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.
AI bias arises from human-defined goals, not algorithmic flaws.
Framework for responsible LLM deployment with human involvement and decentralized technologies.
As machine learning systems move from computer-science laboratories into the open world, their accountability becomes a high priority problem. Accountability requires deep understanding of system behavior and its failures. Current evaluation methods such as single-score error metrics and confusion matrices provide aggr…
Survey on making machine learning models more understandable.
The Availability bias, manifested in the over-representation of extreme eventualities in decision-making, is a well-known cognitive bias, and is generally taken as evidence of human irrationality. In this work, we present the first rational, metacognitive account of the Availability bias, formally articulated at Marr's…
New approach to counterfactual reasoning in AI and psychology.
Proposes HEX for human-in-the-loop explainability in ML models.
Study causal effects on humans in mixed human-AI systems with unobserved unit types.
Paper examines LLM capability benchmarks through construct validity, favoring nomological account.
Human irrationality can improve AI design, study shows.
RLHF fails when humans only partially observe, leading to inflated or overjustified feedback.
Paper tackles unobserved confounding in human-AI collaborations.
Nowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregation of those entries ultimately defines an organization's financial statement. To detect potential misstatements and fraud, international au…
The paper tackles AI advice giving by considering adherence levels and defer options.
It is inconceivable how chaotic the world would look to humans, faced with innumerable decisions a day to be made under uncertainty, had they been lacking the capacity to distinguish the relevant from the irrelevant---a capacity which computationally amounts to handling probabilistic independence relations. The highly …
Study assesses consistency and reproducibility of LLMs in finance and accounting tasks.
New method accounts for hidden context in preference learning for RLHF models.
Framework enhances AI explainability by aligning with human cognitive models.
Trust-aware MAB improves learning performance by accounting for human deviation.
Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predic…
Decoding strategies often exclude human-like tokens, creating a detectable gap in generated text.
Method tackles uncertainty in reward models for LLMs from heterogeneous human feedback.
Computer generated academic papers have been used to expose a lack of thorough human review at several computer science conferences. We assess the problem of classifying such documents. After identifying and evaluating several quantifiable features of academic papers, we apply methods from machine learning to build a b…
Human categorization is one of the most important and successful targets of cognitive modeling in psychology, yet decades of development and assessment of competing models have been contingent on small sets of simple, artificial experimental stimuli. Here we extend this modeling paradigm to the domain of natural images…
Predictive Q-learning algorithm for IoT networks with human operators.
Robots need models of human behavior for both inferring human goals and preferences, and predicting what people will do. A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward. Whi…
New simulation model predicts financial market dynamics with high accuracy.
A human-in-the-loop ML framework for precision dosing reduces expert workload and removes bias.
The Lee Carter modelling framework is widely used because of its simplicity and robustness despite its inability to model specific cohort effects. A large number of extensions have been proposed that model cohort effects but there is no consensus. It is difficult to simultaneously account for cohort effects and age-adj…
We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular approach consists of using a Conditional Variational Autoencoder (CVAE). However, existing approaches that do so either fail to capture the …
Study examines the impact of employment benefit costs on firm profitability.
Corrects bias in LLM-as-a-judge evaluations using adaptive calibration.
New method infers human sensorimotor costs from behavior.
Develops a test to assess if human experts add value to predictions.
Predicting potential credit default accounts in advance is challenging. Traditional statistical techniques typically cannot handle large amounts of data and the dynamic nature of fraud and humans. To tackle this problem, recent research has focused on artificial and computational intelligence based approaches. In this …
Interpole learns transparent decision-making policies from data.
Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This pape…
The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this paper, we propose a deep neural architecture trained by using expe…
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the varia…
New method pools labels from similar data items to improve learning from small samples.
Develops a new model for RLHF accounting for partially observed states and intermediate feedback.
Introduces lookahead counterfactual fairness to account for downstream effects of ML predictions.
Develops new methods to create imperceptible image changes that fool classifiers.
The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an e…
The ubiquity of systems using artificial intelligence or "AI" has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems will require care. AI systems have the potential to synthesize large amounts of data, allowing for greater levels of personalization and preci…
Introduces principal fairness for fair decision-making.
Human motion prediction is a stochastic process: Given an observed sequence of poses, multiple future motions are plausible. Existing approaches to modeling this stochasticity typically combine a random noise vector with information about the previous poses. This combination, however, is done in a deterministic manner,…
In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Mo…