Artificial Intelligence (AI) systems sometimes make errors and will make errors in the future, from time to time. These errors are usually unexpected, and can lead to dramatic consequences. Intensive development of AI and its practical applications makes the problem of errors more important. Total re-engineering of the…
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48 results for “AI error correction”
New AI error correctors improve classifier performance with provable guarantees.
problem Improving AI classifier performance with scarce training data.
method Weakly supervised AI error correctors with performance guarantees.
result Provable performance guarantees for AI error correction.
This work proposes external correctors for quick AI error corrections without system modification.
problem Quick corrections of AI errors without modifying legacy systems.
method Special `external` devices with classifiers for high-dimensional data.
result Simple classifiers can correct small errors in high-dimensional data.
A method compares AI corrections to a base model for explaining predictions.
problem Creating explanations for AI predictions.
method Introduces a surrogate model to correct a simpler base model and provides criteria for accuracy and fidelity.
result Induces neighborhoods of instances with ideal accuracy and fidelity.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
problem Cognitive biases affect human-AI collaboration, leading to suboptimal outcomes.
method Randomized experiment with 2,784 participants, manipulating AI suggestion quality, task burden, and financial incentives.
result Individual attitudes toward AI are the strongest predictor of performance, influencing accuracy and overcorrection.
PBC improves AI and dynamical subseasonal forecasts by reducing biases.
problem Subseasonal forecast accuracy drops due to model biases and compounding errors.
method Probabilistic bias correction (PBC) using machine learning to correct historical forecasts.
result PBC doubles AI Forecasting System's subseasonal skill and improves dynamical model skill.
Method curates cost-effective, high-quality datasets using AI models.
problem Costly manual labeling of datasets.
method Probably Approximately Correct Labels (PACL) method.
result Curates high-quality datasets with low overall labeling error.
The paper solves optimal bounds for separating data points in high dimensions.
problem Correcting AI errors and analyzing vulnerabilities in high-dimensional data.
method General stochastic separation theorems with optimal probability estimates.
result Explicit and optimal estimates of separation probabilities for important classes of distributions.
AI-generated variables bias regression estimates; methods correct for invalid inference.
problem Bias in regression estimates due to AI-generated variables.
method Two methods: bias correction and joint estimation.
result Valid inference restored through proposed methods.
Local surrogate model improves time series forecasts and provides interpretable explanations.
problem Improving time series forecasting accuracy while maintaining interpretability.
method A local surrogate model is used to correct the base model's predictions, making the corrections interpretable by re-fitting the base model to the error-predicted data.
result The method can discover and explain underlying patterns in the data, improving both accuracy and interpretability.
The paper studies how to use AI-generated labels in econometrics to avoid bias.
problem Small misclassification errors in AI-generated labels can lead to large biases in econometric estimators.
method The paper proposes a coupled-label bootstrap method to correct bias and deliver valid inference.
result The coupled-label bootstrap method is valid without the strong independence condition between true and imputed labels.
VERAFI improves financial AI by verifying calculations and compliance.
problem Financial AI systems generate errors and violations during reasoning.
method VERAFI combines dense retrieval, reranking, and automated reasoning policies.
result VERAFI achieves 94.7% factual correctness, 81% relative improvement.
We consider the problem of efficient "on the fly" tuning of existing, or {\it legacy}, Artificial Intelligence (AI) systems. The legacy AI systems are allowed to be of arbitrary class, albeit the data they are using for computing interim or final decision responses should posses an underlying structure of a high-dimens…
AI-assisted interviews allow respondents to describe experiences naturally, but mapping those accounts into structured survey variables is fallible.
problem Mapping AI-assisted interview responses into structured survey variables is fallible.
method Adaptive Matrix Validation (AMV) is proposed, which involves mapping responses into tabular data and using a small set of structured questions for statistical adjustment.
result The estimator calibrates mapped values using validation answers from other respondents and corrects remaining error with validation answers observed for the target respondent.
Explainable AI improves human decision accuracy but does not enhance it significantly.
problem Improving human decision-making through explainable AI.
method Comparing human decision accuracy with and without AI predictions, including or excluding explanations.
result Providing AI predictions improves human decision accuracy, but explanations do not significantly enhance it.
Generative AI decodes quantum codes without labeled data.
problem Efficient decoding of quantum error-correcting codes.
method Generative Transformers learn logical operators from unsupervised syndromes.
result Significantly better decoding accuracy than traditional methods.
