The artificial intelligence is evolving very quickly, reshaping fields as diverse as artistic creation or personal data management. However, this technological evolution raises ethical and legal questions. Three key challenges stand out: intellectual property, personal data protection, and algorithmic transparency. This article explores these issues and offers potential solutions to better regulate AI development.
Intellectual property: a void to be filled
Artificial intelligence (AI) has disrupted the world of artistic creation, generating textual, visual, and audio assets with remarquable speed and precision. However, this technological revolution comes with significant legal uncertainties: who own these creations? Can copyright be granted to an artificial intelligence? What protection mechanisms should be implemented to safeguard human creators? Facing this question, current legislation struggles to adapt, leading to uncertainties and emerging conflits.
Who owns an IA-Generated work?
The rise of generative AI, such as text and graphic creation models, has highlighted a concerning legal gap. Traditionally, copyright is based on the concept of “work of the mind”, requiring human intervention and intentional creative process. But, when it comes to AI-generated content, several complex scenario arise:
- The creator of the program: some argue that the work beyond to an AI developper since they designed the algorithm capable of generating content. This would mean attributing authorship to the software publisher or to the company behind the AI’s development.
- The user generating the work: others believed that the person using AI to produce an image or text should be recognized as the author, as he is the one initiating the creative intent (prompt) and issuing the command to the algorithm.
- Public domain or specific rights? Some legal experts propose a radical approach, suggesting that AI-generated works belong to no one and should be placed in the public domain due to the lack of sufficient human intervention to justify copyright. Others advocate for a hybrid legal framework, recognizing either a related right or a form of co-creation between the AI and its user.
This lack of legal consensus opens the door to complexe litigation, particularly regarding commercial use and reproduction rights. Without legal clarification, human creators risk seeing their rights diluted in a vast landscape of automatically generated digital content.
Respecting the authors of training data
Another fundamental issue lies in the use of existing works to train artificial intelligence. Generative AIs, whether for writing or visual creation, rely on vast databases of images, texts, and musics, often protected by copyrights. This practice raises several ethical and legal questions:
- Unauthorized use of existing works: many artists and authors criticize the massive absorption of their creations by AIs without permission or compensation. Indeed, most AI models are trained on datasets collected without informing or remunerating the original creators.
- The right to opt-out a potential solution: in response to these abuses, initiatives are emerging to establish a "right to opt-out," allowing artists and writers to exclude their works from the databases used by AIs. Some lawmakers are considering requiring companies developing these technologies to ensure transparency and obtain consent, similar to existing data protection regulations.
- The need for legal framework: various proposals aim to better protect creators, such as requiring AIs to credit the sources of inspiration they use or introducing a compensation system for authors whose works contribute to model training. Without such measures, AI risks impoverishing the cultural sector by diverting creators' rights for the benefit of anonymous algorithms.
Without clear regulations, the growing legal uncertainty could hinder both innovation and the protection of creators. Artificial intelligence offers fascinating possibilities, but it must be accompanied by ethical and legal considerations to ensure balance between technological progress and respect for human rights.
II. Personal data: a key protection issue
The rise of artificial intelligence largely relies on the use of personal data, raising significant challenges in terms of privacy and security.
Mass data collection and fraudulent uses
Every day, billions of pieces of information circulate on the web and are collected to train machine learning algorithms. However, this massive data collection often takes place invisibly, without users’ knowledge. Social media posts, online searches, interactions with voice assistants, and browsing histories all serve as sources of data exploited by AI. These pieces of information are used for various purposes, from improving algorithms to personalizing advertisements and profiling users. This large-scale data capture raises legitimate concerns about privacy and the transparency of digital companies’ practices.
Another major issue lies in the vulnerability of databases containing sensitive information. No infrastructure is entirely immune to cyberattacks, and numerous data breaches have already exposed millions of individuals to identity theft, financial fraud, or abusive exploitation of their private information. When such data falls into the wrong hands, it can be sold on the dark web or used to manipulate important decisions, such as loan approvals or hiring processes. The increasing number of data breaches highlights the urgent need for stronger personal data protection to prevent serious consequences for users.
