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.
New framework controls statistical dispersion for high-stakes applications.
When society maintains a competitive system to promote an abstract goal, competition by necessity relies on imperfect proxy measures. For instance profit is used to measure value to consumers, patient volumes to measure hospital performance, or the Journal Impact Factor to measure scientific value. Here we note that \t…
Improves fairness in machine learning by adding underrepresented group data.
PCL framework optimizes climate risk management across three clusters.
The last decades have not only been characterized by an explosive growth of data, but also an increasing appreciation of data as a valuable resource. Their value comes with the ability to extract meaningful patterns that are of economic, societal or scientific relevance. A particular challenge is the identification of …
New fair regression method improves fairness in chronic kidney disease classification.
Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discrimination. Collecting accurate labeled data in societal applications is challenging and costly. Active learning is a promising approach to build an…
Fair active learning selects data points to balance model accuracy and fairness.
Framework generates precise synthetic populations for scalable modeling.
The potential for learned models to amplify existing societal biases has been broadly recognized. Fairness-aware classifier constraints, which apply equality metrics of performance across subgroups defined on sensitive attributes such as race and gender, seek to rectify inequity but can yield non-uniform degradation in…
Introduces data ethics for mathematicians, covering background, open data, and privacy.
Proposes a new fairness definition based on equity for machine learning classification.
This thesis tackles bias in AI decision-making in banking.
Deep Neural Networks (DNNs) are universal function approximators providing state-of- the-art solutions on wide range of applications. Common perceptual tasks such as speech recognition, image classification, and object tracking are now commonly tackled via DNNs. Some fundamental problems remain: (1) the lack of a mathe…
New fairness criteria for algorithmic recourse actions that consider causal relationships.
Proposes CSRN for better news recommendation by integrating RNN and UserCF.
Study aims to measure and mitigate biases in motor insurance pricing.
New bounds show multicalibration error is close to prediction error.
Bayesian model forecasts hospital resource use during pandemic.
Study finds telemetric data not effective for predicting truck accident risk.
Economies and societal structures in general are complex stochastic systems which may not lend themselves well to algebraic analysis. An addition of subjective value criteria to the mechanics of interacting agents will further complicate analysis. The purpose of this short study is to demonstrate capabilities of agent-…
We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting. In the CMAB setting, a sequential decision maker must, at each time step, choose an arm to pull from a finite set of arms after observing some context for each of the potential arm pulls. In our mo…
This paper explores the potential of Lagrangian duality for learning applications that feature complex constraints. Such constraints arise in many science and engineering domains, where the task amounts to learning optimization problems which must be solved repeatedly and include hard physical and operational constrain…
Text corpora are widely used resources for measuring societal biases and stereotypes. The common approach to measuring such biases using a corpus is by calculating the similarities between the embedding vector of a word (like nurse) and the vectors of the representative words of the concepts of interest (such as gender…
Proposes a framework to create fair IDRs by enforcing demographic parity constraints.
Data is one of the most important assets of the information age, and its societal impact is undisputed. Yet, rigorous methods of assessing the quality of data are lacking. In this paper, we propose a formal definition for the quality of a given dataset. We assess a dataset's quality by a quantity we call the expected d…
Notions of "fair classification" that have arisen in computer science generally revolve around equalizing certain statistics across protected groups. This approach has been criticized as ignoring societal issues, including how errors can hurt certain groups disproportionately. We pose a modification of one of the fairn…
The study examines how social biases are reinforced in machine learning models used for credit scoring.
Proposes a new framework for uncertainty evaluation in ML classification models.
Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic and political agendas. Such unprecedented interest is fuelled by a vision of ML applica…
Adversarial attacks can manipulate ML-aided visualizations, tricking analysts.
This paper defines less discriminatory algorithms and explores their feasibility.
Societal bias towards certain communities is a big problem that affects a lot of machine learning systems. This work aims at addressing the racial bias present in many modern gender recognition systems. We learn race invariant representations of human faces with an adversarially trained autoencoder model. We show that …
The paper analyzes frameworks for integrating sustainability into investment decisions.
New framework tackles fairness in link prediction beyond demographic parity.
FairLangProc simplifies fairness in NLP models for Python users.
A popular approach of achieving fairness in optimization problems is by constraining the solution space to "fair" solutions, which unfortunately typically reduces solution quality. In practice, the ultimate goal is often an aggregate of sub-goals without a unique or best way of combining them or which is otherwise only…
TUV Austria proposes certification for ML applications to ensure reliability.
Statisticians contribute to LLMs for better trust and transparency.
Extends Demographic Parity for fairer wage predictions with expert knowledge.
Proposes a method to achieve quantile fairness in predictions.
Privacy is crucial in many applications of machine learning. Legal, ethical and societal issues restrict the sharing of sensitive data making it difficult to learn from datasets that are partitioned between many parties. One important instance of such a distributed setting arises when information about each record in t…
Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.
Survey on biases in image analysis for industrial safety.
Roads are critically important infrastructure to societal and economic development, with huge investments made by governments every year. However, methods for monitoring those investments tend to be time-consuming, laborious, and expensive, placing them out of reach for many developing regions. In this work, we develop…
The study improves the assessment of fairness in face recognition using ROC curves and statistical guarantees.
The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demogra…
AI boosts study of rare weather extremes with lower costs.