Complex financial instruments allow insider trading without actual trades.
problem How to commit insider trading without executing a trade.
method Use of financial instruments to replicate the economic effect of trading.
result Insider trading can be committed without an actual trade.
The paper highlights legal misunderstandings in ML fairness definitions.
problem Misalignment between ML fairness definitions and legal concepts.
method Examples and comparative analysis of legal and ML terminology.
result Both communities need to learn from these tensions.
Hybrid approach combines topic and graph embeddings for legal document clustering.
problem Challenges in classifying legal texts due to domain-specific language and limited labeled data.
method Combines unsupervised topic and graph embeddings with a supervised model.
result Improves clustering quality over text-only or graph-only embeddings.
This paper improves NEL for legal documents using transfer learning.
problem Linking named entities in legal documents.
method Transfer learning approach applied to legal datasets.
result Improved F1-score of 98.90% and 98.01% on legal datasets.
Deep learning improves legal document translation, summarization, and classification.
problem Data scarcity in legal document processing.
method Multi-task deep learning to leverage transfer learning.
result Multi-task DL outperformed state-of-the-art results in all tasks.
Paper formalizes anti-discrimination law in automated systems.
problem Algorithmic discrimination in legal contexts.
method Decision-theoretic framework grounded in UK anti-discrimination law.
result Introduced 'conditional estimation parity' metric for ML fairness.
Causal discovery algorithms can help generate legal arguments.
problem Leveraging causal discovery algorithms in legal decision-making.
method Prepared a legal dataset, annotated with 17 legal concepts, applied causal discovery algorithms, and quantified degrees of belief.
result Some causal relationships help generate viable legal arguments.
This paper predicts legal proceedings status using NLP and machine learning.
problem Classify Brazilian legal proceedings into archived, active, and suspended categories.
method Combined NLP techniques with machine learning to classify legal proceedings sequences.
result Achieved maximum accuracy of 93% and top average F1 Scores of 89% (macro) and 93% (weighted).
Study identifies and measures biases in legal case data.
problem Addressing representation biases and sentencing disparities in legal case data.
method Utilizes two regression models: a baseline and a fair judge model.
result Quantifies biases across demographic groups in criminal data from Cook County (Illinois).
This study identifies sentence relationships in legal transcripts.
problem Improving understanding of legal case proceedings through sentence relationships.
method Combining machine learning and rule-based approach to classify sentence relationships.
result First study to use discourse relationships for legal court case transcripts.
Formulates LGFO to measure fair ML systems using legal signals.
problem Formally incompatible measures of unfairness in ML systems.
method Uses legal signals to measure social cost of unfairness.
result LGFO aligns with societal view of unfairness.
Insiders camouflage trading to balance wealth and stealth, avoiding legal penalties.
problem Legal penalties and insider trading among liquidity traders.
method Kyle-type model with a diverse spectrum of prosecution schemes.
result Existence and uniqueness of equilibria for large populations, with a stealth index revealing trading scale.
Paper presents LLM-enhanced contract metadata extraction.
problem Automatic detection and annotation of legal clauses in contracts.
method Integration of publicly available and proprietary datasets with advanced LLM methodologies.
result Substantial improvements in clause identification accuracy and efficiency.
LexNLP processes legal and regulatory texts for structured information.
problem Processing structured information from legal and regulatory texts.
method Natural language processing and machine learning for legal and regulatory text.
result LexNLP extracts over eighteen types of structured information from legal and regulatory texts.
Smart Close-out Netting aims to automate close-out netting processes.
problem Inefficiencies in close-out netting processes for financial institutions.
method Standardisation and automation of legal and regulatory processes using a data-driven framework and controlled natural language.
result Standardisation and automation can improve close-out netting processes for prudentially regulated financial institutions.
