The study examines how individualism and collectivism affect economic outcomes.
problem Understanding how societal norms impact economic performance and inequality.
method Dynamic model analyzing the effects of individualism and collectivism on economic outcomes during the Malthusian Era.
result More individualistic societies have smaller populations, higher per capita income, and greater income inequality.
New framework controls statistical dispersion for high-stakes applications.
problem Understanding and controlling the dispersion of loss distributions in high-stakes applications.
method Simple yet flexible framework for distribution-free control of statistical dispersion measures.
result Proposed methods control statistical dispersion measures with societal implications.
PCL framework optimizes climate risk management across three clusters.
problem Comprehensive risk management in response to climate change impacts.
method Optimization of preemptive adaptation, contingent arrangements, and loss acceptance.
result Balanced portfolio of actions across three clusters optimized for long-term aggregate outlay.
New fair regression method improves fairness in chronic kidney disease classification.
problem Mitigating societal bias in health care for multiple groups.
method Penalized fair regression framework for multiple groups, with penalties for true positive rate disparity.
result Achieves fairness-accuracy frontier beyond existing methods in simulations and real-world data.
Optimizes fairness without sacrificing primary objectives.
problem Achieving fairness in optimization without reducing solution quality.
method Parametrized objective function to generate a set of optimal solutions, then optimize fairness using secondary criteria.
result Optimal solutions can be found that balance fairness and primary objectives.
A new algorithm for fair decision-making in bandit problems with biased feedback.
problem Fair allocation of resources across sensitive groups in bandit problems with biased feedback.
method Proposes a novel algorithm that learns a societal bias term to mitigate unfairness in multi-armed bandit problems.
result The proposed algorithm achieves a theoretical bound on regret and demonstrates fairness in synthetic and real-world data.
Framework generates precise synthetic populations for scalable modeling.
problem Generating accurate synthetic populations without personal data.
method Constraint-programming framework encoding aggregated statistics and structural relations.
result Exact control of demographic profiles without requiring microdata.
Study proposes an alternative method to measure societal biases using smoothed co-occurrence relations.
problem Measuring societal biases using word embeddings can introduce irrelevant concepts.
method Proposes an alternative approach using smoothed first-order co-occurrence relations.
result First-order approach shows higher correlations with actual gender bias statistics.
Introduces data ethics for mathematicians, covering background, open data, and privacy.
problem Ensuring ethical use of data in mathematical research and education.
method Overview of data ethics concepts, societal context, and resources.
result Highlights the importance of ethical considerations in data use.
Proposes a new fairness definition based on equity for machine learning classification.
problem Machine learning systems can perpetuate societal biases.
method Formalizes a new fairness definition based on equity, operationalizes it for classification, and evaluates its effectiveness.
result Demonstrates the effectiveness of the new fairness definition for equitable classification.
PEF identifies the best subgroup performance balance for fairness.
problem Fairness constraints can degrade performance in skewed datasets.
method PEF identifies the closest operating point on the Pareto curve of subgroup performances.
result PEF achieves Pareto levels in accuracy for all subgroups.
Proxy measures in competitive systems lead to corruption and inefficiency.
problem Corruption of proxy measures in competitive systems.
method Agent-based model integrating insights from complex systems, contest theory, behavioral economics, and cultural evolution.
result Proxy measures lead to corruption and convergence towards an equilibrium level of corruption.
Improves fairness in machine learning by adding underrepresented group data.
problem Machine learning biases across subgroups due to under-representation or societal biases.
method Data augmentation via pairwise mixup across subgroups to balance subpopulations.
result Achieves fair outcomes with robust if not improved accuracy.
Proposes CSRN for better news recommendation by integrating RNN and UserCF.
problem Lack of societal influence in news recommendation.
method Integrates RNN-based sequential recommendation and UserCF, using a directed co-reading network.
result Significantly outperforms state-of-the-art approaches in news recommendation.
New fairness criteria for algorithmic recourse actions that consider causal relationships.
problem Fairness of recourse actions in algorithmic classification.
method Proposes two new fairness criteria at group and individual levels, explicitly accounting for causal relationships.
result Fairness of recourse is complementary to fairness of prediction, and can be enforced by altering the classifier.
