AI-driven solutions are rapidly adopted every day in a variety of industries, services and products. However, their effectiveness depends entirely on the quality of their trained data – this aspect is often misunderstood or overlooked during dataset creation.
As data protection authorities review how AI technologies are consistent with privacy and data protection regulations, companies are facing increasing pressure to annotate and refine data sets in a compliant and ethical way.
Is there a true ethical way to build AI datasets? What is the biggest moral challenge for a company? How to solve these challenges? How does an evolving legal framework affect the availability and use of training data? Let’s explore these issues.
Data Privacy and AI
By its essence, AI requires a lot of personal data to perform tasks. This raises concerns about collecting, saving and using this information. From the GDPR and the newly introduced European AI Act to the HIPAA in the United States, many laws around the world regulate and restrict the use of personal data, which regulate access to patient data in the healthcare industry.
Refer to strict data protection laws around the world / DLA Piper
For example, there are currently 14 states in the United States with comprehensive data privacy laws that will come into effect again in 2025 and early 2026. The new government shows that its approach to data privacy enforcement at the federal level has changed. The focus is on AI regulation, emphasizing promoting innovation rather than imposing restrictions. This shift includes abolishing previous AI execution commands and introducing new instructions to guide its development and application.
Data protection legislation is developing in various countries: in Europe, the laws are stricter, while in Asia or Africa, they tend to be less stringent.
However, personally identifiable information (PII) (such as facial images, official documents (such as passports), or any other sensitive personal data) is often restricted to some extent. According to the United Nations Trade and Development, the collection, use and sharing of personal information is a major issue in most parts of the world without its notification or consent of consumers. 137 of 194 countries ensure data protection and privacy. As a result, most global companies take extensive precautions to avoid using PII for model training, as regulations in the EU strictly prohibit such practices, which are rare in severely regulated walls such as law enforcement.
Over time, data protection laws have become more comprehensive and implemented globally. Companies adapt to practice to avoid legal challenges and meet emerging legal and ethical requirements.
What methods does the company use to obtain data?
Therefore, when studying data protection issues with training models, it is crucial to understand that companies have access to this data first. There are three main sources and main sources of data.
This approach enables data to be collected from crowdsourcing platforms, media inventory and open source datasets.
It is important to note that public stock media should abide by different licensing agreements. Even commercially used licenses often explicitly state that content cannot be used for model training. These expectations are through platform differences and require businesses to confirm their ability to use content in the way they need to.
Even if AI companies get content legally, they can still face some problems. The rapid development of AI model training has exceeded the legal framework, which means that rules and regulations surrounding AI training data are still evolving. As a result, companies must understand legal developments and carefully review licensing agreements before using inventory content for AI training.
One of the safest ways to prepare data sets is to create unique content, such as shooting people in controlled environments such as studios or outdoor locations. Prior to attending, individuals signed a consent form to use their PII, specifying the collected data, how to use it, and where to access it. This ensures complete legal protection and gives companies confidence that they do not face claims for illegal data use.
The main disadvantage of this approach is its cost, especially when creating data for edge cases or large-scale projects. However, for at least two reasons, large companies and businesses are increasingly continuing to use this approach. First, it ensures full compliance with all standards and laws and regulations. Second, it provides companies with data that is fully tailored to their specific scenarios and needs, ensuring the highest accuracy of model training.
- Synthetic data generation
Use software tools to create images, text, or videos based on a given situation. However, synthetic data has limitations: it is generated based on predefined parameters and lacks the natural variability of real data.
This lack of negatively affects AI models. Although it is not always the case with everything, it does not always happen, it is still important to remember that “model crashes” and that over-reliance on synthetic data can lead to lower models, resulting in poor quality output.
Synthetic data can still be very effective for basic tasks such as identifying general patterns, identifying objects, or distinguishing basic visual elements (such as faces).
However, this is not the best option when companies need to train models entirely from scratch or deal with rare or highly specific solutions.
The most revealed situations occur in a Carbine environment, such as a driver who is distracted by a child, someone is tired on the steering wheel, or even reckless driving. These data points are not common in public data sets and should not involve real individuals in private environments, so they should not. Because AI models rely on training data to generate synthetic outputs, they are difficult to represent scenarios that have never been encountered accurately.
