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.
The paper shows how uncertainty quantification improves counterfactual explainability in AI.
AI helps in drug discovery with understandable explanations.
This paper reviews methods to improve AI explainability in finance.
Artificial Intelligence (AI) has become an integral part of domains such as security, finance, healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human terms is a key challenge--due to the high complexity of the model, as well as the potential implications on human interests, rights, …
The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.
Systematic review of ML explainability in process mining.
Defines explainability as reasoning under background knowledge.
A new score function improves explainability and reliability of AI systems.
A new explainable CBR system predicts financial risks with interpretability and good performance.
With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching or even exceeding the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment ana…
We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such me…
Framework predicts mortality risk in MAFLD subjects.
AI learns market manipulation through simulation, suggesting regulation.
Study evaluates saliency maps on artificial data with different backgrounds.
This paper catalogs R packages for explaining AI models.
This paper analyzes the variability of Concept Activation Vectors (CAVs).
AutoML enhances credit decisions with XAI for better transparency.
We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute cri…
Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of…
Two XAI methods, SHAP and LIME, are discussed for tabular data models.
Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.
There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, p…
This paper evaluates and improves metrics for identifying important features in machine learning models.
Survey reviews explainability in AI for healthcare, emphasizing trust and transparency.
This review explores XAI methods and multicollinearity issues.
Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *…
Paper compares AI models for credit scoring and explains them.
AEC method improves XAI for models with collinear features.
Big data, data science, deep learning, artificial intelligence are the key words of intense hype related with a job market in full evolution, that impose to adapt the contents of our university professional trainings. Which artificial intelligence is mostly concerned by the job offers? Which methodologies and technolog…
Survey on AI math foundations, focusing on neural networks.
This paper uses decolonial theory to improve AI's ethical development.
Deep-learning CNN automates Cu alloy grain size evaluation.
Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard …
This report aims to improve trust in AI by explaining machine learning models.
Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type of data (images, text and tabular), are model-agnostic and are post-hoc (i.e., can be retrofitted). The Local Interpretable Model-agnostic …
CEILS generates feasible counterfactual explanations by considering causal impacts.
Researchers use DL and XAI to evaluate climate downscaling models.
survex explains machine learning survival models, improving model transparency.
New method provides calibrated feature importance explanations for regression models.
According to a mainstream position in contemporary cognitive science and philosophy, the use of abstract compositional concepts is both a necessary and a sufficient condition for the presence of genuine thought. In this article, we show how the ability to develop and utilise abstract conceptual structures can be achiev…
This review explores XAI in finance, highlighting common techniques and areas needing improvement.
Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits of ML models. Explainable ML (i.e. explainable artificial intelligence or XAI) has been implemented in numerous open source and commercial packages and explainable ML i…
This work develops a high precision fault diagnosis classifier using XAI insights.
Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. That is, RL agents can learn, as we learn. Given the importance of learning in our intelligence, RL has been thought to be one of key compone…
Explainable Artificial Intelligence (XAI)has received a great deal of attention recently. Explainability is being presented as a remedy for the distrust of complex and opaque models. Model agnostic methods such as LIME, SHAP, or Break Down promise instance-level interpretability for any complex machine learning model. …
The theory of rational choice assumes that when people make decisions they do so in order to maximize their utility. In order to achieve this goal they ought to use all the information available and consider all the choices available to choose an optimal choice. This paper investigates what happens when decisions are m…
Study uses AI techniques to predict bank customer solvency.
Study on AI-driven modeling for high burnup accident-tolerant fuels in SMRs.