Training AI models to function properly in unpredictable environments requires natural data, also called raw or unprocessed real-world scenario data [2,6]. The flaws, assumptions, and nuances in this data are perfect for AI to predict and adapt well to a massive range of unique scenarios. Synthetic data, on the other hand, is loosely defined as artificially generated to imitate real-world data and often does not have the authenticity or richness necessary for creating robust and reliable AI.

What sits behind natural data is the truth in how humans interact, speak, and behave. For example, the subtleties of language (i.e., slang in different regions, typos, and differences between syntaxes) are only learned through immersion in linguistic diversity [39]. As a result, models trained only on synthetic data may not generalize well to other demographics and contexts, which could produce biased or incorrect results. Thus, having natural data is crucial in constructing AI systems capable of interacting and servicing a wide range of user bases, addressing the fundamental challenge of responsible progress on AI.

Workflow of processing natural data for AI development: •

Start: Initiation of data collection.

In the modern age of digitalization, data and artificial intelligence (AI) are playing a central role in ushering innovation across industries, as shown in Fig. 1. As the quantity of information keeps increasing due to digital devices and online platforms, along with rapid AI progression, our days are getting used to seeing eye-popping improvements.

Unpacking the Synergy of Data and AI:

The Data Deluge: In today’s world, a vast amount of data is being produced by devices and connectivity. It comes with a volume that is difficult to manage; otherwise, AI can process and unearth insights at an unimaginable scale.

The AI Revolution: What was once a concept is more of a reality, as AI now has become the feature that lifts our world. It covers doings/moves such as machine learning and natural language processing, which refers to the capability of machines to learn with data execution tasks requiring human-level cognition. AI and data are related to each other by:

Data Feeds AI: High-quality data trains AI models to recognize patterns and make informed decisions.

Table 2 illustrates how the use of natural data in AI development is influenced by trends towards open-source, the high computational and environmental costs of training models, the financial focus on generative AI technologies, and growing concerns about AI ethics and fairness. These elements highlight the ongoing evolution and challenges of using natural data for AI advancements.

Now that AI has become associated with everything from personalized product recommendations to medical diagnostics, there is a demand for enormous stores of diverse data. Large models (like, for instance, GPT-4) [15,43] need the order of billions of words and images to operate at their best levels in terms of both accuracy and reliability. The burgeoning number of applications in different domains further expedites this demand. It is no secret that industries, be it finance, healthcare, or e-commerce, rely heavily on AI-powered analytics for better insights, to take control of trends, and to make strategic decisions [43].

On the one hand, they count on this level of demand. On the one hand, it hammers out rapid iteration as companies fight to make their AI shine brighter than others. On the other hand, it puts enormous pressure on natural data reserves, leading to a supply-demand imbalance that eventually slows down AI progress. Synthetic data, an alternative to genuine transaction data for testing and developing new analytics such as advanced Machine Learning (ML) [44] or AI, has, of late, become many organizations’ answer where the continued advancement, albeit very slowly brings with positive outcomes in part because it provides test cases where otherwise notary from real-world legitimate events were are even less use for plan these type algorithms. The absence of suitable sources for real data can reduce AI applications’ reliability and ethical integrity, making them less attractive to use in critical sectors.

Table 3 summarizes the latest data on the growing demand for data in AI as of 2023, along with key trends and developments across various industries:

These insights demonstrate the critical role of data in driving advancements in AI across various sectors, highlighting the immense growth in data demand and the evolving capabilities of AI technologies.

AI has been training AI systems on ever-larger datasets, which is why we now have high-performing models such as ChatGPT or DALL-E 3. Simultaneously, research indicates that the growth of online data stocks is significantly slower than that of datasets utilized for AI training. In a paper published last year, researchers predicted we would run out of high-quality text data by 2026 if the current AI training trends continue. They also estimated that low-quality language data would be exhausted between 2030 and 2050 and low-quality image data between 2030 and 2060. According to the accounting and consulting firm PwC, artificial intelligence has the potential to contribute a staggering US$15.7 trillion (A$24.1 trillion) to the global economy by 2030. However, running short of usable data could slow down its development [50].

