# Human-Centered AI for Migrant Integration Through LLM and RAG Optimization
## 6 Results and Discussion
### Results
Regarding neutrality, the score declined from 0.804 to 0.668 with increasing chunk sizes, implying a tendency toward more polarized and less objective responses. For the Non_Hate and Hate metrics, the Non_Hate score decreased from 0.931 to 0.818, reflecting a reduced effectiveness in generating non-offensive responses, while the Hate score dropped from 0.260 to 0.090, which could be interpreted as a partial mitigation of offensive content in responses generated with larger chunks (Figure 5). As the value of K increased, the stereotype score rose from 0.933 at K = 1 to 0.989 at K = 10. In contrast, the anti-stereotype score decreased from 0.886 at K = 1 to 0.711 at K = 10. Similarly, neutrality declined as K increased, dropping from 0.778 at K = 1 to 0.553 at K = 10, indicating a shift toward less neutral and more polarized responses. The effectiveness in generating non-offensive responses, as indicated by the Non_Hate score, also decreased from 0.949 at K = 1 to 0.754 at K = 10. Additionally, the Hate score showed an increase from 0.084 at K = 1 to 0.218 at K = 10, suggesting that as the number of neighbors increases, the model may capture more offensive content present in the data. Figure 4: Comparison of average scores for the stereotype, anti-stereotype, neutral, Non_Hate, and Hate categories as a function of the temperature. Figure 5: Trends in stereotype, anti-stereotype, neutral, Non_Hate, and Hate scores as chunk size increased. The analysis of the various metrics used in the experimentation indicates that selecting the optimal or acceptable alternative requires a rigorous evaluation of the different criteria involved. To provide an appropriate solution, MCDM methods are employed, which facilitate clear and concise structuring of the decision problem, thereby simplifying its analysis. This is in line with the hypothesis that the integration of MCDM methods will enhance the effectiveness and ethical alignment of AI systems, ensuring that they produce fair and contextually appropriate responses, particularly for vulnerable populations such as migrants and refugees. The RIM and TOPSIS and VIKOR methods, which were previously detailed in earlier sections of this article, were utilized. These MCDM techniques offer effective tools for developing RAG systems based on the principles of HCAL Therefore, the application of these methods provides significant support for solutions similar to those proposed in this PoC. The results of the analysis of the 125 alternatives using the RIM and TOPSIS and VIKOR methods are presented in Figure 6. Table 3 shows the top 10 ranked alternatives for each of the methods [72]. A high correlation between the results was not observed across the methods. This divergence was primarily due to the differences in the underlying mathematical models and the philosophical approaches of each method. While TOPSIS and RIM focus on the proximity to ideal or reference solutions, VIKOR emphasizes the trade-offs between criteria, which results in variations in the prioritization of alternatives. The sensitivity of each method to the weighing of the criteria contributes to these discrepancies, particularly in contexts where there is significant heterogeneity among the alternatives. The results of the process integration indicate a significant reduction in bias. The median value for stereotypes was 0.923, while the median for anti-stereotypes was 0.926, suggesting a balanced distribution (see Figure 7). Furthermore, there was an observed increase in neutral responses, with a median of 0.853. Regarding hate speech, the median for Hate was 0.756, indicating a lower generation of offensive content, whereas the values for Non_Hate were high, with a median of 0.945, demonstrating the effectiveness of the implemented mitigation techniques (see dataset [72]). The integration of processes presents certain weaknesses, such as lower similarity values and reduced BLEU scores, which suggest diminished precision and quality in the responses.