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Ensuring Data Ethics And Governance In The Era Of Digital Transformation
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By Abdulaziz Aldoseri Abdulaziz Aldoseri Scilit Preprints.org Google Scholar, Khalifa N. Al-Khalifa Khalifa N. Al-Khalifa Scilit Preprints.org Google Scholar and Abdel Magid Hamouda Abdel Magid Hamouda Scilit Preprints.org Google Scholar *
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Received: May 3, 2023 / Revised: May 30, 2023 / Accepted: June 7, 2023 / Published: June 13, 2023
The use of artificial intelligence (AI) is becoming increasingly widespread in sectors such as healthcare, finance and transportation. Artificial intelligence relies on the analysis of large data sets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This article in-depth explores and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical issues as well as technical knowledge and skills. This paper examines these challenges in detail and offers recommendations on how businesses and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain a competitive advantage in the digital age. It is expected that as this review article provides and discusses different strategies to address data challenges for AI over the past decade, it will be very useful for the scientific research community to generate new and original ideas to rethink our approaches to data strategies for AI.
Artificial intelligence (AI) refers to the ability of machines to imitate human intelligence and perform tasks that typically require human intelligence, such as learning, problem solving, decision making, and natural language understanding . Figure 1 outlines AI technologies including machine learning, natural language processing, robotics, and computer vision. Machine learning is a subset of AI that involves training computer algorithms to learn patterns in data and make predictions or decisions based on the data . Deep learning is a type of machine learning that uses multi-layer neural networks to process complex data such as images or speech . Natural language processing is the ability of computers to understand, interpret and generate human language, including speech and text . Computer vision is the ability of computers to analyze and interpret visual information such as images and videos .
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Artificial intelligence is a rapidly developing field that has the potential to change the way we live and work. From healthcare to finance and transportation, AI has the potential to transform a wide range of industries and create new opportunities for businesses and organizations. Artificial intelligence is transforming various industries, including healthcare, finance, and transportation, with significant advancements in machine learning and deep learning techniques [6, 7]. At the heart of this transformation is the data needed to train and test AI models. AI models leverage large data sets to identify patterns and trends that are difficult to detect using traditional data analysis methods. This allows them to learn and make predictions based on the data they were trained on.
However, using AI data is a real challenge. Data quality, quantity, diversity, and privacy are essential elements of data-driven AI applications, and each presents its own set of challenges. Poor data quality can lead to inaccurate or biased AI models, which can have serious consequences in areas such as healthcare and finance. Insufficient data can lead to models that are overly simplistic and unable to accurately predict real-world outcomes. A lack of data diversity can also lead to biased models that do not accurately represent the population they are intended to serve. Finally, data privacy is a major concern, as AI models may require access to sensitive data, raising concerns about data privacy and security.
In this article, we explore the challenges of using data for AI and offer recommendations for companies trying to address them. To address these challenges, businesses and organizations must develop strategies and frameworks that support data quality, quantity, diversity and privacy. This may include implementing data cleansing and validation processes to ensure data quality, collecting and managing large amounts of disparate data, and implementing privacy policies and procedures to protect sensitive data. By addressing these challenges, businesses and organizations can harness the power of data to create accurate, efficient and equitable AI applications that benefit society.
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Data is essential to AI because it provides the basis on which machine learning algorithms learn, make predictions, and improve their performance over time. Training an AI model requires large amounts of data to enable the model to recognize patterns, make predictions, and improve its performance over time.
AI algorithms need data to learn patterns and make predictions or decisions based on the data. AI machine learning techniques are algorithms that allow machines to learn patterns and make predictions from data without explicit programming . These techniques are widely used in various applications such as natural language processing, image and speech recognition, and recommendation systems. In general, the more data an AI algorithm has, the more accurate its predictions or decisions will be. There are several data learning approaches to create AI systems [8, 9]; To make the article complete, we present the following, as shown in Figure 2.
Supervised learning: In supervised learning, an AI system is trained on a set of labeled data, where each data point is associated with a label or target variable. The goal is to develop a model that can accurately predict the target label or variable for new data points. This approach is commonly used in tasks such as image classification, speech recognition, and natural language processing .
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Unsupervised Learning: In unsupervised learning, an AI system is trained on an unlabeled data set where there are no target variables to predict. The goal is to identify patterns, relationships, and structures in the data. This approach is commonly used for tasks such as clustering, anomaly detection, and dimensionality reduction .
Reinforcement learning: In reinforcement, the AI system learns to make decisions based on feedback from the environment. The system receives rewards or punishments based on its actions and adapts its behavior accordingly. This approach is commonly used in tasks such as gaming, robotics, and autonomous driving .
Transfer Learning: In transfer learning, an AI system uses knowledge acquired in one task to improve performance in another related task. The system is pre-trained on a large dataset and then fine-tuned on a smaller dataset for a specific task. This approach can help reduce the amount of data needed to train an AI model and improve its accuracy and performance .
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Deep learning: Deep learning is a type of neural network-based machine learning that is particularly effective for tasks involving large amounts of data and complex relationships. Deep learning models are composed of multiple layers of interconnected nodes that can learn increasingly complex data representations. This approach is commonly used in tasks such as image and speech recognition, natural language processing, and computer vision .
Ensemble Learning: Ensemble learning is a technique in which multiple models are trained and combined to make predictions or decisions. Combining predictions from multiple models can improve the accuracy and reliability of the final result .
In general, the choice of data learning approach depends on the specific task, data and available resources. It is important to carefully evaluate the benefits and limitations of each approach and choose the one that best fits the requirements of the AI application being developed.
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Data-centric and data-driven are two related but distinct concepts in the world of data analysis and decision-making. By leveraging data, organizations can gain a deeper understanding of their operations, customers and markets and make more informed decisions based on data-driven insights. Data-centric approaches are commonly used in industries such as finance, healthcare and retail, where accurate and timely data is essential for decision-making. For example, in the health sector
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