AI NEEDS BETTER HUMAN DATA, NOT BIGGER MODELS

Last updated: June 19, 2025, 20:55 | Written by: Elizabeth Stark

The AI landscape is currently dominated by a 'bigger is better' mentality.We're constantly bombarded with news about ever-larger language models (LLMs) boasting billions, even trillions, of parameters. In AI research, everyone seems to think that bigger is better. The idea is that more data, more computing power, and more parameters will lead to models that are more powerful.Think ChatGPT, Bard, Gemini - these giants capture the public imagination with their seemingly limitless ability to generate text, images, and code.The assumption is simple: more data, more computing power, and more parameters will inevitably lead to more intelligent AI. Moreover, they can not attract specialized talent, which is necessary to produce high-quality data to progress AI to human-level reasoning. This is where decentralization and AI come together as a powerful synergy. Our proposition stands out amidst all this. We are a human-powered data foundry that matches enterprise AI models with aBut a growing chorus of experts are suggesting a fundamental flaw in this approach.The truth is, AI's progress is being held back not by the size of the models, but by the quality of the data they're trained on. Innovations won't be relevant if AI models continue to train models based on poor-quality data.We need to shift our focus from quantity to quality, recognizing that human expertise in data curation, decentralized feedback, and ethical oversight is essential for building trustworthy and high-performing AI. AI needs better human data, not bigger models. no commentsIt’s about crafting finely tuned datasets that reflect the nuances of human knowledge, understanding, and ethical considerations.Ultimately, the key to unlocking AI's true potential lies not in bigger models, but in better human data.

The Problem with ""Big Data"" in AI Training

The current emphasis on ""big data"" in AI training often overlooks a critical factor: the inherent flaws and biases that can exist within these massive datasets.Simply throwing more data at a model doesn't guarantee better results; in fact, it can exacerbate existing problems. Poor-quality data can lead to inaccurate predictions, biased outcomes, and a general lack of reliability. AI/ML models may struggle to keep up with relevant output unless specialists real humans with proper credentials work to refine them. This demonstrates the need for human experts to guide AI s development by ensuring high-quality curated data for training AI models. Human frontier data is keyThis is because big data is so often improperly formatted, lacking metadata, or ""dirty,"" meaning incomplete, incorrect, or inconsistent.

Consider this: a large language model trained on a dataset containing biased language will inevitably perpetuate that bias in its own outputs.Similarly, an image recognition system trained on a dataset with limited representation of certain demographics may struggle to accurately identify individuals from those groups. The thinking goes: the bigger the model, the smarter the AI. But a new survey of AI experts reveals a growing skepticism of this idea. While today s AI models can generate fluent text, recognize images and even perform complex problem-solving tasks, they still fall short of human intelligence in key ways.These examples highlight the urgent need for a more discerning approach to data selection and preparation.We are not using the full potential of the data we already have available to us.

The Power of Human-Curated Data

The solution to AI's data problem lies in a greater emphasis on human-curated data. Artificial intelligence has been growing in size. The large language models (LLMs) that power prominent chatbots, such as OpenAI s ChatGPT and Google s Bard, are composed of well more than 100This involves carefully selecting, cleaning, and annotating data to ensure its accuracy, relevance, and ethical soundness. ai若缺乏数据管理和训练实践方面的人类专业知识，不过是纸老虎。尽管增长预测惊人，但如果继续基于劣质数据训练模型，ai创新将毫无意义。除了提升数据标准外，ai模型还需要人类介入语境理解和批判性思维，以确保伦理化ai开发和正确输出生成。Human experts bring to the table critical thinking skills, contextual understanding, and domain-specific knowledge that AI models simply cannot replicate.

Human expertise in data curation is essential for several reasons:

Ensuring Accuracy: Humans can identify and correct errors in data that might be missed by automated processes.
Removing Bias: Human curators can identify and mitigate biases in data that could lead to unfair or discriminatory outcomes.
Adding Context: Humans can provide valuable context and annotations that help AI models better understand the data.
Maintaining Ethical Standards: Human oversight is crucial for ensuring that data is collected and used in an ethical and responsible manner.

For example, a medical AI system designed to diagnose diseases requires data that has been carefully reviewed and annotated by medical professionals.This ensures that the system is trained on accurate and reliable information, leading to more accurate diagnoses.Similarly, an AI system used in criminal justice needs to be trained on data that is free from racial and socioeconomic biases to avoid perpetuating discriminatory practices.

