¿Cual es la novedad del trabajo que aparece en este texto ? el texto es el siguiente: "\section{Introduction}
\label{sec:Intro}
The protection of the rights of migrants and refugees within the European Union is an area of significance for Human-Centered Artificial Intelligence (HCAI). We are in an era of creating tools to amplify the human mind without compromising human values \cite{schmidt2020interactive}. Currently, society is shifting its focus towards a more people-centric perspective rather than solely on algorithms. HCAI is deemed suitable as it views technology primarily as a tool to empower, augment, and enhance human agency, aligning with ethics and the science of human flourishing \cite{Dshneiderman2020human}.  

Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) are technologies that serve to bridge gaps for vulnerable populations, such as immigrants, refugees, or individuals with disabilities \cite{naveed2023comprehensive}. When combined with Human-Centered Artificial Intelligence (HCAI), these models can become even more effective. HCAI focuses on human interaction and decision-making, which can benefit from the high-quality text generation of LLMs and the precision of RAGs \cite{tahaei2023systematic, zhao2024retrieval}. Together, they can provide a richer, more human-centered user experience. When implementing solutions based on these technologies, it is crucial to consider critical factors to ensure that the approaches align with the interests of individuals \cite{schmager2023defining}. Therefore, it is not only necessary to identify and optimize technical criteria or metrics but also to consider those within the human and social domain.

Proposals are required to identify and mitigate biases, hate, and stereotypes in computational models to ensure that responses and recommendations are fair and non-discriminatory \cite{mehrabi2021survey}. It is essential to ensure that these technologies do not perpetuate existing biases or harmful attitudes but rather actively contribute to reducing disparities and avoiding the reinforcement of stereotypes. Such tools should empower users by facilitating autonomous understanding and effective use of information for decision-making, eliminating the need for intermediaries. Furthermore, it is critical for these technologies to uphold the principle of valuing vulnerable individuals equitably, thereby avoiding harmful stereotypes. This requirement should be substantiated by experimental findings, allowing for a rigorous assessment of the quality of assistance or responses generated by newly developed or deployed systems in production environments \cite{holstein2019improving}. Optimizing metrics related to bias, hate, and stereotypes alongside performance metrics such as CO\textsubscript{2}e emissions presents complex challenges. More sophisticated models designed to reduce biases can increase energy consumption, adversely affecting CO\textsubscript{2}e metrics \cite{bender2021dangers, mitchell2019model}. Furthermore, addressing these issues requires diverse and high-quality data, which increases complexity, costs, and processing time. Bias and hate metrics are qualitative and contextual, complicating their evaluation in comparison to quantitative metrics. Innovation should focus on methods that balance these objectives through efficient algorithms or hyperparameter optimization that reduce resource usage and carbon footprint while mitigating biases. The aim is to achieve technological advancements without compromising either equity or environmental sustainability in the software systems being developed \cite{castellanos2024strategies, castellanos2023improving}.

The inclusion of Multi-Criteria Decision-Making (MCDM) methods is crucial for identifying optimal solutions according to decision-makers, particularly in contexts involving advanced technologies such as Large Language Models and Retrieval-Augmented Generation. These methods enable the simultaneous evaluation of multiple criteria, integrating technical, human, and social aspects, which is essential for ensuring that the solutions adopted are best suited to the needs of vulnerable populations, such as immigrants or individuals with disabilities \cite{mardani2015multiple}. Through the application of MCDM, decision-makers can weigh and prioritize different factors, such as equity, usability, and social impact \cite{ishizaka2011review}, ensuring that technologies are not only technically effective but also promote a fair and inclusive approach. Thus, MCDM serves as a critical tool for aligning technological capabilities with human values and priorities, facilitating decisions that genuinely benefit people in vulnerable situations \cite{aruldoss2013survey}.

