Human-Centered AI for Migrant Integration Through LLM and RAG Optimization
The enhancement of mechanisms to protect the rights of migrants and refugees within the European Union represents a critical area for human-centered artificial intelligence (HCAI). Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/325 |
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| author | Dagoberto Castellanos-Nieves Luis García-Forte |
| author_facet | Dagoberto Castellanos-Nieves Luis García-Forte |
| author_sort | Dagoberto Castellanos-Nieves |
| collection | DOAJ |
| description | The enhancement of mechanisms to protect the rights of migrants and refugees within the European Union represents a critical area for human-centered artificial intelligence (HCAI). Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to shape technology in ways which better serve human needs, particularly for disadvantaged groups. Large language models (LLMs) and retrieval-augmented generation (RAG) offer significant potential to bridging gaps for vulnerable populations, including immigrants, refugees, and individuals with disabilities. Implementing solutions based on these technologies involves critical factors which influence the pursuit of approaches aligning with humanitarian interests. This study presents a proof of concept utilizing the open LLM model LLAMA 3 and a linguistic corpus comprising legislative, regulatory, and assistance information from various European Union agencies concerning migrants. We evaluate generative metrics, energy efficiency metrics, and metrics for assessing contextually appropriate and non-discriminatory responses. Our proposal involves the optimal tuning of key hyperparameters for LLMs and RAG through multi-criteria decision-making (MCDM) methods to ensure the solutions are fair, equitable, and non-discriminatory. The optimal configurations resulted in a 20.1% reduction in carbon emissions, along with an 11.3% decrease in the metrics associated with bias. The findings suggest that by employing the appropriate methodologies and techniques, it is feasible to implement HCAI systems based on LLMs and RAG without undermining the social integration of vulnerable populations. |
| format | Article |
| id | doaj-art-4f082f64511d4c47a3d37c0ac1238714 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4f082f64511d4c47a3d37c0ac12387142025-01-10T13:15:10ZengMDPI AGApplied Sciences2076-34172024-12-0115132510.3390/app15010325Human-Centered AI for Migrant Integration Through LLM and RAG OptimizationDagoberto Castellanos-Nieves0Luis García-Forte1Computer and Systems Engineering Department, University of La Laguna, 38200 San Cristóbal de La Laguna, SpainComputer and Systems Engineering Department, University of La Laguna, 38200 San Cristóbal de La Laguna, SpainThe enhancement of mechanisms to protect the rights of migrants and refugees within the European Union represents a critical area for human-centered artificial intelligence (HCAI). Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to shape technology in ways which better serve human needs, particularly for disadvantaged groups. Large language models (LLMs) and retrieval-augmented generation (RAG) offer significant potential to bridging gaps for vulnerable populations, including immigrants, refugees, and individuals with disabilities. Implementing solutions based on these technologies involves critical factors which influence the pursuit of approaches aligning with humanitarian interests. This study presents a proof of concept utilizing the open LLM model LLAMA 3 and a linguistic corpus comprising legislative, regulatory, and assistance information from various European Union agencies concerning migrants. We evaluate generative metrics, energy efficiency metrics, and metrics for assessing contextually appropriate and non-discriminatory responses. Our proposal involves the optimal tuning of key hyperparameters for LLMs and RAG through multi-criteria decision-making (MCDM) methods to ensure the solutions are fair, equitable, and non-discriminatory. The optimal configurations resulted in a 20.1% reduction in carbon emissions, along with an 11.3% decrease in the metrics associated with bias. The findings suggest that by employing the appropriate methodologies and techniques, it is feasible to implement HCAI systems based on LLMs and RAG without undermining the social integration of vulnerable populations.https://www.mdpi.com/2076-3417/15/1/325HCAIGreen AIenergy efficiencycarbon dioxide equivalentsocial biasethics |
| spellingShingle | Dagoberto Castellanos-Nieves Luis García-Forte Human-Centered AI for Migrant Integration Through LLM and RAG Optimization Applied Sciences HCAI Green AI energy efficiency carbon dioxide equivalent social bias ethics |
| title | Human-Centered AI for Migrant Integration Through LLM and RAG Optimization |
| title_full | Human-Centered AI for Migrant Integration Through LLM and RAG Optimization |
| title_fullStr | Human-Centered AI for Migrant Integration Through LLM and RAG Optimization |
| title_full_unstemmed | Human-Centered AI for Migrant Integration Through LLM and RAG Optimization |
| title_short | Human-Centered AI for Migrant Integration Through LLM and RAG Optimization |
| title_sort | human centered ai for migrant integration through llm and rag optimization |
| topic | HCAI Green AI energy efficiency carbon dioxide equivalent social bias ethics |
| url | https://www.mdpi.com/2076-3417/15/1/325 |
| work_keys_str_mv | AT dagobertocastellanosnieves humancenteredaiformigrantintegrationthroughllmandragoptimization AT luisgarciaforte humancenteredaiformigrantintegrationthroughllmandragoptimization |