Interpretable machine learning faces acceptance challenges.
problem Acceptance issues in interpretable machine learning.
method Analyzing stakeholder concerns and desires.
result Need deeper consideration of stakeholders' needs.
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48 results for “stakeholder acceptance”
Improves algorithmic recourse to guide towards both acceptance and improvement.
problem Algorithmic recourse recommendations may not lead to improvement.
method Improvement-Focused Causal Recourse (ICR) requires recommendations to guide towards improvement and leverages causal knowledge to design accurate decision systems.
result ICR guides towards both acceptance and improvement given correct causal knowledge.
EXAGREE selects a stakeholder-aligned model to reduce conflicting explanations in machine learning.
problem Conflicting explanations from different attribution methods limit the adoption of machine learning models in safety-critical domains.
method EXAGREE is a two-stage framework that selects a Stakeholder-Aligned Explanation Model (SAEM) from a set of similar-performing models, maximizing Stakeholder-Machine Agreement (SMA).
result EXAGREE achieves simultaneous gains in faithfulness, plausibility, and fairness over baselines while preserving task accuracy.
NFTs raise concerns like scams, racism, and sexism; centralization vs decentralization debate.
problem Concerns and value judgments of stakeholders in NFT market.
method Mixed quantitative and qualitative methods: social media analysis and interviews.
result Identified financial scams, counterfeit NFTs, hacking, and unethical NFTs as major issues.
Community-based system dynamics improves ML fairness by involving excluded stakeholders.
problem Bias in ML system development during problem formulation.
method Community-based system dynamics (CBSD) for stakeholder participation.
result CBSD facilitates deeper problem understanding and bias mitigation.
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.
A groundwater market modelq-fin.GN
A model for groundwater trading among stakeholders.
problem Groundwater trading among stakeholders in a basin.
method Optimization of production by agents considering water rights, consumption, and trading.
result Characterization of Nash equilibrium in a 1-period setting and initial insights into multi-period game.
Managers of US National Forests must decide what policy to apply for dealing with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber companies, home owners, and wildlife biologists) have often led to spirited political debates and even violent eco-terrorism. One way to transform these conflicts into…
Sample-Rank simplifies MO recommendations by sampling and ranking, improving revenue with stable conversion rates.
problem Multi-objective recommendations in online food ordering systems.
method Multi-goal sampling followed by ranking, reducing MO problem to LTR model.
result Significant lift in revenue (2.64%) with stable conversion rates, no drop in last-mile traversal.
Methodology explores fairness limits in decision tree classifiers.
problem Understanding statistical limits of bias mitigation in machine learning.
method Multi-objective framework optimizing accuracy and fairness.
result Decision tree models can be optimized for fairness with minimal accuracy loss.
Study finds explainability used mainly by ML engineers, not end users.
problem Insufficient transparency for end users in deployed machine learning models.
method Synthesizes limitations of current explainability techniques and develops a framework for clear goals.
result Current explainability techniques primarily serve internal stakeholders, not external users.
Framework for eliciting fairness constraints from stakeholders.
problem Complex, nuanced fairness requirements not captured by simple definitions.
method Algorithm for learning accurate models subject to elicited fairness constraints.
result Provably convergent and oracle efficient algorithm with generalization bounds.
Proposes a method to infer the distributional impacts of predictive models on stakeholders.
problem The influence of predictive models on target variable distribution, leading to performative prediction.
method Modeling agents' responses as a cost-adjusted utility maximization problem and using optimal transport to align pre- and post-model distributions.
result Provides estimates for the cost associated with these responses and demonstrates the quality of these estimates.
Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Conformal Prediction Setsstat.AP
Paper uses conformal prediction sets to make criminal justice risk assessments fairer.
problem Fairness issues in criminal justice risk assessment algorithms.
method Adopting conformal prediction sets to remove unfairness from algorithms and covariates.
result Constructs confusion tables and measures fairness effectively free of racial differences.
Data science teams collaborate extensively, using various tools and stakeholders.
problem Lack of understanding in how data science workers collaborate in practice.
method Conducted an online survey with 183 data science workers.
result Data science teams are highly collaborative and use multiple tools and stakeholders.
ABOUT ML aims to improve transparency in ML lifecycle documentation.
problem Lack of standard documentation in machine learning lifecycle.
method Initiative to operationalize ML transparency and standardize documentation.
result Helps address gaps in ML lifecycle documentation.