Insider trading is reduced when penalized, affecting expected penalties in a non-monotone way.
problem Reducing insider trading behavior when insiders face legal penalties.
method Characterized via a backward stochastic differential equation (BSDE) with a non-linear operator.
result The insider's expected penalties are non-monotone in the fee structure and determined by relative entropy.
Extract fairness policies from legal documents using machine learning.
problem Extract fairness policies from legal documents for AI applications.
method Two approaches based on semantic relatedness: Wordnet-based similarity and vector-based similarity.
result Vector-based similarity outperforms classical Wordnet-based similarity.
Bi-LSTM classifies legal documents for Brazil's supreme court.
problem Clogging of Brazil's supreme court due to high volume of lawsuit cases.
method Used a Bidirectional Long Short-Term Memory (Bi-LSTM) network.
result Successfully classified legal documents for efficient case allocation.
Paper proposes GSSNMF for legal document classification and topic modeling.
problem Lack of methods that can both classify and model topics with guidance.
method Guided Semi-Supervised Non-negative Matrix Factorization (GSSNMF).
result Improves both classification accuracy and topic coherence.
Solves reinforcement learning tasks by guiding policies with a penalty signal.
problem Learning policies that exploit reward loopholes and misspecifications.
method Multi-timescale approach using a penalty signal for constraint satisfaction.
result Proves the convergence of Reward Constrained Policy Optimization (RCPO).
Computable contracts simplify financial transactions and reduce legal costs.
problem Difficulty in querying, executing, and analyzing text-based financial contracts.
method Develop a Contract Definition Language and illustrate use cases.
result Substantial improvements in customer experience and cost reduction.
Deep learning model tackles illegal comments with promising results.
problem Lack of labeled data in legal domain.
method Multi-task deep learning model for classification.
result Promising results in classifying illegal comments.
Federated learning improves bioinformatics by sharing data legally.
problem Lack of access to diverse data in bioinformatics.
method Combines data from multiple institutions legally.
result Federated learning accelerates clinical discovery and robust exploration.
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…
Visualizes Bangladeshi laws for quicker searching.
problem Difficulty in finding relevant Bangladeshi laws.
method Doc2Vec for node layout, link mining for citation networks, named entity recognition for quick section finding.
result Users find the tool faster and more intuitive for legal research.
ClauseLens uses reinforcement learning to price reinsurance treaties transparently and auditably.
problem Opaque and difficult-to-audit reinsurance treaty pricing practices.
method ClauseLens models treaty pricing as a Risk-Aware Constrained Markov Decision Process (RA-CMDP), incorporating legal clauses and generating interpretable explanations.
result ClauseLens reduces solvency violations and improves tail-risk performance, achieving 88.2% accuracy in clause-grounded explanations.
This work examines the legal requirement for AI systems to provide explanations.
problem The need for accountability in AI systems that can have significant impacts on individuals.
method Review of legal contexts requiring explanation from AI systems and technical considerations for providing such explanations.
result Technical challenges in providing explanations that align with legal requirements for AI systems.
New approach to counterfactual reasoning avoids demographic interventions.
problem Limitations of traditional counterfactual reasoning in AI systems.
method Backtracking counterfactual approach instead of interventional.
result Allows addressing social concerns without demographic interventions.
GPT learns a causal world model from token predictions, validated in game sequences.
problem Does GPT implicitly learn a causal world model from token predictions?
method Derived a causal interpretation of GPT's attention mechanism and proposed zero-shot causal structure learning.
result GPT can generate legal next moves with high confidence for sequences with encoded causal structures, but fails for illegal moves.
Law responds to adversarial machine learning threats.
problem Adversarial machine learning attacks and their legal implications.
method Scenarios and legal analysis of adversarial ML attacks.
result Some attacks are more likely to result in liability.
Offline RL algorithms protect privacy while learning from sensitive data.
problem Learning sensitive data in financial, legal, and healthcare applications.
method Differential privacy guarantees for offline RL algorithms.
result Provable prevention of privacy risks with strong learning bounds.
