The paper explores fair treatment in financial exchanges, finding unbounded fairness unrealistic and proposing ε-fairness as a solution.
problem Ensuring fair treatment of all competing participants in financial exchanges.
method Investigation of unbounded temporal fairness, analysis of real-world incidents, introduction of ε-fairness.
result Unbounded temporal fairness is unrealistic in FIFO markets, and ε-fairness provides a viable alternative.
A new model for fair IP monetization.
problem Inequitable sharing between IP creators and users.
method A mutual exchange model where users pay for use of IP, and creators receive payment when IP is no longer needed.
result Proposes a new model that aims to improve the coexistence of IP creators and users.
Libra ensures fair order-matching in electronic financial exchanges.
problem Technical shortcomings and infrastructure complexities in electronic trading.
method Formally defined temporal fairness, evaluated existing fair market designs, introduced Libra.
result Libra is more robust and resilient to technical manipulation than existing designs.
The QLBS model is enhanced with a large trader's impact, leading to optimal hedging strategies.
problem Finding an optimal hedging strategy with low transaction costs and fair price convergence.
method Extending the QLBS model, defining a hypothetical limit order book, and using batch-mode reinforcement learning.
result Optimal hedging strategy with lower transaction costs and fair price convergence.
The paper proves ADL mechanisms face a trilemma and optimizes them for fairness, revenue, and exchange solvency.
problem The impossibility of a perpetual futures exchange achieving solvency, revenue, and fairness.
method Formal model of ADL, proving trilemma, and analyzing three ADL mechanisms.
result Optimized ADL mechanisms can reduce trader losses while maintaining exchange solvency.
New techniques exploit market systems to manipulate prices.
problem Market manipulation through technical details and glitches.
method Analyzed and modeled market manipulation techniques and proposed mitigation measures.
result Technical solutions can significantly thwart market manipulation.
Formal framework verifies fairness, uniformity, and rationality in financial market trades.
problem Ensuring fairness, uniformity, and rationality in financial market trades.
method Formal definition and proof of properties in a theorem prover (Coq).
result Formal verification of double-sided auctions in financial markets.
The definition of preferences assigned to individuals is a concept that concerns many disciplines, from economics, with the search of an acceptable outcome for an ensemble of individuals, to decision making an analysis of vote systems. We are concerned in the phenomena of good selection and economic fairness. In Arrow'…
Formally verifies fairness and uniformity in financial market trades.
problem Ensuring fairness and uniformity in automated trading systems.
method Formal definition and verification in Coq proof assistant.
result Properties of double-sided auction mechanisms verified.
Automated market-making for CBDCs and stable coins on blockchain.
problem Creating fair exchange rates for digital assets on blockchain.
method Developed an innovative approach for generating fair exchange rates.
result Illustrated the approach's efficacy on G-10 currency exchange rates.
We discuss the equivalence between kinetic wealth-exchange models, in which agents exchange wealth during trades, and mechanical models of particles, exchanging energy during collisions. The universality of the underlying dynamics is shown both through a variational approach based on the minimization of the Boltzmann e…
Unified framework for combinatorial and rounding algorithms in experimental design.
problem Designing and analyzing combinatorial and rounding algorithms for experimental design problems.
method Local search framework for combinatorial algorithms and regret minimization framework for rounding algorithms.
result Unified approach to match and improve all known results in D/A/E-design and obtain new results in unknown settings.
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.
New algorithm provides robust uncertainty quantification without parameter tuning.
problem Real-world machine learning predictors need reliable uncertainty quantification.
method Parameter-free, group-conditional online prediction algorithm.
result Achieves best group-conditional coverage guarantees.
Meta clustering categorizes learners for collaborative learning.
problem Filtering out unqualified collaborators in collaborative learning.
method Select-Exchange-Cluster (SEC) method to classify learners by their supervised functions.
result SEC can cluster learners into accurate collaboration sets and enhance single-learner performance.
This paper optimizes crypto portfolios and valuates crypto options.
problem High volatility and lack of standard pricing models for crypto assets.
method Optimization techniques to minimize tail risk, dynamic pricing model for crypto assets, Esscher transform for fair valuation.
result Optimized crypto portfolios outperform major stock indices.
