Data quality is crucial for training accurate, unbiased, and trustworthy machine learning models as well as for their correct evaluation. Recent works, however, have shown that even popular datasets used to train and evaluate state-of-the-art models contain a non-negligible amount of erroneous annotations, biases, or artifacts. While practices and guidelines regarding dataset creation projects exist, to our knowledge, large-scale analysis has yet to be performed on how quality management is conducted when creating natural language datasets and whether these recommendations are followed. Therefore, we first survey and summarize recommended quality management practices for dataset creation as described in the literature and provide suggestions for applying them. Then, we compile a corpus of 591 scientific publications introducing text datasets and annotate it for quality-related aspects, such as annotator management, agreement, adjudication, or data validation. Using these annotations, we then analyze how quality management is conducted in practice. A majority of the annotated publications apply good or excellent quality management. However, we deem the effort of 30\% of the works as only subpar. Our analysis also shows common errors, especially when using inter-annotator agreement and computing annotation error rates.

In the era of large AI models, the complex architecture and vast parameters present substantial challenges for effective AI quality management (AIQM), e.g. large language model (LLM). This paper focuses on investigating the quality assurance of a specific LLM-based AI product--a ChatGPT-based sentiment analysis system. The study delves into stability issues related to both the operation and robustness of the expansive AI model on which ChatGPT is based. Experimental analysis is conducted using benchmark datasets for sentiment analysis. The results reveal that the constructed ChatGPT-based sentiment analysis system exhibits uncertainty, which is attributed to various operational factors. It demonstrated that the system also exhibits stability issues in handling conventional small text attacks involving robustness.

It is essential for healthcare and pharmaceutical companies to be aware of both critical and non-critical risks when conducting quality clinical trials. However, managing both takes time and money -- resources that clinical teams are often strapped for. Additionally, the risks that organisations define at the start of the trial may change, meaning the data they need to collect will also change. In order to address these challenges, researchers must break down silos and create a centralised process for monitoring and managing risk. Many organisations are turning to risk-based quality management (RBQM) practices to make that happen.

In 2012, Authors Björn Bloching, Lars Luck, and Thomas Ramge published In Data We Trust: How Customer Data is Revolutionising Our Economy. The book goes into detail about how a lot of companies have all the information they need at their fingertips. Companies no longer need to make decisions based on their gut feeling and the market, they can use streams of data to give them a better understanding of what the future looks like and what their next move should be. As the world of data, in particular, Artificial Intelligence continues to grow - more and more people are skeptical. Some may say that the use of data and autonomous features have improved our day-to-day lives.

Artificial Intelligence and the Data Quality Conundrum … smart factories) leverage cyber-physical systems powered by AI, machine learning (ML) …

In recent years technology has become prominent, both at work and at home. Machine learning (ML) and Artificial Intelligence (AI) are evolving quickly today. Almost everyone will have some interaction with a form of AI daily. Some common examples include Siri, Google Maps, Netflix, and Social media (Facebook/Snapchat).AI and ML have popularly used buzzwords right now, often used interchangeably. Most experimentation has been geared to finding specific solutions to specific problems.

Agriculture is a both major industry and the foundation of the economy. Artificial Intelligence (AI) techniques are widely used to solve a variety of problems and to optimize the production and operation processes in the fields of agriculture, food, and bio-system engineering. The use of artificial intelligence in the agriculture supply chain is becoming more and more important while involving Artificial Intelligence ML algorithms. The main four clusters are preproduction, production, processing, and distribution. In fact, in the preproduction, ML technologies are used, especially for the predictions of given features.

In the past decade, Artificial Intelligence (AI) has become a part of our daily lives due to major advances in Machine Learning (ML) techniques. In spite of an explosive growth in the raw AI technology and in consumer facing applications on the internet, its adoption in business applications has conspicuously lagged behind. For business/mission-critical systems, serious concerns about reliability and maintainability of AI applications remain. Due to the statistical nature of the output, software 'defects' are not well defined. Consequently, many traditional quality management techniques such as program debugging, static code analysis, functional testing, etc. have to be reevaluated. Beyond the correctness of an AI model, many other new quality attributes, such as fairness, robustness, explainability, transparency, etc. become important in delivering an AI system. The purpose of this paper is to present a view of a holistic quality management framework for ML applications based on the current advances and identify new areas of software engineering research to achieve a more trustworthy AI.

As we come towards the end of the year, industry experts discuss how the clinical research market has evolved and how they are looking to prepare for the challenges to overcome in 2020. The past year saw CluePoints, a software developer providing clinical trial monitoring services, build on its agreement with the US Food and Drug Administration (FDA) to provide its services supporting the regulator's oversight of the clinical trial market. Asked about how market demands have shifted since 2018, the company's CCO, Patrick Hughes pointed to the ICH E6 (R2) good clinical practice (GCP) guidance, which'became a reality' for sponsors and clinical research organizations (CROs). As a result, this made 2019, "the year in which we have seen the biggest momentum shift across the industry in the adoption of a risk-based approach to trial management," Hughes said. Risk-based quality management in clinical trials focuses on identifying the most important compliance risks in a study and setting them as a priority in order to prevent and avoid potential disruptions.

The datapine report authored by Sandra Durcevic states that 2019 is the year of data discovery and data quality management. The report describes that the strategies in Business Intelligence will be customized which indicates a good success rate in adapting the Business Intelligence and analytics for their business. Artificial Intelligence: AI emphasizes the creation of intelligent machines that work and react like humans. Data discovery: For many companies, data discovery has seen a massive impact in recent years by the way the company uses manpower with curated data. This empowerment of users in the business is considered to be a recent trend at present according to the BI Practitioners.