Examines AI regulation in finance, highlighting risks and gaps in current laws.
problem Rapid AI adoption in finance introduces risks and compliance challenges.
method Reviews current legislation, industry guidelines, and real-world use cases.
result Need for adaptive, technology-neutral policies to balance innovation and consumer protection.
Smart contracts create digital financial derivatives.
problem Creating a new digital financial derivative contract.
method Applied existing smart contract technologies to develop two prototypes.
result Demonstrated feasibility of digital financial derivatives on centralized and DLT platforms.
Decentralized finance uses blockchain for $70B in assets, differing from traditional finance.
problem Ensuring compliance and security in decentralized finance.
method Systematic analysis of legal, economic, security, and privacy aspects.
result Decentralized finance offers unique economic effects and security features.
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.
problem Public acceptance and privacy concerns with facial recognition.
method Cross-national survey of 4 countries.
result Public views vary significantly across countries.
New framework for contesting algorithmic decisions, not just explaining them.
problem Helping individuals review and correct erroneous algorithmic decisions.
method Operationalized contestability as a natural complement to explainable AI (XAI), identifying three types of evidence for reversal.
result Existing EU legislation already grants individuals legal rights to contest algorithmic decisions.
Proposes a deep learning model for probabilistic forecasting that is also interpretable.
problem Inability to explain predictions of neural network-based time series forecasting methods.
method Deep Autoregressive Networks (DANLIP) for locally interpretable probabilistic forecasting.
result DANLIP provides interpretable predictions with comparable performance to state-of-the-art methods.
Computational method identifies 30 major complaints in GEICO reviews.
problem Analyzing large numbers of online complaints is challenging.
method Topic modeling approach to reveal latent semantic of complaints.
result 30 major complaints in four categories identified from GEICO reviews.
Paper proposes transparent insurance models for PBMs.
problem PBMs' opaque business models and hidden profits.
method Quantitative estimates of two models with fixed premiums and fee-for-service.
result Proposes transparent models with fixed premiums and fee-for-service.
Classifies privacy policy segments for better user understanding.
problem Difficulty in understanding privacy policies due to legal jargon.
method Uses machine learning and deep learning techniques to classify privacy policy segments.
result Identifies data practices in privacy policies for better user comprehension.
Financial option insurance protects investors from option premiums losses.
problem Risk associated with financial option investments.
method Integrating insurance concepts with financial options, creating a three-entity framework and a mathematical model.
result Protection of option investors and minimization of insurer's risk.
Proposes a secure communication method independent of eavesdropper's decoder.
problem Lack of practical security constraints in existing methods.
method Dual MINE-based neural secure communications model.
result Security performance is not affected by eavesdropper's decoding means.
This paper systematizes knowledge on synthetic assets in crypto.
problem Disparate academic literature on synthetic assets in crypto.
method Broad perspective, general framework, data-driven analyses.
result Highlights risks and areas of research interest in synthetic assets.
This research improves debt collection strategies using advanced machine learning.
problem Accurate estimation of propensity to pay and cashflow for optimal debt collection.
method Developed a machine learning framework with pre-processing and model selection.
result The proposed model outperforms current industry strategies.
This study categorizes RWA tokenization challenges and solutions.
problem Navigating the gap between on-chain deterministic code and off-chain probabilistic reality.
method Taxonomy and comparative analysis of RWA protocols, legal and technical standards.
result RWA tokenization requires overcoming legal and technical interoperability issues.
We introduce a topological combinatorial game called the Link Smoothing Game. The game is played on the shadow of a link diagram and legal moves consist of smoothing precrossings. One player's goal is to keep the diagram connected while the other player's goal is to disconnect the shadow. We make significant progress t…
Paper resolves apparent conflict between group and individual fairness.
problem Apparent conflict between group and individual fairness in machine learning.
method Theoretical discussions from fair machine learning, political, and legal philosophy.
result Individual and group fairness are not fundamentally in conflict.