DAIS improves AIS for differentiable marginal likelihood estimation.
problem Differentiable marginal likelihood estimation for complex models.
method Proposes Differentiable Annealed Importance Sampling (DAIS) to make AIS differentiable.
result DAIS achieves convergence and consistency in Bayesian linear regression.
AI enhances refinery optimization by detecting data errors and improving decision-making.
problem Interpreting and applying LP solutions for refinery optimization is challenging due to simplifications and data errors.
method Transformed ECOD methodology, Anomaly Detection tools, and high-dimensional data analysis.
result Identifies data supply errors and reveals business opportunities in refinery scheduling and planning.
Unified view of improving tree model interpretability and debiasing feature importance.
problem Improving interpretability and debiasing feature importance in tree-based models.
method Demonstrates a common thread among bias correction methods and local explanations for trees.
result Points out a bias in explainable AI for trees algorithms due to inbag data inclusion.
New approach shows AI can adapt like toddlers by correcting old knowledge.
problem Lack of adaptability in AI models compared to humans and animals.
method Casting adaptation as posterior correction and using Bayesian Learning Rule.
result AI can learn to adapt quickly by using posterior correction.
This work advances collaborative decision making by combining human and AI strengths in uncertainty quantification.
problem Current AI lacks robust decision-making capabilities under uncertainty, especially in high-stakes contexts.
method Introduces Human AI Collaborative Uncertainty Quantification (HACUQ) framework, formalizing AI-human collaboration and developing calibration algorithms.
result Optimal collaborative prediction sets follow a two-threshold structure, and online adaptation algorithms can adapt to evolving human behavior.
The paper certifies AI reliability via sampling and calibration, providing exact guarantees.
problem Ensuring trust in black-box AI systems' outputs.
method Self-consistency sampling and conformal calibration.
result Reliability levels derived from these methods offer finite-sample guarantees.
Optimal allocation of human effort to correct AI assessments in decision-making.
problem How to allocate costly human effort to correct noisy or biased AI-generated assessments.
method Decision-theoretic framework treating AI assessments as signals and human judgments as costly information. Developed estimation procedures under nonparametric and linear models.
result Our approach substantially outperforms LLM-only predictions and achieves performance comparable to full human review while using only 20-30% of the human information.
AIS corrects rollout-training mismatch in quantized RL, improving speed and stability.
problem Rollout-training mismatch in quantized RL causes bias and training collapse.
method Adaptive Importance Sampling (AIS) adjusts gradient correction per batch.
result AIS matches BF16 baseline on most tasks while improving speed.
Generative AI for Analystsq-fin.ST
Generative AI boosts analyst reports but increases forecast errors.
problem Improving financial analyst reports with AI.
method Natural experiment using FactSet's AI platform.
result AI-assisted reports are more comprehensive but lead to higher forecast errors.
The paper optimizes exceptions in a statistical production system using machine learning.
problem Lack of curated and labeled training data for machine learning in data quality assurance.
method Explainable supervised machine learning to identify and prioritize exceptions.
result Improvement in the quality and efficiency of exceptions generated and authenticated by users.
Theoretical study shows AI models can recover from contaminated training data.
problem Data contamination in AI training can degrade model performance.
method Theoretical analysis and experiments on various data types.
result Models converge to true distribution under mild conditions, with rate dependent on real data fraction.
SRPO improves AI alignment with human preferences through self-improvement and task-independent optimization.
problem AI models trained with RLHF lack self-correction mechanisms and struggle with task generalization.
method SRPO formulates the preference learning problem as a min-max objective, optimizing a self-improvement policy and a generative policy in an adversarial fashion, making the solution task-independent.
result SRPO outperforms existing methods, achieving 90% AI Win-Rate on XSum and 56% on Arena-Hard prompts after a single revision.
AI systems need reliable testing to ensure safety and trustworthiness.
problem Current AI Act lacks functional trustworthiness for AI systems.
method Define technical application distribution, set risk-based performance, and conduct statistically valid testing.
result Reliable functional trustworthiness is essential for AI systems.
Develops methods to correct bias in AI feedback for more accurate alignment.
problem Systematic bias in AI feedback compared to human labels.
method Two debiased alignment methods: DDPO and DIPO.
result Methods improve alignment efficiency and performance close to human-labeled data.
