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View article: An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software
An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software Open
Large language models (LLMs) show promise for translating natural-language statutes into executable logic, but reliability in legally critical settings remains challenging due to ambiguity and hallucinations. We present an agentic approach…
View article: Automated Validation of COBOL to Java Transformation
Automated Validation of COBOL to Java Transformation Open
Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterpriselevel code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-b…
View article: Automated Testing of COBOL to Java Transformation
Automated Testing of COBOL to Java Transformation Open
Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterprise-level code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-…
View article: Utilizing API Response for Test Refinement
Utilizing API Response for Test Refinement Open
Most of the web services are offered in the form of RESTful APIs. This has led to an active research interest in API testing to ensure the reliability of these services. While most of the testing techniques proposed in the past rely on the…
View article: DetAIL: A Tool to Automatically Detect and Analyze Drift in Language
DetAIL: A Tool to Automatically Detect and Analyze Drift in Language Open
Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based systems are as trusted as traditional software. Traditional soft…
View article: Interpretable Differencing of Machine Learning Models
Interpretable Differencing of Machine Learning Models Open
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model with new training data. In these cases, we wish to go beyo…
View article: DetAIL : A Tool to Automatically Detect and Analyze Drift In Language
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language Open
Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based systems are as trusted as traditional software. Traditional soft…
View article: Plug and Play Counterfactual Text Generation for Model Robustness
Plug and Play Counterfactual Text Generation for Model Robustness Open
Generating counterfactual test-cases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the test-cases, a desired property is the ability to control the test-case g…
View article: Explainable Data Imputation using Constraints
Explainable Data Imputation using Constraints Open
Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analysis methods, such as principle components analysis or singul…
View article: FROTE: Feedback Rule-Driven Oversampling for Editing Models
FROTE: Feedback Rule-Driven Oversampling for Editing Models Open
Machine learning models may involve decision boundaries that change over time due to updates to rules and regulations, such as in loan approvals or claims management. However, in such scenarios, it may take time for sufficient training dat…
View article: Data Synthesis for Testing Black-Box Machine Learning Models
Data Synthesis for Testing Black-Box Machine Learning Models Open
The increasing usage of machine learning models raises the question of the reliability of these models. The current practice of testing with limited data is often insufficient. In this paper, we provide a framework for automated test data …
View article: Automated Testing of AI Models
Automated Testing of AI Models Open
The last decade has seen tremendous progress in AI technology and applications. With such widespread adoption, ensuring the reliability of the AI models is crucial. In past, we took the first step of creating a testing framework called AIT…
View article: Towards API Testing Across Cloud and Edge
Towards API Testing Across Cloud and Edge Open
API economy is driving the digital transformation of business applications across the hybrid Cloud and edge environments. For such transformations to succeed, end-to-end testing of the application API composition is required. Testing of AP…
View article: Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets Open
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Various tools and techniques are available that assess data quality with respect to general cleaning and profiling checks.…
View article: Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text Open
Machine Learning has seen tremendous growth recently, which has led to a larger adaptation of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP s…
View article: Testing Framework for Black-box AI Models
Testing Framework for Black-box AI Models Open
With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-to-end generic framework for testing AI Models which performs automat…
View article: Generate Your Counterfactuals: Towards Controlled Counterfactual\n Generation for Text
Generate Your Counterfactuals: Towards Controlled Counterfactual\n Generation for Text Open
Machine Learning has seen tremendous growth recently, which has led to larger\nadoption of ML systems for educational assessments, credit risk, healthcare,\nemployment, criminal justice, to name a few. The trustworthiness of ML and NLP\nsy…
View article: Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text Open
Machine Learning has seen tremendous growth recently, which has led to larger adoption of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP syste…
View article: Verifying Individual Fairness in Machine Learning Models
Verifying Individual Fairness in Machine Learning Models Open
We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or unfair) if there is a pair of valid inputs which are close to…
View article: Bias Mitigation Post-processing for Individual and Group Fairness
Bias Mitigation Post-processing for Individual and Group Fairness Open
Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness. Our novel framework includes a…
View article: AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias Open
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This paper introduces a new open source Pytho…
View article: AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and\n Mitigating Unwanted Algorithmic Bias
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and\n Mitigating Unwanted Algorithmic Bias Open
Fairness is an increasingly important concern as machine learning models are\nused to support decision making in high-stakes applications such as mortgage\nlending, hiring, and prison sentencing. This paper introduces a new open source\nPy…
View article: Automated Test Generation to Detect Individual Discrimination in AI Models
Automated Test Generation to Detect Individual Discrimination in AI Models Open
Dependability on AI models is of utmost importance to ensure full acceptance of the AI systems. One of the key aspects of the dependable AI system is to ensure that all its decisions are fair and not biased towards any individual. In this …
View article: Functional Partitioning of Ontologies for Natural Language Query Completion in Question Answering Systems
Functional Partitioning of Ontologies for Natural Language Query Completion in Question Answering Systems Open
Query completion systems are well studied in the context of information retrieval systems that handle keyword queries. However, Natural Language Interface to Databases (NLIDB) systems that focus on syntactically correct and semantically co…