Decentralization and AI: A Powerful Synergy

While human expertise is crucial, the process of data curation doesn't have to be centralized and controlled by a few large organizations.In fact, decentralization can play a vital role in improving data quality and accessibility. By empowering individuals and communities to contribute to data curation, we can tap into a wider range of perspectives and expertise.

Decentralized data curation can take many forms, including:

Citizen science initiatives: Engaging the public in data collection and annotation for scientific research.
Crowdsourcing platforms: Leveraging the collective intelligence of a large group of people to label and categorize data.
Community-based data trusts: Creating organizations that manage and share data on behalf of specific communities, ensuring that their interests are represented.

This approach not only improves data quality but also promotes greater transparency and accountability in AI development.By involving a wider range of stakeholders, we can ensure that AI systems are aligned with societal values and address the needs of diverse communities. For years, AI research has largely followed a single formula: more data, bigger models, better results. This approach has fueled major breakthroughs in generative AI, with models like ChatGPT, Gemini and Claude reaching new milestones in conversational ability.Moreover, they can attract specialized talent, which is necessary to produce high-quality data to progress AI to human-level reasoning.

The Ethical Imperative of Human Oversight

The rise of AI raises important ethical questions about bias, fairness, and accountability. cointelegraph.com: Opinion by: Rowan Stone, CEO at SapienAI is a paper tiger without human expertise in data management and training practices. Despite massive growth projections, AI innovations won t be relevant if they continue training models based on poor-quality data. Besides improving data standards, AI models need human intervention for contextual understanding and critical thinkingAI models trained on biased data can perpetuate and amplify existing inequalities, leading to discriminatory outcomes in areas such as hiring, lending, and criminal justice.To address these concerns, it's essential to incorporate ethical considerations into every stage of the AI development process, from data collection to model deployment.

Human intervention is crucial for ensuring ethical AI:

Identifying and Mitigating Bias: Human experts can identify and mitigate biases in data and algorithms that could lead to unfair or discriminatory outcomes.
Ensuring Transparency and Explainability: Humans can help to make AI systems more transparent and explainable, allowing users to understand how decisions are being made.
Establishing Accountability: Human oversight is necessary for establishing clear lines of accountability for the actions of AI systems.
Upholding Human Values: Human judgment is essential for ensuring that AI systems are aligned with human values and ethical principles.

Moving Beyond ""More Data"": A New Paradigm for AI Development

The current focus on ""more data"" as the primary driver of AI progress is unsustainable and ultimately counterproductive. We need to shift towards a new paradigm that prioritizes data quality, human expertise, and ethical considerations. This requires a fundamental rethinking of how we approach AI development.

Focusing on High-Quality Data

Instead of blindly collecting massive amounts of data, we need to be more selective and deliberate in our data acquisition efforts. Why Gen AI Needs Better Data, Not Bigger Models . and the public with its ability to produce human-like text, images, and even code. The development of large language models (LLMs) like GPT-3This means:

Defining clear data quality standards: Establishing metrics for accuracy, completeness, consistency, and relevance.
Implementing rigorous data validation processes: Ensuring that data meets established quality standards before being used for training.
Investing in data curation and annotation: Employing human experts to clean, label, and annotate data to improve its quality and usefulness.

Investing in Human Capital

Human expertise is the key to unlocking AI's true potential. This is a noteworthy dismissal of tech industry predictions that, since the generative AI boom of 2025, has maintained that the current state-of-the-art AI models only need more data, hardwareWe need to invest in training and education to develop a workforce that is skilled in data curation, ethical AI, and responsible AI development.This includes:

Creating data science education programs: Training data scientists in data quality, bias detection, and ethical considerations.
Supporting interdisciplinary collaboration: Fostering collaboration between data scientists, ethicists, and domain experts.
Promoting diversity and inclusion: Ensuring that the AI workforce reflects the diversity of the population it serves.

Embracing Ethical Principles

Ethical considerations should be at the forefront of AI development.This means:

Developing ethical guidelines for AI: Establishing clear principles for the responsible design, development, and deployment of AI systems.
Implementing ethical review processes: Evaluating AI projects for potential ethical risks and biases.
Promoting transparency and accountability: Making AI systems more transparent and explainable, and establishing clear lines of accountability for their actions.

