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Book chapter(2024)
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H. Torkamaan, S.N.R. Buijsman, Mohammad Tahaei, Ziang Xiao, Daricia Wilkinson, Bart P. Knijnenburg
This chapter explores the principles and frameworks of human-centered artificial intelligence (AI), specifically focusing on user modeling, adaptation, and personalization. It introduces a four-dimensional framework comprising paradigms, actors, values, and levels of realization that should be considered in the design of human-centered AI systems. This framework highlights a perspective-taking approach with four lenses of technology-centric, user-centric, human-centric, and future-centric perspectives. Ethical considerations, transparency, fairness, and accountability, among other aspects, are highlighted as values when developing and deploying AI systems. The chapter further discusses the corresponding human values for each of these concepts. Opportunities and challenges in human-centered AI are examined, including the need for interdisciplinary collaboration and the complexity of addressing diverse perspectives. Human-centered AI provides valuable insights for designing AI systems that prioritize human needs, values, and experiences while considering ethical and societal implications.
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This chapter explores the principles and frameworks of human-centered artificial intelligence (AI), specifically focusing on user modeling, adaptation, and personalization. It introduces a four-dimensional framework comprising paradigms, actors, values, and levels of realization that should be considered in the design of human-centered AI systems. This framework highlights a perspective-taking approach with four lenses of technology-centric, user-centric, human-centric, and future-centric perspectives. Ethical considerations, transparency, fairness, and accountability, among other aspects, are highlighted as values when developing and deploying AI systems. The chapter further discusses the corresponding human values for each of these concepts. Opportunities and challenges in human-centered AI are examined, including the need for interdisciplinary collaboration and the complexity of addressing diverse perspectives. Human-centered AI provides valuable insights for designing AI systems that prioritize human needs, values, and experiences while considering ethical and societal implications.
Personalized content provided by recommender systems is an integral part of the current online news reading experience. However, news recommender systems are criticized for their'black-box' approach to data collection and processing, and for their lack of explainability and transparency. This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations. The paper makes a first step toward consolidating epistemic values of news providers and news readers. We present an evaluation of an explanation interface reflecting these values, and find that providing users with different goals for self-actualization (i.e., Broaden Horizons vs. Discover the Unexplored) influences their reading intentions for news recommendations.
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Personalized content provided by recommender systems is an integral part of the current online news reading experience. However, news recommender systems are criticized for their'black-box' approach to data collection and processing, and for their lack of explainability and transparency. This paper focuses on explaining user profiles constructed from aggregated reading behavior data, used to provide content-based recommendations. The paper makes a first step toward consolidating epistemic values of news providers and news readers. We present an evaluation of an explanation interface reflecting these values, and find that providing users with different goals for self-actualization (i.e., Broaden Horizons vs. Discover the Unexplored) influences their reading intentions for news recommendations.
