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.
Computable contracts simplify financial transactions and reduce legal costs.
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…
Examines AI regulation in finance, highlighting risks and gaps in current laws.
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…
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…
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.
Paper presents LLM-enhanced contract metadata extraction.
New approach to counterfactual reasoning avoids demographic interventions.
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…
Insider trading is reduced when penalized, affecting expected penalties in a non-monotone way.
Paper proposes GSSNMF for legal document classification and topic modeling.
New framework for contesting algorithmic decisions, not just explaining them.
New AI framework without networks outperforms traditional models.
Federated learning improves bioinformatics by sharing data legally.
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…
Research creates a taxonomy to bridge AI security and regulatory gaps.
This paper systematizes knowledge on synthetic assets in crypto.
This research improves debt collection strategies using advanced machine learning.
GPT learns a causal world model from token predictions, validated in game sequences.
At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, can…
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…
Maximal correlation framework improves fairness in machine learning algorithms.
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…
Offline RL algorithms protect privacy while learning from sensitive data.
Machine learning has shown much promise in helping improve the quality of medical, legal, and financial decision-making. In these applications, machine learning models must satisfy two important criteria: (i) they must be causal, since the goal is typically to predict individual treatment effects, and (ii) they must be…
This paper develops efficient federated learning and unlearning methods in Bayesian models.
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…
Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfe…
Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions…
Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a …
Develops a new tensor PCA method for analyzing multiple network data.
Proposes a method to enforce fairness in machine learning models without sensitive data.
In the framework of applying econophysics ideas in religious topics, the finances of the Antoinist religious movement organized in Belgium between 1920 and 2000 are studied. The interest of investigating financial aspects of such a, sometimes called, sect stems in finding characteristics of conditions and mechanisms un…
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…
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. In a successful adversarial attack, the targeted mis-classification should be ach…
Decentralized finance uses blockchain for $70B in assets, differing from traditional finance.
Privacy policies are legal documents that describe how a website will collect, use, and distribute a user's data. Unfortunately, such documents are often overly complicated and filled with legal jargon; making it difficult for users to fully grasp what exactly is being collected and why. Our solution to this problem is…
The authors discuss fairness in machine learning and introduce new methods.
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…
Survey examines public views on facial recognition technology.