However, it is not just a by-product of its limitations as it has ethical and socio-economic implications. Without natural, high-quality data, AI models cannot remain perpetually privy to the dynamics of change and instead become captives in their synthetic ivory towers. For example, large language models require continuous access to diverse, up-to-date data to remain relevant and accurate. Otherwise, their outputs might become obsolete or inaccurate in time.

In addition, without ample natural data, AI systems may also become biased. Because these models are trained on incomplete or homogeneous datasets, they learn and replicate the biases built into that data, e.g., leading to discriminatory/unfair outcomes. One example might include an AI system used in hiring, which may lack diverse data, leading to certain demographics being preferred over others and ultimately favoring one group, thereby perpetuating societal imbalance. The consequences of these biases could be devastating as AI systems become central to different decision-making processes, from court judgments to healthcare recommendations [51].

Data scarcity is also very expensive from a monetary perspective. Many may struggle to innovate as companies who rely on AI for efficient operations, cost reduction, or enhanced customer experiences find it difficult when they are not sure about the origin of their data. This, in turn, could stall AI deployment in industry and, with it, the development of technologies that improve economic advancement. Table 4 lists key challenges of data scarcity in AI—i.e., data exhaustion, risk of bias, regulatory constraints, and cost—and their potential negative implications on model performance, fairness, and innovation.

As natural data becomes less available, synthetic data has become a possible solution. Synthetic data is fake; it is artificially created to mirror real-world data, and the possibilities of being generated are endless. However, replacing natural data comes with some risks and imperfections. One is synthetic data because it cannot replicate the nuanced complexities that humans experience in interactions. AI models do great in a controlled environment but will struggle to generalize on chaotic, real-life situations. Many synthetic data also risk exacerbating pre-existing biases by default, as they tend to be generated based on patterns in existing data that often exclude specific user groups.

Furthermore, using synthetic data touches on its ethical dilemmas. This is a problem in machine learning as the AI may generate data that it assumes is correct by using another self-trained algorithm. Concepts like “AI training AI” are not accountable or transparent, so these models’ authenticity and accuracy cannot be easily proven. The biases contained within synthetic data will eventually dilute into the AI systems themselves, and in situations where accuracy is critical for a design, this could have adverse effects. Table 5 highlights how synthetic data can address data scarcity by increasing accessibility, affordability, control over bias, scalability, and ethics while also outlining associated risks like loss of authenticity, potential for bias, and ethical concerns.

The lack of natural data is also hindered by privacy and ethical challenges. Human-generated data must be acquired and employed, often at the cost of sensitive private-output validation with significant end-user privacy implications. Instances like the Cambridge Analytica scandal have seeped into internet culture and public consciousness concerning data privacy, rising to quadruple scrutiny among nations. This has led to severe regulations and left little scope for vendors or site owners to snoop around without permission.

AI companies have to navigate data regulations that change dramatically between countries; they must also balance this with their deep wish for more user data and our demand for a private age. When the data is used to train AI systems that were not explicitly consented to or carried out with explicit permissions, ethical challenges start showing their face. Others are calling for more robust frameworks, especially concerning data collection, reiterating the importance of ethical approaches to AI regarding standards, as shown in Table 6.

Given these challenges, the AI industry should also work to maintain energy—and data-efficient systems. With so many seemingly successful algorithms, developing data-efficient algorithms that extract as much value from each data point will be vital to progress once natural resources are scarce. Policymakers will also have a role in making that happen, such as through regulations to facilitate responsible data use and ensure businesses maintain high ethical standards.

Much like oil, data is a resource that could be the key to unlocking even more potential human progress—but as we move forward into an uncertain future where unlimited data may no longer be possible or generally allowed, people can turn their attention from the acquisition of massive amounts of low-quality convenience samples and direct our efforts on authoring and cataloging diversity in “good” taste if it were. Ultimately, it comes down to combining lawful data quality with trustworthy AI systems and responsible development to ensure top-notch security features. If the industry keeps these values in mind, it should be able to innovate and grow responsibly, with AI as a positive force for good.