QTAML models quantum tunneling errors for AI robustness.
problem Quantum tunneling errors in AI inference.
method Derives weight-error distribution using WKB approximation, introduces TAC algorithm.
result TAC achieves 95% clean accuracy with 3.4-33.6x less ECC overhead.
There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an op…
A model corrects Lithuanian grammatical errors.
problem Lack of language skills and typing errors in Lithuanian.
method Transformer architectures for subword and byte-level approaches.
result F=0.92 for Lithuanian grammatical error correction.
XAI methods fail to explain ML models reliably.
problem Current XAI methods fail to provide reliable explanations for ML models.
method Formally define problems and design methods accordingly.
result Diverse notions of explanation correctness and metrics needed.
Evaluating AI investment strategies
problem Auditing a black-box algorithmic decision-maker
method Exact decomposition of cumulative regret
result Cumulative regret equals sum of per-period covariances
The study examines methods to correct measurement error in nutritional epidemiology studies.
problem Measurement error in nutritional studies leads to biased and underconfident estimates.
method The article reviews various bias-correction models for exposure variables in nutritional epidemiology.
result Bias-correction methods are essential for accurate inference in nutritional studies.
New tools for assessing and correcting bias in AI algorithms.
problem Fairness and bias in AI algorithms, especially when ground truth data is unavailable.
method Three tools: controlled fairness, retraining algorithms, and parameter adjustment algorithms.
result Effective in reducing bias and improving fairness in AI models.
Khovanov homology helps create quantum error-correcting codes.
problem Creating robust quantum error-correcting codes.
method Using Khovanov homology and its extensions to define and analyze quantum codes.
result New families of quantum codes with desirable properties.
Study loop corrections in random feature models affecting training and test errors.
problem Analyzing loop corrections in random feature models to understand training and test errors.
method Statistical physics and effective field theory approach to study loop corrections.
result Derived loop corrections to training error, test error, and generalization gap.
Humans increasingly interact with Artificial intelligence(AI) systems. AI systems are optimized for objectives such as minimum computation or minimum error rate in recognizing and interpreting inputs from humans. In contrast, inputs created by humans are often treated as a given. We investigate how inputs of humans can…
A new L2D system produces calibrated probabilities of expert correctness without sacrificing accuracy.
problem Calibration of learning to defer systems for safety.
method One-vs-all classifiers with a consistent surrogate loss function.
result Proposes a calibrated L2D system that outperforms existing methods in accuracy and calibration.
VB-Score evaluates AI systems without ground truth, revealing robustness.
problem Evaluating AI systems without ground truth labels, especially for entity-centric tasks.
method VB-Score uses variance-bounded evaluation, constraint relaxation, and Monte Carlo sampling.
result VB-Score reveals robustness differences not seen by conventional frameworks.
Correction for Error estimates for binomial approximations of game options [math.PR/0607123]
Fault-tolerant neural networks inspired by biological error correction codes.
problem Achieving reliable computation with unreliable neurons.
method Using biological error correction codes from grid cells in the mammalian cortex to develop a fault-tolerant neural network.
result Noisy biological neurons operate below a fault-tolerance threshold, suggesting a mechanism for reliable computation in the brain.
Quantum codes on hyperbolic lattices outperform Euclidean ones with higher rates and lower overhead.
problem Improving quantum error correction performance with hyperbolic lattices.
method Unified framework using Hyperbolic Cycle Basis algorithm for CSS codes construction and benchmarking.
result Achieved higher encoding rates and lower qubit overhead in hyperbolic quantum error correction codes.
Physics-Informed Inference Time Scaling for Solving High-Dimensional PDE via Defect Correctionmath.NA
SCaSML improves PDE solvers by correcting errors efficiently.
problem Reliable and error-free high-dimensional PDE solutions.
method Defect correction method to derive a Structural-preserving Law of Defect.
result SCaSML achieves faster convergence and reduced errors in high-dimensional PDEs.
Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.
problem Improving accuracy in time series prediction.
method Combines difference-attention LSTM and error-correction LSTM in a cascade approach.
result Improves prediction accuracy in time series.
AI detects 38% NFT trades likely manipulated, improving on indirect methods.
problem Detecting crypto wash trading using indirect methods and leaked data.
method Public NFT data analysis, direct estimation, AI-based estimator.
result AI reduces estimation errors in NFT markets, improving on indirect methods.