Adaptive Conformal Inference improves time series forecasting uncertainty.
problem Uncertainty quantification in time series models with dependency.
method AgACI, an adaptive method based on online expert aggregation.
result AgACI provides efficient prediction intervals for day-ahead electricity price forecasting.
New EPS insurance offers partial protection against superannuation losses.
problem Lack of efficient investment insurance for superannuation holders.
method Developed a new financial derivative, equity protection swap (EPS), and derived a fair pricing formula.
result EPS can be an efficient investment insurance tool for superannuation accounts.
We introduce here for the first time the long-term swap rate, characterised as the fair rate of an overnight indexed swap with infinitely many exchanges. Furthermore we analyse the relationship between the long-term swap rate, the long-term yield, see Biagini et al. [2018], Biagini and Härtel [2014], and El Karoui et a…
Two simple methods learn fair metrics from data to improve fairness in ML tasks.
problem Lack of widely accepted fair metrics for many ML tasks hinders individual fairness adoption.
method Presented two simple ways to learn fair metrics from various data types.
result Fair training with learned metrics improves fairness on three ML tasks.
Study on CFMMs pricing and hedging, developing models for LP and derivatives valuation.
problem Valuation and hedging of liquidity provider mechanisms in CFMMs.
method Developed a model with two types of traders, simulated their behavior, and calculated PnL.
result Foundations for estimating CFMM derivatives and understanding fair price distribution.
New concept of within-group fairness improves AI fairness without sacrificing accuracy.
problem Fairness issues in AI models treating individuals in the same sensitive group unfairly.
method Introducing within-group fairness, proposing mathematical definitions, and developing learning algorithms.
result Improves within-group fairness without sacrificing accuracy and between-group fairness.
This work studies fairness in systems of multiple algorithms, addressing pitfalls and constructing fair compositions.
problem Fairness of scoring and classification algorithms in systems of multiple algorithms.
method Identifying and addressing pitfalls of naive composition, constructing fair compositions for individual and group fairness.
result Fairness properties of systems of multiple fair algorithms are not necessarily preserved under composition.
A new fairness metric for decision-making algorithms, conditioning on known fair variables.
problem Fairness issues in decision-making systems.
method Conditional fairness metric, Derivable Conditional Fairness Regularizer (DCFR), adversarial representation.
result Traditional fairness notations are special cases of the new conditional fairness notation.
A new model adds stochastic spot/volatility correlation to Heston model for better exotic pricing.
problem Improving exotic option pricing in foreign exchange markets.
method Developed a Double Heston model with stochastic spot/volatility correlation, an affine model.
result The new model increases prices of out-of-the-money knockout options and one touch options.
We introduce convex fairness regularizers for regression problems.
problem Fairness in regression models, especially individual fairness.
method Flexible convex regularizers for linear and logistic regression, varying fairness weights.
result Efficient frontier of accuracy-fairness trade-off and Price of Fairness (PoF) measure.
Fair Mixup improves fairness in classifiers by interpolating between groups.
problem Ensuring fairness in classifiers during training and evaluation.
method Fair Mixup uses interpolation of samples between groups to enforce fairness constraints.
result Fair Mixup ensures better generalization of fairness in various benchmarks.
Paper proposes a modified fairness constraint to address shortcomings of counterfactual fairness.
problem Counterfactual fairness is not a necessary condition for algorithmic fairness.
method Analyzed hypothetical scenario and explicated discrimination to develop causal relevance fairness.
result Causal relevance fairness is a modified constraint that circumvents shortcomings of counterfactual fairness.
Bayesian fairness tackles fairness in uncertain probabilistic models.
problem Fairness in decision making when probabilistic models are uncertain.
method Introducing Bayesian fairness, using balance fairness definition.
result Bayesian approach leads to fair decision rules under high uncertainty.
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.
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.
The paper connects counterfactual fairness to robust prediction and group fairness using causal context.
problem The challenge of ensuring fairness in AI systems when counterfactuals cannot be directly observed.
method Using causal context to bridge counterfactual fairness, robust prediction, and group fairness.
result Counterfactual fairness is equivalent to group fairness metrics in specific contexts.