Collaboration and strategic partnerships between organizations can be pivotal in overcoming data scarcity:

Data Sharing Initiatives: Companies like Google and IBM have pioneered sharing large datasets. For example, Google’s release of the ImageNet database has revolutionized computer vision research.

Public-Private Partnerships: Collaborations between governments and tech companies can facilitate the development of AI technologies with shared datasets. An example is the partnership between the U.S. Department of Health and AI startups to analyze health data securely.

Table 9 highlights significant partnerships between organizations aimed at leveraging collaboration to address challenges, including data scarcity.

Effective policy and regulation are crucial to ensuring data is used responsibly:

Data Privacy Regulations: The implementation of GDPR in Europe [64] and the CCPA in California has set new benchmarks for data privacy, influencing AI data handling practices worldwide.

Incentives for Data Sharing: Governments can incentivize data sharing with tax breaks and grants, as seen in the EU’s Data Governance Act, which aims to foster data sharing while ensuring privacy.

Table 10 explores how specific data privacy regulations affect AI development and application across various regions.

This is a common challenge where rare but critical events, such as fraudulent transactions, need to be identified, like fraudulent transactions. For a hands-on understanding of synthetic data generation, we used the Credit Card Fraud Detection Dataset [68], a benchmark dataset in which only 0.17% of transactions are marked as fraudulent. This case study is the application of synthetic oversampling techniques, such as SMOTE (Synthetic Minority Oversampling Technique), to mitigate data imbalance (See Appendix A, Table A1) and enhance AI model accuracy.

Dataset Overview: The dataset [68] consists of anonymized transaction data with significant class imbalance, as shown in Eqs. (1) and (2):

Normal Transactions (Support: 85,295):

This imbalance is a clear representation of practical data scarcity, where the availability of examples for critical classifications (fraudulent cases) is minimal.

Addressing Practical Data Scarcity

This case study shows that data scarcity can be alleviated substantially through synthetic data generation techniques like SMOTE. By generating more samples of the minority class, the model was able to:

Reduce Bias (Recall—Fraud Transactions): Overcome the inherent bias of the model towards the majority class.

Applicability across Domains

The success of SMOTE, in this case, has broader implications:

In healthcare, synthetic data can help detect rare diseases with limited availability.

Though theoretically, the development of AI appears promising, a number of real-world applications regarding health care have shown the effectiveness of these technologies in the real world. Applying a RandomForestClassifier to the Breast Cancer Wisconsin dataset represents another case study demonstrating how AI approaches the dual challenge of data sparsity and decision ethics in diagnostics. The model yielded high precision and recall with a balanced dataset of benign and malignant cases. This could be helpful in the early detection of a disease and its accurate diagnosis, which is one of the most important aspects of any patient care and treatment. Applications of this type constitute milestones in transforming healthcare by equipping medical professionals with reliable tools for decision-making. This case study describes an application of RandomForestClassifier using the Breast Cancer Wisconsin dataset [69] to estimate a diagnosis as benign or malignant. The dataset [69] is a multivariate one, consisting of 569 samples with 30 features each, which are further divided into a training set of 70% and a test set of 30%. The model RandomForest was trained and afterward tested on the test set in order to calculate the precision, recall, f1-score, and support of both diagnostic classes. Results, represented in Table 12, suggest very high accuracy regarding the diagnostic capability of the model: 98.33% precision and 93.65% recall for malignant cases and precision of 96.39% with a recall of 99.07% for benign cases. These metrics underline the robustness of the model, with F1-scores of 95.93% for malignant and 97.72% for benign diagnoses, therefore postulating that this model is accurate and reliable in distinguishing the two conditions quite well, as seen from Table 12. The support values are 63 for malignant and 108 for benign, showing the number of instances evaluated in each class and the balance in the dataset used.

The above case study shows the vast potential of machine learning to improve diagnostic accuracy in the medical field. Therefore, it justifies the development of AI tools that can support clinical decision-making and hopefully improve patient outcomes.

