The Council of Europe is taking part in the third edition (online) of the "Athens Roundtable on Artificial Intelligence and the Rule of Law" on 6 and 7 December. Organised by the Future Society and ELONTech under the Patronage of the President of the Hellenic Republic, Katerina Sakellaropoulou, the event is co-hosted by UNESCO, the Council of Europe, the European Parliament's Panel on the Future of Science and Technology (STOA), IEEE SA, the Center on Civil Justice at the NYU School of Law and the National Judicial College, among other institutions. It will also address important issues at the intersection of AI, industry, government and law, including civil liability regimes, regulatory compliance, privacy and consumer protection, and judicial capacity building. Council of Europe Secretary General Marija Pejčinović Burić is speaking at the opening. The Director of Information Society – Action against Crime, Jan Kleijssen, is taking part in the panel "EU AI Act and Beyond: Regulatory Perspectives from Europe and the United States" and the Head of the Information Society Department, Patrick Penninckx in the panel on "AI and Human Rights".
The Center for Innovation, Administration and Research in the Judiciary (CIAPJ) of the Getulio Vargas Foundation (FGV) released the report of the first phase of the research "Technology applied to conflict resolution in the Brazilian Judiciary " (click here to obtain the PDF document) carried out in December 2020. This research was coordinated by the Minister of the Superior Court of Justice (STJ) Luis Felipe Salomão. The research covered 3 of the 5 branchs of the Brazilian Judiciary: State Justice, Labor Justice, Federal Justice, Electoral Justice and Military Justice. The collection of these data was carried out with 59 (fifty-nine) courts (Federal Supreme Court -- STF, Superior Court of Justice -- STJ, Superior Labor Court -- TST, Regional Labor Courts, Federal Regional Courts and Courts of Justice) and the National Council of Justice. The report indicates that half of the courts have an artificial intelligence project under development or already implemented.
Unlike the courts in western countries, public records of Indian judiciary are completely unstructured and noisy. No large scale publicly available annotated datasets of Indian legal documents exist till date. This limits the scope for legal analytics research. In this work, we propose a new dataset consisting of over 10,000 judgements delivered by the supreme court of India and their corresponding hand written summaries. The proposed dataset is pre-processed by normalising common legal abbreviations, handling spelling variations in named entities, handling bad punctuations and accurate sentence tokenization. Each sentence is tagged with their rhetorical roles. We also annotate each judgement with several attributes like date, names of the plaintiffs, defendants and the people representing them, judges who delivered the judgement, acts/statutes that are cited and the most common citations used to refer the judgement. Further, we propose an automatic labelling technique for identifying sentences which have summary worthy information. We demonstrate that this auto labeled data can be used effectively to train a weakly supervised sentence extractor with high accuracy. Some possible applications of this dataset besides legal document summarization can be in retrieval, citation analysis and prediction of decisions by a particular judge.
During the pandemic, technology companies have been pitching their emotion-recognition software for monitoring workers and even children remotely. Take, for example, a system named 4 Little Trees. Developed in Hong Kong, the program claims to assess children's emotions while they do classwork. It maps facial features to assign each pupil's emotional state into a category such as happiness, sadness, anger, disgust, surprise and fear. It also gauges'motivation' and forecasts grades.
Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.
AI in Supreme Court is, by all means, catching the eye of a huge segment of individuals, no question due to the limitless potential outcomes it offers. It absorbs, contributes just as stances difficulties to practically all disciplines including theory, psychological science, financial aspects, law, and the sociologies. AI and Machine Learning have a multiplier impact on expanding the effectiveness of any system or industry. If utilized adequately, it can bring steady change and transform the biological system of a few areas. In any case, before applying such innovation, it is important to distinguish the issues and the difficulties within every area and foster the particular modalities on how artificial intelligence will have the most elevated effect.
There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.
An automated system that could assist a judge in predicting the outcome of a case would help expedite the judicial process. For such a system to be practically useful, predictions by the system should be explainable. To promote research in developing such a system, we introduce ILDC (Indian Legal Documents Corpus). ILDC is a large corpus of 35k Indian Supreme Court cases annotated with original court decisions. A portion of the corpus (a separate test set) is annotated with gold standard explanations by legal experts. Based on ILDC, we propose the task of Court Judgment Prediction and Explanation (CJPE). The task requires an automated system to predict an explainable outcome of a case. We experiment with a battery of baseline models for case predictions and propose a hierarchical occlusion based model for explainability. Our best prediction model has an accuracy of 78% versus 94% for human legal experts, pointing towards the complexity of the prediction task. The analysis of explanations by the proposed algorithm reveals a significant difference in the point of view of the algorithm and legal experts for explaining the judgments, pointing towards scope for future research.