Obianuju Okeke
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Researcher
Also affiliated: Cosmos Corporation (United States) (2024)
Unknown Researcher
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Biography and Research Information
OverviewAI-generated summary
Obianuju Okeke's research focuses on the analysis of bias and fairness within artificial intelligence systems, particularly in the context of online information environments. Her work investigates how algorithms, such as those used by YouTube, can perpetuate biases related to emotion, morality, and geopolitical discourse. Okeke has published studies evaluating the emergence of collective identity using socio-computational techniques and exploring the impact of algorithmic bias on content dissemination, including specific analyses of China-Uyghur content.
Her research also touches on the computational aspects of information processing, including the adoption of parallel processing for rapid transcript generation in multimedia-rich online environments. Okeke has a h-index of 3 and has co-authored five publications with key collaborators at the University of Arkansas at Little Rock, including Billy Spann, Mert Can Çakmak, Nitin Agarwal, and Ugochukwu Onyepunuka.
Metrics
- h-index: 3
- Publications: 5
- Citations: 37
Selected Publications
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Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse (2024)
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Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content (2024)
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Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm (2023)
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Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment (2023)
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Evaluating the Emergence of Collective Identity using Socio-Computational Techniques (2023)
Collaboration Network
Top Collaborators
- Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
- Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content
- Evaluating the Emergence of Collective Identity using Socio-Computational Techniques
- Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse
- Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
- Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content
- Evaluating the Emergence of Collective Identity using Socio-Computational Techniques
- Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse
- Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
- Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content
- Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
- Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content
- Evaluating Bias and Fairness in AI: An Analysis of YouTube’s Recommendation Algorithm and its Impact on Geopolitical Discourse
- Evaluating the Emergence of Collective Identity using Socio-Computational Techniques
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