New method improves fairness in biased predictions.
problem Improving fairness in biased classifier predictions.
method Individual bias detector prioritizes data samples for a bias mitigation algorithm.
result Superior performance in individual and group fairness on real-world datasets.
New fairness notion helps identify fair auditors for evaluating decision-support systems.
problem Identifying fair auditors to evaluate decision-support systems for bias.
method Introducing a non-comparative fairness notion based on desired system properties.
result The proposed fairness notion provides guarantees in terms of comparative fairness.
The paper studies fairness in multi-stage selection problems and introduces a method to compute fair selections.
problem Fairness in multi-stage selection problems with additional features at each stage.
method Introducing fairness notions, proposing a linear program for fair selections, and defining the price of local fairness.
result It is possible to have a selection that has a small price of local fairness and is close to locally fair.
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.
FCA improves fair clustering by optimizing utility and fairness.
problem Balancing fairness and utility in clustering.
method FCA alternates between aligning data and optimizing cluster centers in an aligned space.
result FCA achieves a superior trade-off between fairness and utility.
Fair MP-Boost improves fairness and interpretability in boosting methods.
problem Improving fairness and interpretability in boosting methods.
method Fair MP-Boost uses adaptive sampling of minipatches to balance accuracy and fairness.
result Fair MP-Boost enhances fairness and accuracy while providing interpretable feature importance.
New framework for fair ranking with noisy protected attributes.
problem Errors in socially-salient attributes undermine fairness guarantees.
method Modeling perturbations in protected attributes and incorporating probabilistic information.
result Framework provides provable guarantees on fairness and utility.
The paper introduces metrics and methods to improve fairness in text classification models.
problem Counterfactual fairness issues in text classifiers, like predicting toxicity based on sensitive attributes.
method Developed a metric (CTF) and three approaches (blindness, counterfactual augmentation, CLP) to optimize counterfactual fairness during training.
result Blindness and CLP methods improve counterfactual fairness without harming classifier performance.
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.
The paper explores fairness in credit scoring using machine learning.
problem The lack of research on fair machine learning in credit scoring.
method Revisits statistical fairness criteria, catalogs algorithmic options, and empirically compares fairness processors.
result Multiple fairness criteria can be approximately satisfied at once, and fair processors deliver a good balance between profit and fairness.
Proposes a method to achieve quantile fairness in predictions.
problem Lack of research on quantile fairness in socially sensitive domains.
method Introduces a framework to learn a real-valued quantile function under Demographic Parity fairness.
result Demonstrates superior empirical performance and uncovering fairness-accuracy trade-offs.
Proposes FACT, a diagnostic for understanding group fairness trade-offs.
problem Group fairness notions often conflict with each other, requiring a cost in model performance.
method Characterizes trade-offs via the fairness-confusion tensor and optimizes accuracy and fairness objectives.
result Demonstrates the use of FACT on synthetic and real datasets to understand accuracy-fairness trade-offs.
Proposes individual fairness for clustering, making data points prefer their own cluster.
problem No fair clustering for clustering data points.
method Introduces a new fairness notion for clustering and studies its feasibility and heuristics.
result Individual fairness for clustering is NP-hard in general but feasible for one-dimensional data.
New fairness metrics improve collaborative filtering fairness.
problem Collaborative filtering's bias in historical data leads to unfair predictions for minority groups.
method Identified and proposed four new fairness metrics to address different forms of unfairness.
result Our new metrics better measure fairness than baseline metrics and effectively reduce unfairness.
Unified framework for measuring causality-based fairness in machine learning.
problem Challenges of measuring causality-based fairness from observational data.
method Unified definition of PC fairness and constrained optimization method.
result Correctness and effectiveness of the proposed method demonstrated on synthetic and real-world datasets.
Unified approach for fair classification with overlapping groups.
problem Ensuring fairness across multiple overlapping groups in prediction problems.
method Probabilistic population analysis leading to Bayes-optimal classifier, unifying existing methods.
result Outperforms baselines in fairness-performance tradeoff on real datasets.