Legal requirements on explainability in machine learning

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Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation

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Explaining Explanations in AI

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A Survey of Methods for Explaining Black Box Models

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Meaningful information and the right to explanation

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Accountability of AI Under the Law: The Role of Explanation

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The Intuitive Appeal of Explainable Machines

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Legal Judgment Prediction via Topological Learning

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Learning to Predict Charges for Criminal Cases with Legal Basis

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Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation

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Automatically classifying case texts and predicting outcomes

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Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions

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Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”?

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All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class ReliancePreprint

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Data-centric and logic-based models for automated legal problem solving

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Enslaving the Algorithm: From a 'Right to an Explanation' to a 'Right to Better Decisions'?

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Research and Design on Cognitive Computing Framework for Predicting Judicial Decisions

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Guest Editor's Introduction

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Supersparse Linear Integer Models for Predictive Scoring SystemsPreprint

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La motivation des décisions administratives en droit comparé

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Regards sur le droit au procès équitable

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Interpretable machine learning: Fundamental principles and 10 grand challenges

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Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations

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Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-Making

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Choice modelling in the age of machine learning - Discussion paper

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Deep learning in insurance: Accuracy and model interpretability using TabNet

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Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

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A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

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The black box problem revisited. Real and imaginary challenges for automated legal decision making

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Explainable AI for Credit Assessment in Banks

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Explainable AI and Law: An Evidential Survey

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Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency

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An Explainable AI framework for credit evaluation and analysis

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Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

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Varieties of AI Explanations Under the Law. From the GDPR to the AIA, and Beyond

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Challenges of responsible AI in practice: scoping review and recommended actions

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The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations

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The GDPR enforcement fines at glance

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Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

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Building Trustworthy AI Solutions: A Case for Practical Solutions for Small Businesses

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Non-functional requirements for machine learning: understanding current use and challenges among practitioners

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The Conflict Between Explainable and Accountable Decision-Making Algorithms

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Bringing order into the realm of Transformer-based language models for artificial intelligence and law

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The Explainability of Transformers: Current Status and Directions

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Exploring the Intersection of Machine Learning and Big Data: A Survey

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Pairing conceptual modeling with machine learning

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Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

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An objective metric for Explainable AI: How and why to estimate the degree of explainability

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From Public E-Procurement 3.0 to E-Procurement 4.0; A Critical Literature Review

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Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions

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Addressing Data Challenges to Drive the Transformation of Smart Cities

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The landscape of facial processing applications in the context of the European AI Act and the development of trustworthy systems

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Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data

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Towards eXplainable Artificial Intelligence (XAI) in Tax Law: The Need for a Minimum Legal Standard

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Blaming Humans and Machines: What Shapes People’s Reactions to Algorithmic Harm

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Patient similarity analytics for explainable clinical risk prediction

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Ethics and AI Issues: Old Container with New Wine?

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Questioning the ability of feature-based explanations to empower non-experts in robo-advised financial decision-making

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Fairness and explainability in automatic decision-making systems. A challenge for computer science and law

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Public tenders, complaints, machine learning and recommender systems: a case study in public administration

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Exploring explainable AI in the tax domain

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Do public comments make a difference in open rulemaking? Insights from information management using machine learning and QCA analysis

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Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective

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Towards Grad-CAM Based Explainability in a Legal Text Processing Pipeline. Extended Version

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Software engineering for Responsible AI: An empirical study and operationalised patterns

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Thirty years of Artificial Intelligence and Law: overviews

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An extension of iStar for Machine Learning requirements by following the PRISE methodology

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Understanding the Decision of Machine Learning Based Intrusion Detection Systems

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Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces

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Boosting court judgment prediction and explanation using legal entities

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Manifestations of xenophobia in AI systems

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AI, Law and beyond. A transdisciplinary ecosystem for the future of AI & Law

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Data-Driven Criminal Justice in the age of algorithms: epistemic challenges and practical implications