Ethical Considerations

Although synthetic data resolves most scarcity-related issues, it introduces its own set of potential risks:

Bias Propagation: Synthetic samples can also inherit biases from the original data.

This raises the need for synthetic data to be utilized along with a strong validation mechanism and an ethical framework, as discussed in Section 3.4.

The pursuit of Artificial General Intelligence (AGI) and agentic AIs entering many industries as a new type of workforce are some of the hottest topics in AI-related spaces in the mid-2020s. Such well-known AI personas as Altman, Huang, Sutskever et al. claim that AGI has already been achieved and/or they know exactly how to do it [70–74]. While this is currently a top-secret, the possible solutions are those presented in Fig. 2 or a combination of them, which is even more likely. Top Large Language Models (LLMs) confirmed the possible solutions through the short survey to gather their input.

As can be seen from Fig. 2, the top option with the highest weight is Synthetic Data Generation. The authors agree with this direction [74] and believe that creating high-quality synthetic data is impossible without AI-human collaboration or, in other words, a Human-in-the-loop.

This study underscores the significance of synthetic data generation as a pivotal technological innovation. Synthetic data addresses one of the most pressing challenges in AI development by enabling AI to function effectively in domains with limited access to natural data. Focusing on enhancing recall and overall model efficacy further emphasizes the benefits of complementing conventional model training with synthetic data to surmount challenges associated with natural data availability. As AI systems continue to extend into the areas where lack of data becomes a bottleneck, hybrid schemes utilizing synthetic data, transfer learning, and few-shot learning will gain even more traction. This study illustrates that you can bring AI to reality in low-resource environments and achieve responsible and sustainable AI development goals. Developers can overcome data bottlenecks and improve model generalization by carefully generating diverse and representative synthetic samples.

Small language models [13–15] are designed with fewer parameters, reducing their reliance on massive datasets compared to larger models. Their compact architecture allows for effective training with limited data, making them suitable for niche applications or domains where data collection is challenging. SLMs also provide advantages in terms of reduced computational cost and faster inference, further enhancing their practicality.

Quantization and pruning help mitigate data scarcity by enabling the deployment of models with reduced memory footprints and lower computational demands. Quantization achieves this by representing model weights and activations with lower precision, while pruning removes less important connections in the network. Consequently, these techniques allow for efficient training and deployment of models even with limited data, as the reduced model complexity lessens the risk of overfitting and improves generalization on smaller datasets.

Future research could explore how AI integrates with emerging technologies such as quantum computing, which might enhance model training efficiency and solve complex optimization problems faster than classical methods. This integration could be particularly beneficial in data-intensive domains like drug discovery [29] and financial modeling [74]. However, practical implementations remain a challenge and require further investigation. Additionally, research should focus on dynamic AI governance frameworks (See Appendix A, Table A1), such as the EU’s Data Governance Act, which can adapt to the rapid pace of technological change [16]. Future work could explore how regulatory sandbox environments (See Appendix A, Table A1) allow AI systems to be tested while ensuring ethical compliance and risk mitigation. Another promising area is advancing synthetic data generation techniques. While current methods like Generative Adversarial Networks (GANs) [77] and diffusion models are effective, they still struggle with maintaining diversity, realism, and fairness. Future research could focus on hybrid approaches that combine synthetic data with human feedback (Human-in-the-Loop AI) to improve data quality [78]. This collaborative approach leverages human expertise to guide AI systems, enhancing the realism and applicability of synthetic data [79]. Moreover, Green AI, as discussed in this paper, is gaining importance in developing computationally efficient models [76]. Investigating techniques like model pruning, quantization, and smaller AI architectures like Small Language Models (SLMs) like Phi-4 and Mistral could provide sustainable AI solutions for low-resource environments. These approaches aim to reduce the environmental impact of AI development while maintaining performance and adaptability. Addressing these areas will ensure that AI remains ethical, efficient, and adaptable in overcoming data scarcity challenges.