Our previous results are extended to the case of the margin account, which may depend on the contract's value for the hedger and/or the counterparty. The present work generalizes also the papers by Bergman (1995), Mercurio (2013) and Piterbarg (2010). Using the comparison theorems for BSDEs, we derive inequalities for …
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arXiv research
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.
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48 results for “Fair Value Accounting”
Proposes measuring fairness through multiple stakeholder-curated stress tests.
problem Limited power of rigid fairness metrics and lack of stakeholder involvement in fairness discussions.
method Shift focus from fairness metrics to stress tests curated by stakeholders.
result Machine's performance under multiple stress tests reflects fairness.
This paper presents a coherent approach to FVA and CVA pricing of derivatives, based on recent accounting developments.
problem Derivative pricing and risk adjustment issues, especially FVA and CVA.
method A new pricing model based on fair value accounting and ISDA standards, focusing on the liability side of derivatives.
result A coherent method for determining FVA and CVA, resolving several debate issues and allowing reuse of existing infrastructure.
Two new methods assess feature importance for fairness in machine learning models.
problem Understanding how features influence fairness in machine learning models.
method Two model-agnostic approaches: permutation and occlusion.
result Simple, scalable, and interpretable methods to quantify feature importance for fairness.
New algorithms ensure fairness in sequential decisions, accounting for feedback effects.
problem Ignoring feedback effects can lead to unfair outcomes in sequential decision-making.
method Model feedback effects as MDPs and propose fair properties and algorithms.
result Demonstrated the necessity of considering dynamical effects for fairness.
A Python tool assesses fairness, accountability, and transparency in AI decisions.
problem Lack of regulation and certification for AI-driven decisions.
method Developed an open-source Python toolbox to analyze fairness, accountability, and transparency aspects of machine learning.
result Automatically reports fairness, accountability, and transparency aspects of AI decisions to stakeholders.
The paper addresses monotonicity in machine learning models for fairness and accountability.
problem Ensuring fairness and accountability in transparent machine learning models.
method Study of three types of monotonicity (individual, weak pairwise, strong pairwise) and propose monotonic groves of neural additive models.
result Monotonic groves of neural additive models maintain transparency, accountability, and fairness.
This paper examines how taxation and stochastic interest rates affect GMWB Variable Annuities.
problem Improving the financial cost and withdrawal dynamics of GMWB Variable Annuities.
method Developed a numerical framework to compute fair value of GMWB contracts, accounting for taxation and stochastic interest rates.
result Accounting for both taxation and stochastic interest rate significantly impacts GMWB withdrawal strategy and cost.
New algorithm reduces discrimination in predictions.
problem Tackles potential discrimination in AI predictions.
method Integrates fairness adjustments into tree-building process.
result Reduces discriminatory predictions without significant loss in accuracy.
Introduces lookahead counterfactual fairness to account for downstream effects of ML predictions.
problem Downstream effects of ML predictions on individuals not considered by counterfactual fairness.
method Introduces lookahead counterfactual fairness (LCF), a new fairness notion that considers future status. Proposes an algorithm based on theoretical conditions.
result Proposes an algorithm to achieve lookahead counterfactual fairness and validates it on synthetic and real data.
Paper addresses fairness issues in error-prone outcomes.
problem Fairness in error-prone outcomes.
method Combining fair ML methods and measurement models.
result Using a latent variable model removes detected unfairness.
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
problem Algorithmic systems can unfairly impact marginalized groups, especially when considering only individual-level bias.
method Formalizes multi-level fairness using causal inference tools, addressing effects of sensitive attributes at multiple levels.
result Illustrates the importance of accounting for macro-level sensitive attributes in fairness assessments.
Extends micro-price concept to RFQ markets for fair pricing.
problem Valuing securities in illiquid RFQ markets.
method Bidimensional Markov-modulated Poisson processes for liquidity.
result Introduces Fair Transfer Price for fair securities valuation.
FAHT improves fairness in online decision-making systems.
problem Discrimination in decision support systems due to biased data.
method FAHT is an adaptive fairness-aware decision tree classifier based on Hoeffding Tree algorithm.
result FAHT maintains fairness while maintaining moderate predictive performance in streaming environments.
Introduces principal fairness for fair decision-making.
problem Discrimination among similarly affected individuals.
method Uses principal stratification from causal inference.
result Explicitly accounts for decision impacts, not just protected attributes.
Proposes a method to create fair ITRs that balance value and fairness.
problem Fairness issues in ITRs that can lead to unfair advantages or disadvantages.
method Optimal transport theory to transform optimal ITRs into fair ITRs.
result Established a theoretical upper bound on value loss for improved trade-off ITRs.
