On the Nature of Bias Percolation: Assessing Multiaxial Collaboration in Human-AI Systems
Workshop paper
Andi Peng, Besmira Nushi, Kori Inkpen, Emre Kiciman, Ece Kamar
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APA
Peng, A., Nushi, B., Inkpen, K., Kiciman, E., & Kamar, E. On the Nature of Bias Percolation: Assessing Multiaxial Collaboration in Human-AI Systems.
Chicago/Turabian
Peng, Andi, Besmira Nushi, Kori Inkpen, Emre Kiciman, and Ece Kamar. On the Nature of Bias Percolation: Assessing Multiaxial Collaboration in Human-AI Systems, n.d.
MLA
Peng, Andi, et al. On the Nature of Bias Percolation: Assessing Multiaxial Collaboration in Human-AI Systems.
Abstract
Because most machine learning (ML) models are trained and evaluated in isolation, we understand little regarding their impact on human decision-making in the real world. Our work studies how effective collaboration emerges from these deployed human-AI systems, particularly on tasks where not only accuracy, but also bias, metrics are paramount. We train three existing language models (Random, Bag-ofWords, and the state-of-the-art Deep Neural Network) and evaluate their performance both with and without human collaborators on a text classification task. Our preliminary findings reveal that while high-accuracy ML improves team accuracy, its impact on bias appears to be model-specific, even without an interface change. We ground these findings in cognition and HCI literature and propose directions to further unearthing the intricacies of this interaction.
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Because most machine learning (ML) models are trained and evaluated in isolation, we understand little regarding their impact on human decision-making in the real world. Our work studies how effective collaboration emerges from these deployed human-AI systems, particularly on tasks where not only accuracy, but also bias, metrics are paramount. We train three existing language models (Random, Bag-ofWords, and the state-of-the-art Deep Neural Network) and evaluate their performance both with and without human collaborators on a text classification task. Our preliminary findings reveal that while high-accuracy ML improves team accuracy, its impact on bias appears to be model-specific, even without an interface change. We ground these findings in cognition and HCI literature and propose directions to further unearthing the intricacies of this interaction.