Artificial intelligence (AI) has undergone rapid development in recent years, with conversational models such as ChatGPT and DeepSeek representing advanced applications of AI. Although both systems are built on similar deep learning architectures, their differences become clear in their applications, technological contexts, and the social and ethical challenges they raise. What are the key features of ChatGPT and DeepSeek, and what are the ethical and social implications, particularly those related to the concepts of "Society in the Loop" and "Human in the Loop"?

ChatGPT: versatility and language generation

ChatGPT, developed by OpenAI, is based on an advanced language model, GPT-4, which uses the Transformer architecture. This model is designed to generate natural language responses, adapting to a wide variety of contexts, from informal conversations to creative content production and answering specific questions. ChatGPT's main strength lies in its ability to understand language at a superficial level and generate fluent responses in real-time. However, as noted by Bender et al. (2021), the model lacks deep semantic understanding, making it vulnerable to errors or inconsistencies, especially when responses require specialized knowledge.

In this context, ChatGPT technology is altering the labor and social landscape, with applications ranging from automated customer support to content generation for marketing, and even assisting in education. However, the use of ChatGPT also raises concerns about misinformation and privacy, as the generated responses are based on pre-existing data without a critical understanding of the concepts discussed.

DeepSeek: specialization in information retrieval

In contrast, DeepSeek (developed by DeepSeek Technologies) stands out for its focus on advanced search and information retrieval. While ChatGPT is a generative model, DeepSeek is designed to analyze vast amounts of data and return contextually relevant answers, thanks to the use of deep learning models focused on semantics. This approach makes DeepSeek particularly useful in sectors requiring the management and analysis of complex information, such as scientific research, legal analysis, or consulting.

Unlike ChatGPT, which produces new responses based on a broad spectrum of linguistic data, DeepSeek focuses on identifying and extracting precise information from existing sources. DeepSeek's advanced semantic analysis is optimized to discover links and context-specific answers, responding in a much more targeted way compared to OpenAI’s generative model. While DeepSeek cannot produce new creative content, its precision makes it ideal for applications where the quality and reliability of information are crucial.

Strengths and limitations

ChatGPT's main strength lies in its ability to adapt to a wide range of linguistic tasks, such as the previously mentioned creative writing, translation, and customer support. However, its content generation without true semantic understanding can lead to errors that cannot be easily corrected, as highlighted in recent studies (Bender et al., 2021).

DeepSeek, on the other hand, excels in retrieving contextual information. Its application in academic and legal fields relies on its ability to analyze and return accurately contextualized information. However, its limited generative capacity restricts its use in creative fields.

Ethical and Social Considerations: "Human in the Loop" and "Society in the Loop"

The evolution of AI has raised important ethical and social issues, which are at the heart of public and academic debate. Reflections on concepts like "Human in the Loop" (HITL) and "Society in the Loop" (SITL) are more relevant than ever in the context of AI technology adoption. The management of automated decisions, such as those that ChatGPT might make in customer support or marketing, requires human oversight to avoid the risk of incorrect or misleading responses, as noted by Bender et al. (2021).

In the case of DeepSeek, the concept of "Society in the Loop" becomes crucial. Since this model focuses on retrieving highly contextualized information, social integration is key to ensuring that the AI adheres to the ethical norms and expectations of the community in which it is applied. While ChatGPT can sometimes be seen as an "autonomous" system, DeepSeek, being more oriented toward research and retrieval, requires constant oversight by human experts to ensure that the information returned is correct and relevant.

The regulation of AI, such as the proposed Artificial Intelligence Act by the European Commission, reflects the growing need to balance innovation with responsibility. Protection of privacy and fundamental rights must be ensured, and human oversight should be viewed not only as a safety measure but as an essential principle for integrating AI into society (Zeng et al., 2021).

Conclusions

In the comparison between ChatGPT and DeepSeek, it becomes clear how two advanced AI technologies can have different approaches while being built on similar deep learning architectures. ChatGPT represents a generative language model that excels in natural conversation and content production, but also raises ethical challenges related to misinformation and managing incorrect responses. On the other hand, DeepSeek, although also powered by artificial intelligence, stands out for its application in the retrieval of semantic and precise information, oriented toward more specialized contexts and less inclined toward autonomous content generation.

Both technologies, however, raise crucial questions related to human control and the social implications of their applications.

The differences between the two models are not only technical but also reflect in their social applications and the ethical challenges they raise. ChatGPT, with its ability to generate fluent responses on a wide range of topics, raises the question of human supervision and transparency in the responses it produces. If not adequately regulated, it could become a tool that fuels misinformation and alters the flow of information, making the concept of "Human in the Loop" (HITL) central. Human intervention in these situations is essential to avoid the AI acting without oversight that ensures its reliability and coherence, especially in sensitive contexts such as education or decision support.

DeepSeek, while lacking the same generative capacity, raises similar concerns but in different contexts. Its precision in retrieving information and its ability to navigate vast amounts of data make it a powerful tool for research and specialized consulting. However, DeepSeek’s approach also requires supervision to ensure that the results returned are truly relevant and align with the ethical values of society. Its ability to integrate information from diverse sources requires that the model's choices always align with social needs and expectations, an aspect that points to the concept of "Society in the Loop".

In light of these considerations, it is evident that although ChatGPT and DeepSeek are both powerful examples of how AI can transform our interaction with machines, they must be used with ethical awareness that respects the importance of human oversight and social regulation. In a world increasingly driven by artificial intelligence, the adoption of such technologies must be accompanied by a commitment to ensure that AI is designed, developed, and applied responsibly, so that it does not replace human reflection and decision-making. The challenge is not only technological but deeply social and political.

In summary, while ChatGPT and DeepSeek offer innovative and complementary solutions, their impact will depend on our ability to integrate AI into society in an ethical, controlled, and collective-oriented manner. The key to a future in which AI truly serves humanity lies in recognizing the importance of a balanced approach that combines technological progress with social responsibility.

Sources

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. NeurIPS.

Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2021). Learning to Generate Reviews and Discovering Sentiment. OpenAI.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., et al. (2020). Language Models are Few-Shot Learners. NeurIPS.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT.

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

Zeng, J., Li, Z., & Zhang, X. (2021). AI and Society: Impacts of AI on Social Structures and Inequality. AI & Society, 36(3), 645-660.