Advocates focusing on utility functions to avoid unfair outcomes.
problem Unfair outcomes from fairness criteria in optimizing policies.
method Defines value of information fairness and proposes modifying utility functions.
result Value of information fairness leads to better answers than existing fairness notions.
Extends individual fairness to online decision-making, ensuring fair treatment over time.
problem Ensuring fair treatment of individuals in online decision-making.
method Introduces fairness-across-time (FT) and fairness-in-hindsight (FH) definitions, and designs a new algorithm (CaFE) to achieve sub-linear regret guarantees.
result FH can be embedded as a primary safeguard against unfair discrimination without hindering long-term decision-making.
New algorithms handle missing data to improve fairness in machine learning.
problem Missing values in data can lead to unfair outcomes in machine learning models.
method Developed scalable and adaptive algorithms to handle missing values while preserving predictive information.
result Our adaptive algorithms consistently achieve higher fairness and accuracy than standard impute-then-classify methods.
Proposes a method to enforce fairness in machine learning models without sensitive data.
problem Bias in machine learning models from historical data.
method Infers sensitive attributes from auxiliary features and integrates fairness constraints into model training.
result Mitigates bias while preserving predictive accuracy.
This work surveys algorithmic recourse, aiming to clarify definitions and solutions.
problem Providing explanations and recommendations to individuals affected by automated decisions.
method Literature review and unified definitions, formulations, and solutions.
result Unified definitions and solutions for algorithmic recourse.
Framework improves fairness in machine learning models using adversarial techniques.
problem Unfair decision processes in machine learning models.
method Two-step framework involving fairness optimization and evasion attacks.
result Framework significantly improves fairness metrics on tested datasets.
This paper examines AI and ML bias and fairness issues.
problem Bias and unfairness in AI and ML algorithms.
method Overview of bias and fairness issues, types and sources of data bias, algorithmic unfairness, fairness metrics, and de-biasing techniques.
result Discussion of the limitations of fairness metrics and de-biasing techniques.
This work proposes a new pre-processing method for supervised learning to improve fairness without sacrificing utility.
problem Improving fairness in supervised learning without compromising model performance.
method Task-tailored pre-processing approach that balances fairness and utility.
result The proposed method preserves consistent trade-offs among multiple downstream models and improves fairness in computer vision tasks.
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.
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.
FairTrade uses variational inference to create fair predictions in causal models.
problem Creating fair predictions in machine learning models with causal reasoning.
method FairTrade uses variational inference to account for unobserved confounders and integrates fairness constraints on causal paths.
result Demonstrates the effectiveness of FairTrade in creating fair predictions in both simulated and real-world datasets.
Fairness issues in AI systems impact many users, prompting new design considerations.
problem Fairness in AI systems that impact many users or stakeholders.
method Advocating for the use of social welfare functions that encode fairness in reinforcement learning.
result The need for fairness considerations in reinforcement learning systems.
The paper clusters group fairness metrics and visualizes them using PCA.
problem Measuring discrimination against socio-demographic groups using multiple metrics.
method Empirical observation and PCA visualization of fairness metrics.
result Group fairness metrics cluster into two or three main clusters.
Proposes methods to make data streams fair without fixing a model.
problem Fairness of data-driven models in evolving data streams.
method Modifies input data to ensure fair outcomes for any classifier.
result Improves predictive performance and low discrimination scores over time.
The paper addresses fairness in dynamic pricing for strategic buyers.
problem Price disparities among specific groups can lead to unfair perceptions and legal violations.
method Proposes a dynamic pricing policy that achieves fairness and discourages strategic behavior.
result Achieves an upper bound of regret over time horizons, reducing regret by 35.06% compared to a benchmark policy.
A new decision tree method tackles fairness in datasets with missing values.
problem Fairness concerns in machine learning models trained on data with missing values.
method An integrated approach based on decision trees that incorporates missing values directly and optimizes a fairness-regularized objective function.
result Our method outperforms existing fairness intervention methods applied to imputed datasets.
The paper tackles fair set-valued classification under demographic parity constraints.
problem Set-valued classification can amplify discriminatory bias, especially in multiclass settings.
method Proposes two strategies: an oracle-based method and a proxy method, both aiming to satisfy demographic parity and expected size constraints.
result Established distribution-free convergence rates and excess-risk bounds for both methods.
