Digital Manipulation & Bot Detection (App development)

This project investigates how automated accounts, coordinated networks, and algorithmic amplification shape online public discourse.

Key components include:

• Identifying bot-like behavior on social media platforms.

• Mapping clusters and networks of coordinated activity.

• Studying how automation can distort political debate.

• Analyzing the role of algorithms in amplifying synthetic influence.

• Developing analytical methods that can support journalists and civil society organizations.

The long-term goal is to contribute to more transparent and trustworthy digital environments.

Socioeconomic Conditions and Public Health — HIV Case Study (Current Research Project)

This project examines how social and economic conditions are associated with health outcomes, using HIV incidence as a case study.

Using publicly available data, the project explores:

• How poverty, housing conditions, and employment instability relate to patterns of HIV incidence and related mortality.

• The role of education, migration, and access to healthcare in shaping vulnerability to infection.

• How structural inequalities influence both exposure to disease and access to care.

Rather than treating HIV purely as a biomedical issue, this project frames it as a social and structural health problem, where inequality plays a central role.

Findings from this work aim to inform public health discussions, community organizations, and policy debates about more equitable approaches to prevention and care.

Comparative Welfare Systems: Colombia vs Canada (Exploratory Project)

This project examines how different social, economic, and political systems shape wellbeing, opportunity, and inequality in Colombia and Canada. Rather than assuming that one model is superior, ARCAICO approaches this comparison through empirical data, historical context, and lived social realities.

Using publicly available datasets, geospatial analysis, and policy review, the project seeks to understand how welfare arrangements influence everyday life across both countries.

Key research questions include:

• How do healthcare systems differ in access, quality, and equity between Colombia and Canada?

• How do education systems affect social mobility, employment, and long-term opportunity?

• How do housing markets, urban planning, and social safety nets shape inequality?

• What role do labor markets, informality, and gig work play in economic security?

• How do race, class, and migration intersect with access to public services?

• Which policies appear to reduce vulnerability, and which unintentionally deepen disparities?

Methodologically, the project combines:

• Cross-national statistical comparisons

• Longitudinal trends over time

• Regional case studies (e.g., urban vs rural, wealthy vs marginalized communities)

• Visualization of inequality through maps, dashboards, and interactive tools

The ultimate goal is not to declare a “winner,” but to generate evidence-based insights that can inform more just and effective social policy in both contexts — and contribute to broader global debates about welfare, fairness, and public responsibility.

Water Quality, Minerals, and Kidney Health (Exploratory Project)

This emerging project investigates possible relationships between drinking water composition, mineral content, and the incidence of kidney stones across different regions.

Using environmental and health datasets, the study seeks to:

• Compare water mineral profiles across municipalities.

• Examine correlations with reported kidney stone cases.

• Explore how public water treatment processes may influence health outcomes.

• Evaluate educations sets and government programs help or simps those health outcomes.

• Identify regions where environmental factors may increase health risks.

This project reflects ARCAICO’s broader commitment to connecting environmental data with real public health impacts.

Evaluating Bias and Social Impact in Artificial Intelligence Models (Exploratory Project)

This project examines how artificial intelligence systems, including conversational models, generative tools, and recommendation algorithms, can reproduce or amplify social, cultural, and political biases.

Using a combination of qualitative analysis, structured testing, and data-driven evaluation, the project seeks to:

• Assess how AI models respond differently to questions related to gender, race, class, nationality, and political ideology.

• Identify patterns of implicit bias in language generation, image creation, and decision-making systems.

• Compare outputs across different AI platforms (such as ChatGPT, Gemini, DeepSekk, Claude and other models).

• Study how training data and internet based data, moderation policies, and algorithmic design shape model behavior.

• Explore how users from different cultural and social backgrounds experience AI tools differently.

Rather than treating AI as neutral technology, this project frames it as a social actor that can influence public opinion, knowledge production, and access to information.

The long-term goal is to contribute to more transparent, accountable, and socially responsible AI systems by providing evidence-based analysis that can inform developers, policymakers, and the public.