Examples of AI Successes Driven by High-Quality Data

While much of the current discussion focuses on the limitations of ""big data,"" there are also numerous examples of AI systems that have achieved remarkable success through the use of high-quality, human-curated data.These examples demonstrate the power of focusing on quality over quantity.

Consider the development of AI-powered medical diagnosis tools. AI needs better human data, not bigger models. Opinion by: Rowan Stone, CEO at SapienAI is a paper tiger without human expertise in data management and training practices.These systems rely on meticulously curated datasets of medical images, patient records, and expert annotations.The accuracy and reliability of these systems depend heavily on the quality of the data they are trained on.Similarly, AI systems used in fraud detection rely on carefully labeled datasets of fraudulent and legitimate transactions. Home Opinion India s AI boom: Why fixing AI hallucinations starts with better data, not bigger models Follow us Published by the RP-Sanjiv Goenka Group, Fortune India - the Indian edition of Fortune magazine - sets the benchmark for editorial excellence and thought leadership in Indian business journalism.The effectiveness of these systems hinges on the ability of human experts to identify and categorize fraudulent activities.

Addressing Common Concerns About Human-Curated Data

Despite the clear benefits of human-curated data, some concerns are often raised about its cost and scalability.While it's true that human curation can be more expensive and time-consuming than simply collecting large amounts of raw data, the long-term benefits in terms of accuracy, reliability, and ethical soundness far outweigh the costs.Moreover, there are ways to make human curation more efficient and scalable.

What about the cost of human curation?

While human curation does involve costs, it is essential to look at the overall lifecycle cost. AI has a data quality problem. In a survey of 179 data scientists, over half identified addressing issues related to data quality as the biggest bottleneck in successful AI projects. Big data is so often improperly formatted, lacking metadata, or dirty, meaning incomplete, incorrect, or inconsisteFlawed data causes faulty algorithms and systems, costing far more to fix and remedy issues.It also diminishes customer trust. AI s growth is limited by poor-quality data, not model size. Human expertise in data curation, decentralized feedback and ethical oversight is essential for building trustworthy, high-performing AI.Furthermore, we need to factor in the potential cost savings from reduced errors, improved efficiency, and increased user trust. News Aggregator. Blockchair News Aggregator allows you to stay on top of all crypto-related news, in just one place. News Aggregator brings you news in 11 languages from more thanBy investing in high-quality data, we can avoid the costly consequences of biased or inaccurate AI systems.

Can human curation be scaled to meet the demands of AI?

While scaling human curation can be a challenge, there are several strategies that can be employed to make it more efficient and effective. Opinion by: Rowan Stone, CEO at SapienAI is a paper tiger without human expertise in data management and training practices. Despite massive growth projections, AI innovations won t be relevant if they continue training models based on poor-quality data. Besides improving data standards, AI models need human intervention for contextual understanding and critical thinking to ensure ethical AIThese include:

Leveraging crowdsourcing platforms: Engaging a large pool of human annotators to label and categorize data.
Developing AI-assisted curation tools: Using AI to automate some of the more repetitive tasks involved in data curation, freeing up human experts to focus on more complex tasks.
Adopting active learning techniques: Prioritizing the annotation of the most informative data points, reducing the overall amount of data that needs to be labeled.

The Future of AI: A Human-Centered Approach

The future of AI depends on our ability to move beyond the ""bigger is better"" mentality and embrace a more human-centered approach.This means prioritizing data quality, human expertise, and ethical considerations in every stage of the AI development process. Despite massive growth projections, AI innovations won t be relevant if they continue training models based on poor-quality data.By investing in high-quality data, fostering collaboration between humans and machines, and upholding ethical principles, we can unlock the true potential of AI to benefit society.

This shift requires a change in mindset, a re-evaluation of our priorities, and a commitment to building AI systems that are not only powerful but also trustworthy, responsible, and aligned with human values.The real leap in AI will come when we value the wisdom embedded in carefully curated data, guiding the machines towards genuine understanding and ethical decision-making.

Conclusion

In conclusion, the relentless pursuit of bigger AI models without a corresponding focus on data quality is a recipe for stagnation and potential harm. AI needs better human data, not bigger models.Human expertise in data curation, decentralized feedback, and ethical oversight is paramount.The future of AI hinges on our ability to prioritize quality over quantity, invest in human capital, and embrace ethical principles.By doing so, we can unlock the true potential of AI to solve some of the world's most pressing challenges and create a more just and equitable future for all.Are you ready to prioritize high-quality data in your AI initiatives?The time to act is now.