A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper formalizes anti-discrimination law in automated systems.
Smart Close-out Netting aims to automate close-out netting processes.
Hybrid approach combines topic and graph embeddings for legal document clustering.
This paper predicts legal proceedings status using NLP and machine learning.
The digitalization of the legal domain has been ongoing for a couple of years. In that process, the application of different machine learning (ML) techniques is crucial. Tasks such as the classification of legal documents or contract clauses as well as the translation of those are highly relevant. On the other side, di…
What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, g…
Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processi…
Past literature has been effective in demonstrating ideological gaps in machine learning (ML) fairness definitions when considering their use in complex socio-technical systems. However, we go further to demonstrate that these definitions often misunderstand the legal concepts from which they purport to be inspired, an…
Causal discovery algorithms can help generate legal arguments.
In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testi…
LexNLP is an open source Python package focused on natural language processing and machine learning for legal and regulatory text. The package includes functionality to (i) segment documents, (ii) identify key text such as titles and section headings, (iii) extract over eighteen types of structured information like dis…
Study identifies and measures biases in legal case data.
Formulates LGFO to measure fair ML systems using legal signals.
ClauseLens uses reinforcement learning to price reinsurance treaties transparently and auditably.
Insiders camouflage trading to balance wealth and stealth, avoiding legal penalties.
Offline RL algorithms protect privacy while learning from sensitive data.
This research improves debt collection strategies using advanced machine learning.
Paper presents LLM-enhanced contract metadata extraction.
Insider trading is reduced when penalized, affecting expected penalties in a non-monotone way.
Examines AI regulation in finance, highlighting risks and gaps in current laws.
This paper develops a structural credit risk model to characterize the difference between the economic and recorded default times for a firm. Recorded default occurs when default is recorded in the legal system. The economic default time is the last time when the firm is able to pay off its debt prior to the legal defa…
Paper proposes GSSNMF for legal document classification and topic modeling.
Proposes a deep learning model for probabilistic forecasting that is also interpretable.
Computable contracts simplify financial transactions and reduce legal costs.
Federated learning improves bioinformatics by sharing data legally.
Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA) which enables a continuous interpol…
New approach to counterfactual reasoning avoids demographic interventions.
GPT learns a causal world model from token predictions, validated in game sequences.
Machine Learning community is recently exploring the implications of bias and fairness with respect to the AI applications. The definition of fairness for such applications varies based on their domain of application. The policies governing the use of such machine learning system in a given context are defined by the c…
Deep learning methods are often difficult to apply in the legal domain due to the large amount of labeled data required by deep learning methods. A recent new trend in the deep learning community is the application of multi-task models that enable single deep neural networks to perform more than one task at the same ti…
Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions are neither regulated nor certified. To help counter the potential harm that suc…
This paper presents thirteen datasets for binary, multiclass and multilabel classification based on the European Court of Human Rights judgments since its creation. The interest of such datasets is explained through the prism of the researcher, the data scientist, the citizen and the legal practitioner. Contrarily to m…
Market manipulation is a strategy used by traders to alter the price of financial securities. One type of manipulation is based on the process of buying or selling assets by using several trading strategies, among them spoofing is a popular strategy and is considered illegal by market regulators. Some promising tools h…
Browsing and finding relevant information for Bangladeshi laws is a challenge faced by all law students and researchers in Bangladesh, and by citizens who want to learn about any legal procedure. Some law archives in Bangladesh are digitized, but lack proper tools to organize the data meaningfully. We present a text vi…
Decentralized finance uses blockchain for $70B in assets, differing from traditional finance.
In this note we describe the application of existing smart contract technologies with the aim to construct a new digital representation of a financial derivative contract. We compare several existing DLT based technologies. We provide a detailed description of two separate prototypes which are able to be executed on a …
Survey examines public views on facial recognition technology.
Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data. Full…
New framework for contesting algorithmic decisions, not just explaining them.
tDB removes unfairness from black-box models using probability theory.
Classifies privacy policy segments for better user understanding.
This paper analyzes crypto white papers under MiCAR, highlighting NLP's role.
Provides a compendium of data sources for various applications.
The online environment has provided a great opportunity for insurance policyholders to share their complaints with respect to different services. These complaints can reveal valuable information for insurance companies who seek to improve their services; however, analyzing a huge number of online complaints is a compli…
Proposes a secure communication method independent of eavesdropper's decoder.
A distinction has been drawn in fair machine learning research between `group' and `individual' fairness measures. Many technical research papers assume that both are important, but conflicting, and propose ways to minimise the trade-offs between these measures. This paper argues that this apparent conflict is based on…
This paper systematizes knowledge on synthetic assets in crypto.
This study categorizes RWA tokenization challenges and solutions.