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.
Interpretable AI model boosts investment confidence and profitability.
problem Challenges in financial forecasting and interpretability in decision-making models.
method SHAP-based explainability technique for interpretable AI models.
result Notable enhancement in investor's portfolio value.
New star-shaped acceptability indexes generalize existing methods.
problem Generalizing existing acceptability measures.
method Characterizing acceptability indexes through star-shaped risk measures and sets.
result Introducing concrete examples linked to various financial measures.
Toolkit and taxonomy for diverse AI explainability methods.
problem Diverse stakeholder needs for AI explanations.
method Open-source software toolkit with eight explainability methods and evaluation metrics.
result Taxonomy helps navigate explanation methods.
Studies acceptable bundles on a partially punctured polydisk.
problem Understanding acceptable bundles in Simpson--Mochizuki theory.
method Expository study with new arguments.
result New arguments differ from Mochizuki's.
ELICA helps analysts understand unfamiliar domains by extracting relevant terms.
problem Communication barriers between analysts and stakeholders in unfamiliar domains.
method ELICA uses WFSTs to dynamically extract and label requirements-relevant knowledge from text and non-linguistic cues.
result ELICA supports analysts in understanding and eliciting requirements from unfamiliar domains.
Study on acceptable bundles on a punctured disk.
problem Understanding acceptable bundles in Simpson--Mochizuki theory.
method Detailed study and introduction of a new invariant.
result Arguments differ from Simpson and Mochizuki's.
FinTech framework clusters innovations for financial services.
problem Lack of comprehensive definition and analysis of FinTech.
method Narrative review of over 100 studies, clustering framework development.
result Developed a comprehensive FinTech clustering framework.
This paper introduces STAP to measure DEX efficiency and shows better routing algorithms increase DEX performance and stakeholder benefits.
problem Measuring and improving the efficiency of decentralized exchanges (DEXs).
method Introduces STAP as a measure of DEX efficiency and compares two routing algorithms.
result Better routing algorithms improve DEX efficiency and stakeholder benefits.
Simple conditions for comonotonic additive risk measures from acceptance sets.
problem Conditions for comonotonic additive risk measures from acceptance sets.
method Conditions on acceptance sets for induced comonotonic additive risk measures.
result Acceptance sets induce comonotonic additive risk measures if and only if the acceptance sets and their complements are stable under convex combinations of comonotonic random variables.
The paper develops a theory for speculative decoding acceptance criteria.
problem Speculative decoding's acceptance criteria and their rejection regions.
method Characterization of rejection regions as lower level sets of the target distribution, derivation of exact and margin-based certificates.
result Relaxed and tree-based acceptance criteria substantially enlarge the region of certified acceptance.
Study uses VC correlation to uncover directional financial relationships.
problem Understanding causal relationships between financial variables.
method Volatility constrained correlation (VC correlation) method.
result Operating income is most influential, while market capitalization and revenue are most susceptible.
Dual representations for systemic risk measures using acceptance sets.
problem Measuring systemic risk in financial systems.
method Developed dual representations for systemic risk measures based on acceptance sets.
result Simple and self-contained proof of dual representations for utility-based risk measures.
In this paper we present a theoretical framework for studying coherent acceptability indices in a dynamic setup. We study dynamic coherent acceptability indices and dynamic coherent risk measures, and we establish a duality between them. We derive a representation theorem for dynamic coherent risk measures in terms of …
Cactus improves auto-regressive decoding speed without sacrificing quality.
problem Accelerating auto-regressive decoding while maintaining output quality.
method Formalizes speculative sampling as constrained optimization and proposes Cactus for controlled divergence from the verifier distribution.
result Empirically validated effectiveness across various benchmarks.
Proposes new deviation measures using Minkowski gauges.
problem Lack of suitable acceptance sets for deviation measures.
method Derives deviation measures through Minkowski gauges of acceptable sets.
result Any positive homogeneous deviation measure can be accommodated in the framework.
Improved NTL detection using human-in-the-loop approach with explainability.
problem Challenges in detecting NTL due to biased data and black-box models.
method Human-in-the-loop approach with explanatory methods to guide model training.
result Improved accuracy, interpretability, robustness, and flexibility of the prediction model.
