{"id":10,"date":"2025-08-01T21:21:25","date_gmt":"2025-08-01T21:21:25","guid":{"rendered":"http:\/\/vargas-solar.com\/seeds\/?page_id=10"},"modified":"2025-08-25T18:01:08","modified_gmt":"2025-08-25T18:01:08","slug":"content","status":"publish","type":"page","link":"http:\/\/vargas-solar.com\/seeds\/content\/","title":{"rendered":"CONTENT"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\"><strong>1. Ethical Foundations<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Definitions of ethics in data science<\/li>\n\n\n\n<li>Transparency: explainable models and open pipelines<\/li>\n\n\n\n<li>Accountability: auditability, reproducibility, and traceability<\/li>\n\n\n\n<li>Bias mitigation principles (procedural, distributive, and intersectional fairness)<\/li>\n\n\n\n<li>Real-world harms: predictive policing, credit scoring, and health algorithm bias<\/li>\n\n\n\n<li>Responsible Stack\n<ul class=\"wp-block-list\">\n<li><strong>Data Layer<\/strong>: provenance, consent, inclusion in data collection (feminist principles)<\/li>\n\n\n\n<li><strong>Storage Layer<\/strong>: energy cost, cloud jurisdiction, green infrastructure<\/li>\n\n\n\n<li><strong>Processing Layer<\/strong>: low-energy compute, scheduling, decentralization (the cost of connection)<\/li>\n\n\n\n<li><strong>Modeling Layer<\/strong>: fairness-aware modeling, robustness, explainability<\/li>\n\n\n\n<li><strong>Deployment Layer<\/strong>: edge vs. cloud tradeoffs, latency vs. sovereignty (accessibility and digital sovereignty)<\/li>\n\n\n\n<li><strong>Governance Layer<\/strong>: logging, access control, human-in-the-loop<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>2. AI &amp; Environment<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Environmental cost of training large AI models (GPT, BERT, etc.)<\/li>\n\n\n\n<li>Cloud computing and water\/electricity consumption<\/li>\n\n\n\n<li>Carbon emissions tracking (tools: CodeCarbon, Green Algorithms)<\/li>\n\n\n\n<li>Carbon-aware scheduling and green load balancing<\/li>\n\n\n\n<li>Sustainable DS pipeline design strategies<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>3. Data Sovereignty<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Digital colonialism and extractivist data practices<\/li>\n\n\n\n<li>Legal and regulatory frameworks (GDPR, Indigenous Data Sovereignty protocols)\n<ul class=\"wp-block-list\">\n<li>GDPR: data minimization, portability, consent<\/li>\n\n\n\n<li>EU AI Act: risk-based classification and governance<\/li>\n\n\n\n<li>UNESCO AI Ethics: proportionality, inclusiveness, sustainability<\/li>\n\n\n\n<li>OECD AI Principles: transparency, robustness, accountability<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Rights-based frameworks and Indigenous protocols (e.g., OCAP)<\/li>\n\n\n\n<li>Community ownership and participatory governance<\/li>\n\n\n\n<li>Hybrid sovereign clouds and data localization policies<\/li>\n\n\n\n<li>Culturally-sensitive and respectful data collection protocols<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>4. Fairness &amp; Bias<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sources of bias: historical, representational, measurement<\/li>\n\n\n\n<li>Fairness metrics:\n<ul class=\"wp-block-list\">\n<li>Demographic Parity<\/li>\n\n\n\n<li>Equal Opportunity \/ Equalized Odds<\/li>\n\n\n\n<li>Predictive Parity<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Algorithmic auditing tools and techniques<\/li>\n\n\n\n<li>Inclusive dataset design and rebalancing strategies<\/li>\n\n\n\n<li>Bias-variance-fairness trade-offs in model evaluation<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>5. Feminist &amp; Decolonial Data Science<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Feminist critiques of objectivity and \u201cneutrality\u201d in tech<\/li>\n\n\n\n<li>Technopolitical critiques of extractive data infrastructures<\/li>\n\n\n\n<li>Data feminism principles (e.g., make power visible, consider context)<\/li>\n\n\n\n<li>Epistemic violence and knowledge representation gaps<\/li>\n\n\n\n<li>Feminist infrastructure and makerspaces as alternatives\n<ul class=\"wp-block-list\">\n<li>Trade-offs in DS design: accuracy vs. fairness vs. energy use<\/li>\n\n\n\n<li>Pareto efficiency and fairness frontiers<\/li>\n\n\n\n<li>Composite scoring metrics (e.g., weighted sum models)<\/li>\n\n\n\n<li>Fair resource dispatching in cloud systems (Dominant Resource Fairness)<\/li>\n\n\n\n<li>Scheduling for diversity, sustainability, and transparency<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Decolonial methodologies and care-based design<\/li>\n<\/ul>\n\n\n\n<p><strong>6. Conclusions and Open Challenges<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1. Ethical Foundations 2. AI &amp; Environment 3. Data Sovereignty 4. Fairness &amp; Bias 5. Feminist &amp; Decolonial Data Science 6. Conclusions and Open Challenges<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"class_list":["post-10","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/vargas-solar.com\/seeds\/wp-json\/wp\/v2\/pages\/10","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/vargas-solar.com\/seeds\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/vargas-solar.com\/seeds\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/seeds\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/seeds\/wp-json\/wp\/v2\/comments?post=10"}],"version-history":[{"count":6,"href":"http:\/\/vargas-solar.com\/seeds\/wp-json\/wp\/v2\/pages\/10\/revisions"}],"predecessor-version":[{"id":36,"href":"http:\/\/vargas-solar.com\/seeds\/wp-json\/wp\/v2\/pages\/10\/revisions\/36"}],"wp:attachment":[{"href":"http:\/\/vargas-solar.com\/seeds\/wp-json\/wp\/v2\/media?parent=10"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}