New framework for fairness in machine learning models using SHAP values and adversarial learning.
problem Fairness of model predictions, especially for unprivileged groups.
method Develops a new fairness definition and a framework using SHAP values and adversarial learning to mitigate bias.
result Models produced are fairer and performant, demonstrating the approach on various datasets.
New algorithm tackles fairness in multi-armed bandit problem.
problem Fairness in stochastic multi-armed bandit problem.
method Characterized a class of Fair-SMAB algorithms with two parameters.
result Achieves O(log(T)) r-Regret with UCB1 learning algorithm.
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systemscs.LG
The paper explores fairness in multi-component recommender systems.
problem How to ensure fairness in recommender systems composed of multiple models.
method Study of fairness ranking metrics, theoretical analysis, and empirical evaluation.
result Fairness in recommendation systems can be achieved by improving individual components.
New algorithm tackles fairness in multi-armed bandit problem.
problem Fairness constraints in multi-armed bandit.
method Characterized Fair-SMAB algorithms with fairness tolerance and learning algorithm parameters.
result Achieves -Regret with UCB1 learning algorithm.
FedFaiREE addresses fairness in decentralized learning with small samples.
problem Ensuring fairness in decentralized federated learning with limited data.
method FedFaiREE is a post-processing algorithm for distribution-free fair learning in decentralized settings with small samples.
result FedFaiREE provides theoretical guarantees for both fairness and accuracy in decentralized environments.
Algorithm samples fair rankings to ensure individual fairness while maintaining group fairness.
problem Fair ranking tasks with group fairness constraints and uncertainty in item utilities.
method Efficient algorithm that samples rankings from an individually-fair distribution ensuring group fairness.
result Expected utility of output ranking is at least α times optimal fair solution, where α depends on utilities and constraints.
New model considers unfairness complaints to ensure multiple fairness criteria.
problem Ensuring fairness in systems that may conflict with each other.
method Data-driven model guided by unfairness complaints, supports multiple fairness criteria, and considers their incompatibilities. Stochastic and adversarial settings analyzed with efficient algorithms.
result Efficient algorithms for both stochastic and adversarial settings with competitive guarantees.
Secure methods learn fair models without revealing sensitive attributes.
problem Training fair machine learning models without exposing sensitive data.
method Secure multi-party computation to encrypt sensitive attributes.
result Outcome-based fair models can be learned, checked, or verified without revealing sensitive attributes.
The influence of missing data mechanisms and simple missing data handling techniques on fairnessstat.ML
Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.
problem Impact of missing data mechanisms and simple imputation methods on fairness of machine learning algorithms.
method Three popular datasets for classification fairness were used. Missing values were generated using three missing data mechanisms. Various missing data handling techniques (listwise deletion, mean imputation, mode imputation, multiple imputation) were applied to the datasets. Fairness was assessed using classification algorithms (random forests).
result Missing data mechanism does not significantly impact fairness; listwise deletion gives highest fairness on average.
New fairness measures account for prediction uncertainties to detect bias.
problem Fairness of ML models is not well-defined and measures are limited.
method Introduce new fairness measures based on aleatoric and epistemic uncertainties.
result Uncertainty-based measures reveal bias not captured by existing measures.
Paper resolves apparent conflict between group and individual fairness.
problem Apparent conflict between group and individual fairness in machine learning.
method Theoretical discussions from fair machine learning, political, and legal philosophy.
result Individual and group fairness are not fundamentally in conflict.
Linear optimizers can handle group fairness objectives efficiently.
problem Balancing performance or loss with fairness across groups.
method Polynomial-time reduction using linear optimizers for arbitrary objectives.
result Optimizing arbitrary group fairness objectives is as computationally tractable as optimizing average performance.
Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-makingstat.ML
DFL framework improves action and outcome fairness in policy learning.
problem Fairness in policy learning, especially action and outcome fairness.
method Integrates action and outcome fairness into a multi-objective optimization problem using a lexicographic weighted Tchebyshev method.
result DFL framework improves both action and outcome fairness with minimal value reduction.
Paper examines fairness of data augmentation methods, finding vanilla Mixup outperforms Fair Mixup.
problem Improving fairness in classification models with limited minority data.
method Uses multicalibration to rigorously evaluate and improve data augmentation methods for classification fairness.
result Vanilla Mixup outperforms Fair Mixup and baseline methods in fairness and accuracy, especially with small minority groups.
Counterfactual Fairnessstat.ML
The paper develops a framework for fair machine learning predictions.
problem Fairness in machine learning models for legal and ethical decisions.
method Uses causal inference to model fairness, defining counterfactual fairness.
result Demonstrates the framework on predicting success in law school.