As our world becomes increasingly interconnected, technology continues to evolve at an unprecedented pace. One of the most intriguing advancements in recent years is the development of emotion recognition technologies [1] . These systems, powered by artificial intelligence algorithms for machine reasoning and learning, running on sophisticated hardware, can analyze human expressions, vocal tones, and body language to identify and interpret emotions [2] . While the potential applications of this technology are vast, ranging from mental health support [3] to enhancing customer experiences [4] , it also raises significant concerns about personal privacy and public safety. This article delves into the complex implications of emotion recognition technologies, exploring the delicate balance between the promise of improved public safety and the protection of individual privacy.

Even though waste pickers play a crucial role in the urban solid waste management system in developing countries, their social, familial, and labor conditions remain uncertain. In this study, we analyzed the profiles of waste pickers in Colombia using multivariate statistical methods and cluster analysis. Our findings indicate that the majority of waste pickers have only completed elementary or high school education, and most of them are not associated with any organization and belong to the subsidized health regime. We identified five profiles of waste pickers in the population. The first consists of the most experienced individuals, while the second comprises individuals with the lowest salaries. The third includes older individuals who work more hours per day. The fourth is made up of individuals who work in the informal sector, and the fifth includes individuals who work in the formal sector. Our study highlights specific actions to be taken for each profile. Improving family and household conditions, social and educational status, and job conditions can have a significant impact on their lives.