Research uses Twitter data to analyze public perception of city logistics.
problem Understanding public views on city logistics from multiple stakeholders.
method Collecting Twitter content, applying unsupervised learning and NLP.
result Built an Interest Map and determined sentiment of city logistics entries.
Generative Adversarial Network (GAN) generates user-friendly explanations for loan denials.
problem Lack of explainable AI for financial services, especially in loan denials.
method Developed a GAN to generate explanations for loan denials, using a representative dataset.
result Demonstrated the GAN can generate explanations for various stakeholders, including applicants and decision makers.
Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lenscs.LG
This paper rates robustness of multi-modal time-series forecasting models.
problem Robustness of AI systems in time-series forecasting is crucial for stakeholders.
method Causal analysis to assess robustness of MM-TSFM models.
result Multi-modal forecasting models are more robust than numeric models.
Acceptability maximizationq-fin.MF
The paper studies optimal investment using acceptability indices to maximize portfolio performance.
problem Optimal investment problem using coherent acceptability indices.
method Numerical algorithm approximating the original problem, dynamic coherent risk measures, set-valued Bellman's principle.
result Acceptability maximization problem reduces to a one-period problem under certain conditions.
Estimates boundaries for acceptable bilateral gamma risk in financial markets.
problem Determining the compensation needed for risky future cash flows to be considered acceptable.
method Statistical inference from market prices and derivatives, using prospect theory.
result Upper and lower boundaries for bilateral gamma risk are estimated and tested against market data.
New index for evaluating cash flow processes over a fixed horizon.
problem Evaluating performance of cash flow processes over a fixed investment horizon.
method Extended acceptability indices to càdlàg processes, providing a new index based on Average Value-at-Risk and running minimum.
result Suggested index represents a RAROC-type model for performance evaluation.
Study reveals bias in machine learning conference reviews.
problem Bias in machine learning conference review process.
method Comprehensive analysis of ICLR papers from 2017-2020.
result Strong institutional bias in accept/reject decisions.
Star-Shaped deviationsq-fin.RM
Introduces Star-Shaped deviation measures for risk analysis.
problem Risk measurement and analysis in finance.
method Characterizes Star-Shaped deviation measures through acceptance sets and convex deviation measures.
result Exposes the relationship between Star-Shaped risk measures and deviation measures.
We consider a trader who wants to direct his portfolio towards a set of acceptable wealths given by a convex risk measure. We propose a black-box algorithm, whose inputs are the joint law of stock prices and the convex risk measure, and whose outputs are the numerical values of initial capital requirement and the funct…
Model uses Preisach hysteresis to predict gig worker acceptance, reducing costs and improving fill rates.
problem Predicting and optimizing gig worker acceptance in labor markets.
method Preisach hysteresis model applied to neural network and XGBoost classifier for binary transaction outcomes.
result Model reduces total wage bill by 21.3% and increases expected fill rate by 9.7 pp.
Study financial contracts pricing in markets with nonproportional costs and constraints.
problem Financial contract pricing in markets with nonproportional transaction costs and portfolio constraints.
method Direct and dual characterization of market-consistent prices with acceptable risk thresholds.
result Extension of the Fundamental Theorem of Asset Pricing to include good deals and scalable good deals.
Measures incompleteness of financial markets using asset rank and acceptance set dimension.
problem Measuring incompleteness of incomplete financial markets.
method Introduce rank of vector price process and dimension of acceptance set.
result Rank and dimension of acceptance set are equal.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
problem Investor preference for a set of securities over a reference security with constraints.
method Mean-variance optimization with Sharpe Ratio performance measurement.
result Derives an optimal portfolio that maximizes returns while minimizing risk.
Acceptance Rates of Invertible Neural Networks on Electron Spectra from Near-Critical Laser-Plasmas: A Comparisonphysics.plasm-ph
INNs improve acceptance rates in electron spectra analysis.
problem Analyzing electron spectra from near-critical laser-plasmas.
method Invertible Neural Networks (INNs) for forward and inverse modeling.
result INNs significantly increase acceptance rates up to a factor of 10.
The theory of acceptance sets and their associated risk measures plays a key role in the design of capital adequacy tests. The objective of this paper is to investigate, in the context of bounded financial positions, the class of surplus-invariant acceptance sets. These are characterized by the fact that acceptability …