Exposure of children's personal data
Children represent a particularly vulnerable group when it comes to these risks. From an early age, their data is collected by online platforms, video games, social networks, and educational apps. However, current protection mechanisms are still insufficient to ensure strict control over this collection. In many cases, children are unaware of the amount of information they share, and parents do not always have the necessary tools to regulate this exposure. The lack of strict regulations on the use of minors’ data can lead to abusive practices, particularly in targeted advertising and behavioral monitoring.
Consequences of erroneous data
Another significant danger lies in the impact of inaccurate data on AI-driven automated decisions. A simple inaccuracy in a database can lead to flawed analyses and serious injustices. In recruitment, for instance, an AI trained on biased data may favor or discriminate against certain profiles without valid reasons. Similarly, in the banking sector, an incorrect credit history could result in unjustified loan rejections. The amplification of these errors by algorithms risks exacerbating inequalities and undermining trust in automated systems.
Given these critical issues, stronger protective measures are essential. Regulations such as the General Data Protection Regulation (GDPR) in Europe have laid a solid foundation for personal data protection, but they must continuously evolve to keep pace with rapidly advancing AI technologies. Greater transparency in data collection and usage is essential, requiring companies to clearly inform users and provide them with real control over their personal information. Regular database audits, stricter security protocols, and the implementation of deterrent penalties for non-compliant companies are all necessary steps to ensure better privacy protection in the age of artificial intelligence.
III. Transparency: the need for clarity
Artificial intelligence operates using vast amounts of data, yet the origin and reliability of this data often remain unclear. Many algorithms rely on databases with unknown sources, making verification difficult. This lack of transparency creates a trust issue: if we do not know where the data comes from, how can we assess its relevance and accuracy?
Another challenge is data updates. AI models relying on outdated information can produce incorrect or even risky results, particularly in healthcare, finance, or in the legal sector. For instance, a model used for fraud detection or credit evaluation may be unfair if its data is not regularly updated. Similarly, a medical AI could suggest inappropriate treatments if it is based on outdated knowledge.
To ensure more reliable and ethical AI, transparency must be improved. Companies should clearly indicate the origin of their data and allow independent audits. Better traceability would strengthen trust and enhance the quality of AI outcomes.
Regulations should also be established to ensure regular database updates. Additionally, tools enabling users to report errors or biases in AI decisions would be highly beneficial.
Finally, it is crucial to demystify how AI works. Often perceived as "black boxes," AI systems need to become more understandable. Initiatives such as explainable AI aim to make these systems more transparent for the general public.
In summary, transparency is a key issue for ensuring the responsible and ethical use of artificial intelligence. Without efforts in this direction, errors, injustices, and public distrust toward these promising technologies will only multiply.
IV. Bias: AI as a mirror of inequality
Artificial intelligence is not neutral; it reflects the biases present in the data it is trained on. As a result, many AI systems reproduce, and sometimes amplify gender, age, and racial inequalities. These imbalances often stem from datasets that are not diverse or representative enough of social realities. For example, facial recognition systems have shown lower accuracy for individuals with darker skin tones due to insufficiently varied training samples.
These biases can have serious real-world consequences. In recruitment, an AI may unconsciously favor certain profiles based on historical data, excluding qualified candidates based on discriminatory criteria. Similarly, in banking, algorithms used for credit approvals may disadvantage certain demographic groups, reinforcing economic inequalities. Additionally, technologies like facial recognition are sometimes used for mass surveillance, raising concerns about individual freedoms and privacy protection.
To mitigate these biases, it is essential to develop more inclusive datasets that accurately reflect the diversity of individuals and social contexts. Regular audits of AI algorithms should be conducted to identify and correct biases. Furthermore, increased transparency in AI models and mechanisms that explain their decision-making processes would help build trust and reduce unintentional discrimination.
AI is a growing concern, as surveys indicate that people believe it will have a significant impact on their lives. While they remain open to AI, they also express numerous concerns. AI biases contribute to psychosocial risks, highlighting the importance of establishing a regulatory framework, such as the AI Act in Europe, which classifies AI risks into different levels. Companies must also take responsibility by implementing ethical governance for AI systems.
The rise of AI presents major challenges in intellectual property, personal data protection, and transparency. Addressing these issues requires clear regulations and the promotion of ethical practices in AI development. Only a balanced approach combining innovation with the protection of fundamental rights will ensure the responsible use of artificial intelligence.