{"id":{"repo_id":"guelph","oai_identifier":"oai:atrium.lib.uoguelph.ca:10214/29044"},"canonical_url":"https://search.dev.ndltd.org/etd/guelph/oai:atrium.lib.uoguelph.ca:10214/29044","repository":{"repo_id":"guelph","name":"University of Guelph","base_url":"https://atrium.lib.uoguelph.ca/server/oai/request"},"display":{"title":"Back to the building blocks: Making forecasting more context aware through human and data-centric practices","abstract":"The rise of artificial intelligence (AI) and machine learning (ML) presents substantial opportunities for high-stakes fields like health and public policy. While model-centric AI, focusing on algorithmic improvements and architectural innovations, has historically dominated the field, growing calls for more responsible AI have led to the emergence of two complementary paradigms:(1) human-centred AI (HCAI), which emphasizes user-centred design and collaboration with domain experts, and (2) data-centric AI, which focuses on data quality, selection, and curation. This thesis examines the interdependence of these approaches through the DelphAI framework, a sociotechnical methodology that translates expert engagement into data-centric techniques to improve forecasting and address challenges related to responsibility and impact. The rise of artificial intelligence (AI) and machine learning (ML) presents substantial opportunities for high-stakes fields like health and public policy. In response to calls for more responsible AI, three major development paradigms have gained prominence: (1) model-centric AI, which focuses on optimizing architectures, algorithms, and training procedures; (2) data-centric AI, which emphasizes the quality, relevance, and curation of training data; and (3) human-centred AI (HCAI), which promotes collaboration with domain experts and alignment with user needs. This thesis examines the interdependence of these paradigms through the DelphAI framework, a sociotechnical methodology that translates expert engagement into data-centric and model-informed forecasting strategies aimed at improving impact, trust, and deployment in real-world systems.","abstract_html":"The rise of artificial intelligence (AI) and machine learning (ML) presents substantial opportunities for high-stakes fields like health and public policy. While model-centric AI, focusing on algorithmic improvements and architectural innovations, has historically dominated the field, growing calls for more responsible AI have led to the emergence of two complementary paradigms:(1) human-centred AI (HCAI), which emphasizes user-centred design and collaboration with domain experts, and (2) data-centric AI, which focuses on data quality, selection, and curation. This thesis examines the interdependence of these approaches through the DelphAI framework, a sociotechnical methodology that translates expert engagement into data-centric techniques to improve forecasting and address challenges related to responsibility and impact. The rise of artificial intelligence (AI) and machine learning (ML) presents substantial opportunities for high-stakes fields like health and public policy. In response to calls for more responsible AI, three major development paradigms have gained prominence: (1) model-centric AI, which focuses on optimizing architectures, algorithms, and training procedures; (2) data-centric AI, which emphasizes the quality, relevance, and curation of training data; and (3) human-centred AI (HCAI), which promotes collaboration with domain experts and alignment with user needs. 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In response to calls for more responsible AI, three major development paradigms have gained prominence: (1) model-centric AI, which focuses on optimizing architectures, algorithms, and training procedures; (2) data-centric AI, which emphasizes the quality, relevance, and curation of training data; and (3) human-centred AI (HCAI), which promotes collaboration with domain experts and alignment with user needs. 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