When synthetic data fails, the created data (collected through a controlled environment with real participants) becomes the solution.
Data solutions providers like Keymakr put cameras in cars, hire actors, and document signs such as caring for babies, drinking from bottles or showing signs of fatigue. The actor signed a contract to expressly agree to use their data for AI training to ensure compliance with privacy laws.
Responsibilities in the dataset creation process
Each participant from the client to the annotation company has specific responsibilities outlined in its agreement. The first step is to create a contract detailing the nature of the relationship, including terms regarding non-disclosure and intellectual property rights.
Let’s consider the first choice of using data, i.e. when creating data from scratch. Intellectual Property states that any data created by the provider belongs to the recruiting company, which means it was created on their behalf. This also means that providers must ensure that data is legal and correctly obtained.
As a data solution company, Keymakr ensures data compliance and ensures that data can be legally used for AI training by first checking the jurisdictions that created the data, obtaining appropriate consent from all relevant personnel.
It is also important to note that once the data is used for AI model training, it is almost impossible to determine which specific data will help the model for the model, because AI fuses all the data together. Therefore, a particular output is not its output, especially when discussing millions of images.
Due to its rapid development, clear guidelines for allocating responsibilities remain established in the field. This is similar to the complexity surrounding the self-driving car, in which case questions about responsibility still need clear allocations, whether it is a driver, manufacturer or software company.
In other cases, when the annotation provider receives the annotation dataset, he assumes that the customer has legally obtained the data. If there are clear signs that the data has been illegally obtained, the provider must report it. However, this obvious situation is extremely rare.
It is also important to note that large companies, companies and brands that value their reputation are very careful about the location of their data sources, even if not created from scratch, but obtained from other legal sources.
In short, each participant’s responsibility during the data work process depends on the protocol. You can consider this process of a broader “sustainability chain” where each participant plays a crucial role in maintaining legal and ethical standards.
What are the misunderstandings about the backend of AI development?
A major misunderstanding about AI development is that AI models work similar to search engines, collecting and summarizing information to introduce information to users based on learning knowledge. However, AI models, especially language models, are usually based on functions of probability rather than real understanding. They predict words or terms based on statistical possibilities using patterns seen in previous data. Artificial intelligence does not “know” anything; it can infer, guess and adjust probabilities.
Furthermore, many believe that training AI requires huge data sets, but most of the things AI needs (such as dogs, cats, or humans) have established good knowledge. The focus now is on improving accuracy and refining the model, rather than reinventing the recognition capabilities. Most of today’s AI development revolves around the last small gap in accuracy rather than starting from scratch.
Ethical Challenges and How EU AI Act and U.S. Regulations Will I Affect Global AI Market
It is also important to have a clear understanding of what defines “ethics” when discussing the ethics and legitimacy of data legitimacy.
Today, the biggest ethical challenge for companies in AI is to determine whether what AI does or teaches is unacceptable. There is a broad consensus that ethical AI should help rather than hurt humans and avoid deception. However, AI systems can make mistakes or “illusions” that challenge the challenge of determining whether these errors are consistent with false information or harm.
AI ethics is the main debate with organizations such as UNESCO that participate in, and its key principles revolve around auditability and traceability of outputs.
The legal framework surrounding data access and AI training plays an important role in shaping the ethical landscape of AI. Countries with less restrictive data usage can achieve more accessible training data, while countries with stricter data laws limit the data availability of AI training.
For example, Europe, which adopted the AI Act, and the United States has backed down many AI regulations, providing a comparison method that demonstrates the current global landscape.
The EU AI Act is greatly affecting companies operating in Europe. It enforces a strict regulatory framework that makes it difficult for enterprises to use or develop certain AI models. Companies must obtain specific permissions to use certain technologies, and in many cases, the regulations effectively make it difficult for small businesses to comply with these rules.
As a result, some startups may choose to leave Europe or avoid operating there altogether, similar to the impact seen by cryptocurrency regulations. Large companies that can afford to derive compliance requirements may adapt. Nevertheless, the bill could bring AI innovation out of Europe in favor of markets like the United States or Israel, with less stringent regulations.
The decision of the U.S. to invest its main resources in AI development may also have disadvantages, but will cause more diversity in the market. While the European Union focuses on safety and regulatory compliance, the United States may promote more risk-taking and cutting-edge experiments.
