Abstract
Artificial intelligence (AI) depends significantly on extensive datasets to produce insights, automate decision-making, and engage with consumers. The composition of these datasets is predominantly influenced by technologically advanced cultures, resulting in potential biases that compromise the accuracy and neutrality of AI archives. This study examines the ramifications of data imbalance, especially for emerging nations, whose cultural narratives, languages, and histories may be inadequately represented or distorted in AI systems. Participants will analyze, through discussions, how leading contributors—mainly nations with substantial technology infrastructures—formulate AI knowledge models, frequently favoring viewpoints that correspond with their cultural norms and institutional biases. This influence may lead to AI-generated results that inadequately represent different global perspectives, impacting areas such as historical records, which in turn could lead to falsification of history, scientific findings, etc. The workshop will emphasize potential solutions, such as diversifying AI training data, promoting international collaboration in AI development, and advocating for legislation that requires greater cultural inclusivity. It will also examine ethical considerations, highlighting the obligation of AI developers to guarantee equitable representation in machine learning models. By the end of the workshop, participants will acquire an enhanced comprehension of the potential biases in AI archives that favor predominant cultural viewpoints and examine methodologies to foster improved accuracy, neutrality, and inclusion in forthcoming AI systems, especially for marginalized regions.
Details
Presentation Type
Theme
KEYWORDS
ARTIFICIAL INTELLIGENCE, AI DATABASE, ONLINE JUSTICE, ARCHIVES, HISTORY, IMPARTIALITY