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Transparency and Explainability for Public Policy

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Model- and data-agnostic justifications with A Fortiori Case-Based Argumentation

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Q-SENN: Quantized Self-Explaining Neural Networks

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Application and challenges of machine learning in microbial remediation: A review of current status and future directions

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The Tower of Babel in Explainable Artificial Intelligence (XAI)

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ARTIFICIAL INTELLIGENCE AND ITS ROLE IN THE DEVELOPMENT OF THE FUTURE OF ARBITRATION

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Explainable Legal Case Matching via Graph Optimal Transport

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Balancing fraud analytics with legal requirements: Governance practices and trade-offs in public administrations

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Explaining Explainable Artificial Intelligence: An integrative model of objective and subjective influences on XAI

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CAILIE 1.0: A dataset for Challenge of AI in Law - Information Extraction V1.0

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Artificial intelligence systems and the right to good administration

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TimeREISE: Time Series Randomized Evolving Input Sample Explanation

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A Decade of Legal Argumentation Mining: Datasets and Approaches

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Efficient compliance checking of RDF data

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Improving Requirements Classification Models Based on Explainable Requirements Concerns

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AI is Entering Regulated Territory: Understanding the Supervisors' Perspective for Model Justifiability in Financial Crime Detection

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The AI Act in a law enforcement context: The case of automatic speech recognition for transcribing investigative interviews

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The disagreement dilemma in explainable AI: can bias reduction bridge the gap

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A Comparison of Classification Methods Applied to Legal Text Data

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Enabling affordances for AI Governance

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From outputs to insights: a survey of rationalization approaches for explainable text classification

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Innovation in intensive care: a framework to turn ideas and concepts into actionable solutions

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Joining metadata and textual features to advise administrative courts decisions: a cascading classifier approach

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On the Explainability of Financial Robo-Advice Systems

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Brain Tumor Detection Using Selective Search and Pulse-Coupled Neural Network Feature Extraction

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A spatial dependency based reinforcement learning model for selecting features in spatial classification

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The need for a numeric measure of explainability

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Towards Grad-CAM Based Explainability in a Legal Text Processing PipelinePreprint

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Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industry

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Judgment Prediction Based on Tensor Decomposition With Optimized Neural Networks

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Investigating High-Level Decision Making for Automated Driving

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

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The Grand Gala of PNR Litigations: Case C-817/19, Ligue des droits humains v Conseil des ministers

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Explainable machine learning for hydrocarbon prospect risking

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Law and Artificial Intelligence: Possibilities and Regulations on the Road to the Consummation of the Digital Verdict

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An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms

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Machine learning and law

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Legislating for Legal Certainty, with a Right to a Human Face, in an Automated Public Administration

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Requirements on and Procurement of Explainable Algorithms—A Systematic Review of the Literature

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Artificial Intelligence and Machine Learning in Finance: Key Concepts, Applications, and Regulatory Considerations

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Requirements Elicitation Techniques and Tools in the Context of Artificial Intelligence

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PoliToHFI at SemEval-2023 Task 6: Leveraging Entity-Aware and Hierarchical Transformers For Legal Entity Recognition and Court Judgment Prediction

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Closed-form interpretation of neural network classifiers with symbolic gradients

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How should an explanation be? A mapping of technical and legal desiderata of explanations for machine learning models

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A Conceptual View on the Design and Properties of Explainable AI Systems for Legal Settings

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Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society

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Old and New Taxpayers' Rights in the Digital Age: Supranational Convergences

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More Than Justifications an Analysis of Information Needs in Explanations and Motivations to Disable Personalization

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Impact of Legal Requirements on Explainability in Machine LearningPreprint

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The coming of age of interpretable and explainable machine learning models

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How should an explanation be? A mapping of technical and legal desiderata of explanations for machine learning models

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explainy: A Toolkit for Legal-XAI

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Interpretable AI models for judicial decisionmaking: beyond explicability towards legal due process

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