New bounds show multicalibration error is close to prediction error.
problem Addressing fairness in machine learning systems.
method Sample complexity bounds for uniform convergence of multicalibration error.
result Uniform convergence guarantees for multicalibration error, independent of prediction error.
A novel approach learns constraints and maximizes rewards for autonomous agents.
problem Ensuring autonomous agents align with societal norms and values.
method Inverse reinforcement learning for constraints, contextual bandit orchestrator for policy mixing.
result Agent learns to act optimally within constraints and maximize rewards.
Study finds telemetric data not effective for predicting truck accident risk.
problem Estimating the risk of truck accidents using telemetric data.
method Used machine learning approaches: Random Forests and Convolutional Neural Networks.
result Neither approach successfully predicted truck accident risk.
New method detects long-term structures with internal dynamics in time series data.
problem Identifying patterns across time in data growth.
method Adaptive identification of majority overlaps between groups at different time points.
result Detection of persistent structural elements with internal dynamics.
New method to understand bias in word embeddings.
problem Understanding and mitigating bias in word embeddings.
method Developed a technique to trace bias origins back to training documents.
result Accurate approximations of bias reduction can be made.
Demonstrates ABCE's fairness analysis in complex systems.
problem Complex stochastic systems and subjective value criteria.
method Agent-based computational economics.
result Shows ABCE's capability for fairness analysis.
TerraNova models Earth and societies as a unified system.
problem Modeling the physical Earth and human societies as a coupled system.
method TerraNova integrates physical Earth fields and societal indicators in their native geometries using encoders, cross-modal transformers, and a hypernetwork.
result TerraNova represents the physical Earth and societies without lossy averaging over borders, achieving competitive performance and spanning axes not represented by purpose-built encoders.
Adversarial attacks can manipulate ML-aided visualizations, tricking analysts.
problem Adversarial attacks on ML-aided visualizations.
method Identifying attack surface and exemplifying five adversarial attacks.
result Adversaries can induce various attacks, like creating arbitrary and deceptive visualizations.
This paper defines less discriminatory algorithms and explores their feasibility.
problem Creating algorithms that are less discriminatory while meeting business needs.
method Formal definition of less discriminatory algorithms, evaluation of feasibility, and search for alternatives.
result Formal definitions of less discriminatory algorithms face challenges due to lack of held-out data, necessitating a reliance on reasonableness standards.
The paper analyzes frameworks for integrating sustainability into investment decisions.
problem Understanding how ESG factors influence investment choices.
method Examined and analyzed various theoretical frameworks including Behavioral Finance, Modern Portfolio, and Risk Management.
result Investors increasingly integrate ESG factors to optimize financial outcomes and societal goals.
New fairness criterion aims to reduce financial harm to certain groups.
problem Financial decisions can disproportionately harm certain groups.
method Introduces 'equalized financial impact' to address fairness in financial decisions.
result Reduces financial harm to certain groups in financial decisions.
This thesis tackles bias in AI decision-making in banking.
problem Bias in AI-driven banking decisions.
method Understanding, mitigating, and accounting for bias in AI systems.
result Establishment of Responsible AI practices for fair decision-making.
TUV Austria proposes certification for ML applications to ensure reliability.
problem Ensuring trust in AI applications to meet societal reliance requirements.
method Holistic approach analyzing security, functionality, data quality, ethics, and criticality levels.
result Certification process for low-risk ML applications in supervised learning.
Study aims to measure and mitigate biases in motor insurance pricing.
problem Ethical biases in motor insurance pricing that affect fairness and regulatory compliance.
method Statistical methodologies and data analysis to measure and mitigate biases.
result Developed tools to measure and mitigate ethical biases in motor insurance pricing.
Bayesian model forecasts hospital resource use during pandemic.
problem Modeling constrained hospital resources during pandemic.
method Approximate Bayesian Computation for an ACED-HMM.
result Mechanistic approach provides competitive probabilistic forecasts.
Reduces gender classification bias by learning race-invariant face representations.
problem Societal bias in gender recognition systems.
method Adversarially trained autoencoder model to learn race-invariant face representations.
result Achieved a significant drop of over 40% in racial bias surrogate metric with race invariant representations.
Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.
problem Balancing public benefits and private insurance for loss of earning capacity in Denmark.
method Innovative approaches from researchers and practitioners, leveraging actuarial expertise.
result Development of equitable, data-driven solutions to mitigate risk and enhance societal well-being.
AI boosts study of rare weather extremes with lower costs.
problem Difficulty in studying rare weather events due to limited data and models.
method Coupling AI forecasts with physics models using rare-event algorithms.
result Efficiently characterizes very rare events like once-per-millennium heatwaves.
Fair active learning selects data points to balance model accuracy and fairness.
problem Ensuring fairness in machine learning models used in high-stakes applications.
method Designing algorithms for fair active learning that select data points to balance model accuracy and fairness.
result Demonstrated the effectiveness and efficiency of fair active learning algorithms over benchmark datasets.
Fair active learning selects data points to balance model accuracy and fairness.
problem Ensuring fairness in machine learning models used in high-stakes applications.
method Designing algorithms for fair active learning that select data points to balance model accuracy and fairness, focusing on demographic parity.
result Demonstrated the effectiveness of the proposed fair active learning approach over benchmark datasets.
A textbook on machine learning explaining patterns, predictions, and actions.
problem Understanding patterns in data for predictions and actions.
method Explains foundations of decision making, representation, optimization, and generalization.
result Equips readers with tools to reason about actions and their consequences.
Mitigates gender bias amplification in model predictions.
problem Gender bias amplification in model predictions.
method Posterior regularization to mitigate bias.
result Almost removes gender bias amplification in model predictions.
This study conducts a comprehensive analysis of time series segmentation on the Japanese stock prices listed on the first section of the Tokyo Stock Exchange during the period from 4 January 2000 to 30 January 2012. A recursive segmentation procedure is used under the assumption of a Gaussian mixture. The daily number …
New norms derived from box-norm improve multitask learning performance.
problem Improving multitask learning performance in matrix completion and prediction.
method Derived new norms (box-norm, spectral k-support, spectral box-norm) and improved algorithms to compute them.
result New norms provide state-of-the-art performance in matrix completion and multitask learning.
Abstract compares two norms in holomorphic quadratic differentials.
problem Comparing two norms in holomorphic quadratic differentials.
method Comparison between Avila-Gouëzel-Yoccoz norm and Teichmüller norm.
result Comparison of two norms in holomorphic quadratic differentials.
We introduce a new family of matrix norms, the "local max" norms, generalizing existing methods such as the max norm, the trace norm (nuclear norm), and the weighted or smoothed weighted trace norms, which have been extensively used in the literature as regularizers for matrix reconstruction problems. We show that this…
We introduce twisted Alexander norms of a compact connected orientable 3-manifold with first Betti number bigger than one generalizing norms of McMullen and Turaev. We show that twisted Alexander norms give lower bounds on the Thurston norm of a 3-manifold. Using these we completely determine the Thurston norm of many …
The study examines how social biases are reinforced in machine learning models used for credit scoring.
problem Reinforcement of societal biases in machine learning algorithms for credit scoring.
method Analysis of machine learning models predicting gender or ethnicity based on loan applications data.
result Machine learning models can reflect and reinforce social biases present in the data.
Study relates Gromov norm to harmonic norm on non-positively curved manifolds.
problem Relating norms on homology classes to cohomology.
method Relates Gromov norm to harmonic norm, using volume and geometric quantities.
result Obtains double-sided bounds on norms.
New exotic semi-norms on homology discovered.
problem Understanding refined size information on homology classes.
method Developed finite functorial semi-norms on singular homology.
result Exotic semi-norms not carried by the l^1-semi-norm.
The paper analyzes feedback loops in recommender systems causing echo chambers and filter bubbles.
problem Feedback loops in recommender systems leading to echo chambers and filter bubbles.
method Theoretical analysis of user dynamics and recommender system behavior.
result Solutions to slow down system degeneracy and understanding echo chambers and filter bubbles.
New L0 norm added to TDA for market analysis.
problem Improving TDA tools for market prediction.
method Defined and applied L0 norm in TDA for four markets.
result Enhanced TDA tools for market analysis.
The k-support norm is a regularizer which has been successfully applied to sparse vector prediction problems. We show that it belongs to a general class of norms which can be formulated as a parameterized infimum over quadratics. We further extend the k-support norm to matrices, and we observe that it is a special …