The identification of stereotypes and hate speech commonly employs natural language processing (NLP) techniques and machine learning. Fortuna and Nunes \cite{fortuna2018survey} conducted a comprehensive review of methods for the automatic detection of hate speech, emphasizing the role of sentiment analysis. Nobata et al. \cite{nobata2016abusive} demonstrated the effectiveness of N-grams and Bag-of-Words approaches in identifying abusive language within online content. Davidson et al. \cite{davidson2017automated} explored syntactic and semantic features, highlighting the challenges associated with offensive language. Badjatiya et al. \cite{badjatiya2017deep} applied deep learning techniques to detect hate speech on social media platforms, achieving promising results. Warner and Hirschberg \cite{warner2012detecting} employed predefined lists of offensive words and specific linguistic patterns for the identification of hate speech on the web, underscoring the utility of dictionaries in text classification. Furthermore, solutions exploring the use of LLM systems with RAG to mitigate bias and stereotypes are being developed. Gehman et al. \cite{gehman2020realtoxicityprompts} emphasize modifying retrieved data and generation parameters to minimize toxicity and stereotypes in responses. Dinan et al. \cite{dinan2020multi} propose a retrieval and generation model that detects and alters text fragments in real-time to prevent perpetuating stereotypes. Liang et al. \cite{liang2020towards} present filtered retrieval and embedding modification techniques to reduce biased content inclusion in RAG systems. In the presented proposal, LLM techniques with RAG are employed, optimizing hyperparameters and their selection with MCDM, which represents a potentially significant contribution.


In this study, we conducted a proof of concept using the open language model LLAMA 3.0 (Large Language Model Meta AI) and a linguistic corpus containing legislative, regulatory, and assistance information from various agencies in the European space related to immigrants \cite{dubey2024llama}. The proposal involves the optimization of the hyperparameters of both the LLM and RAG. Metrics such as ROUGE  (Recall-Oriented Understudy for Gisting Evaluation) Score, perplexity, BLEU (Bilingual Evaluation Understudy) Score, hate bias, stereotypes, and carbon footprint are analyzed to evaluate contextually appropriate responses \cite{lin2004looking, papineni2002bleu, dhamala2021bold, zekun2023towards, castellanos2023improving, castellanos2024strategies}. This framework is designed to enhance Human-Centered Artificial Intelligence systems, leveraging both LLM and RAG models, with the ultimate goal of promoting the integration of the most vulnerable individuals into society through the use of AI.


The hypothesis proposed in this research is that integration MCDM methods for optimizing the hyperparameters of LLMs and RAG systems will improve both the effectiveness and ethical alignment of HCAI applications. This integration is anticipated to yield fair and contextually appropriate responses tailored to the needs of vulnerable populations, such as migrants and refugees within the European Union. A comprehensive evaluation of linguistic, technical, and social metrics is expected to result in more inclusive AI systems that foster enhanced social integration.

In this paper, we are pleased to announce that we have made a series of significant contributions, which we will detail in the following sections:
\begin{itemize}
    \item A proof of concept is presented in the field of HCAI systems based on LLM and RAG models, with the aim of enhancing the integration of the most vulnerable individuals into society.
    \item We have delved into the application of technical, bias, and environmental sustainability metrics to evaluate and enhance the identification and mitigation of biases in algorithmic models, with the aim of ensuring precise, fair, and non-discriminatory responses and recommendations.
    \item A set of best practices and recommendations are defined for the development of HCAI systems supported by LLM and RAG to ensure that these technologies do not perpetuate existing biases and actively work to reduce disparities.
    \item Multicriteria decision-making methods are integrated into the proof of concept, enabling the simultaneous evaluation of multiple criteria encompassing technical, human, and social aspects.
\end{itemize}

In the following State of the Art section, we will review the current advancements and challenges in HCAI systems, LLMs, and RAG. Subsequently, in Section 3, we will analyze the main technical metrics, biases, and the optimization of model hyperparameters. In Section 4 introduces RIM, TOPSIS, and VIKOR as multi-criteria decision-making tools. The materials and methods used in our study will be described in Section 5. The results and their discussion are presented in Section 6. Finally, we will summarize our main conclusions and propose future research directions in Section 7.

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