spelling task based on the “oddball paradigm” while P300 power was measured. Additionally, ERD and ERS were measured while subjects performed mental motor imagery tasks. ANOVA results showed that the absolute P300 power exhibited a statistically significant difference between sham and A-tDCS when measured over channel Pz (p=0.0002). However, the difference in ERD and ERS power was found to be statistically insignificant, in controversion of the the mainstay of the litrature on the subject. The outcomes confirm the possible postintervention effect of tDCS on the P300 response. Heightening P300 response using A-tDCS may help improve the accuracy of P300 spellers for neurologically impaired subjects. Additionally, it may help the development of neurorehabilitation methods targeting the parietal lobe

The limits of annotation in machine learning a documents Hohfeldian legal entities

Natural language processing (NLP) summarisers aim to capture the essential elements of a document. Yet, the ontological character of a summary can be domain specific. In legal analysis, the Hohfeldian matrix is used to summarise principle legal relations between agents, such as individuals and organisations. We test a limit of using machine learning (ML) to detect such agents. Based on training with our 2400 hand labelled annotations, an F1= 80.1 is found. Extrapolating this suggests that over one million annotations are required to capture all the agents mentioned in a document. This questions the feasibility of such an approach, one that is unable to be inclusive of all agents who are party to a legal relation. Such complete capture is an essential criteria of fair ML and accurate legal summaries. An alternative approach based on hypernymy is suggested

Using the interest theory of rights and Hohfeldian taxonomy to address a gap in machine learning methods for legal document analysis

Rights and duties are essential features of legal documents. Machine learning algorithms have been increasingly applied to extract information from such texts. Currently, their main focus is on named entity recognition, sentiment analysis, and the classification of court cases to predict court outcome. In this paper it is argued that until the essential features of such texts are captured, their analysis can remain bottle-necked by the very technology being used to assess them. As such, the use of legal theory to identify the most pertinent dimensions of such texts is proposed. Specifically, the interest theory of rights, and the first order Hohfeldian taxonomy of legal relations. These principal legal dimensions allow for a stratified representation of knowledge, making them ideal for the abstractions needed for machine learning. This study considers how such dimensions may be identified. To do so it implements a novel heuristic based in philosophy coupled with language models. Hohfeldian relations of 'rights-duties’ vs. ‘privileges-no-rights’ are determined to be identifiable. Classification of each type of relation to accuracies of 92.5% is found using Sentence Bidirectional Encoder Representations from Transformers. Testing is carried out on religious discrimination policy texts in the United Kingdom

Using the interest theory of rights and Hohfeldian taxonomy to address a gap in machine learning methods for legal document analysis

Abstract Rights and duties are essential features of legal documents. Machine learning algorithms have been increasingly applied to extract information from such texts. Currently, their main focus is on named entity recognition, sentiment analysis, and the classification of court cases to predict court outcome. In this paper it is argued that until the essential features of such texts are captured, their analysis can remain bottle-necked by the very technology being used to assess them. As such, the use of legal theory to identify the most pertinent dimensions of such texts is proposed. Specifically, the interest theory of rights, and the first-order Hohfeldian taxonomy of legal relations. These principal legal dimensions allow for a stratified representation of knowledge, making them ideal for the abstractions needed for machine learning. This study considers how such dimensions may be identified. To do so it implements a novel heuristic based in philosophy coupled with language models. Hohfeldian relations of ‘rights-duties’ vs. ‘privileges-no-rights’ are determined to be identifiable. Classification of each type of relation to accuracies of 92.5% is found using Sentence Bidirectional Encoder Representations from Transformers. Testing is carried out on religious discrimination policy texts in the United Kingdom