The biggest challenge of data scientists and machine learning developers is to create a multi disciplinary approach across social, economic and political perspectives. The primary reason being the objectives of national AI programs differ across nations and continents. Europe focusing on taking leadership in regulatory frameworks both for data privacy and AI applications, North American region trying to strike balance between technological innovation, regulation and economic prosperity leaning towards technology, Asia creating an insulated yet open AI framework, these macro level differences can cause AI solution providers and enablers, specially multi-national organisations, adverse challenges while operating in a global environment and yet executing locally, as the necessary outcomes of deploying AI have different, yet significant perspectives.
• So how do multi-national companies incorporate a multi-dimensional mindset in their work processes to avoid undesirable consequences?
• Analysing escalations and damage in key macro points of disagreement and disengagement from recent case studies
• Discussing outcomes while benchmarking with global AI ethics guidelines: Global Partnership on AI, OECD, European Union and United Nations
Social Scientist & Founder , AI Governance International