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1. Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information

   Jo, Changhun; Lee, Kangwook (July 2021, Proceedings of Machine Learning Research)

   Meila, Marina; Zhang, Tong (Ed.)

   Incorporating graph side information into recommender systems has been widely used to better predict ratings, but relatively few works have focused on theoretical guarantees. Ahn et al. (2018) firstly characterized the optimal sample complexity in the presence of graph side information, but the results are limited due to strict, unrealistic assumptions made on the unknown latent preference matrix and the structure of user clusters. In this work, we propose a new model in which 1) the unknown latent preference matrix can have any discrete values, and 2) users can be clustered into multiple clusters, thereby relaxing the assumptions made in prior work. Under this new model, we fully characterize the optimal sample complexity and develop a computationally-efficient algorithm that matches the optimal sample complexity. Our algorithm is robust to model errors and outperforms the existing algorithms in terms of prediction performance on both synthetic and real data. 

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2. When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation

   https://doi.org/10.1145/3611313  

   Wang, Clarice; Wang, Kathryn; Bian, Andrew Y.; Islam, Rashidul; Keya, Kamrun Naher; Foulds, James; Pan, Shimei (September 2023, ACM Transactions on Interactive Intelligent Systems)

   Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g., along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we examine the challenges which arise when humans and fair AI interact. Our results show that due to an apparent conflict between human preferences and fairness, a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using college major recommendation as a case study, we build a fair AI recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy in prediction. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans. We conducted a follow-up survey to gain additional insights into the effectiveness of various design options that can help participants to overcome their own biases. Our results suggest that making fair AI explainable is crucial for increasing its adoption in the real world. 

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3. Teaching Inclusive Design Skills with the CIDER Assumption Elicitation Technique

   https://doi.org/10.1145/3549074  

   Oleson, Alannah; Solomon, Meron; Perdriau, Christopher; Ko, Amy J. (August 2022, ACM Transactions on Computer-Human Interaction)

   Technology should be accessible and inclusive, so designers should learn to consider the needs of different users. Toward this end, we created the theoretically-grounded CIDER assumption elicitation technique, an educational analytical design evaluation method to teach inclusive design skills. CIDER ( Critique , Imagine , Design , Expand , Repeat ) helps designers recognize and respond to bias using the critical lens of assumptions about users . Through an eleven-week mixed-method case study in an interaction design course with 40 undergraduate students and follow-up interviews, we found that activities based on the CIDER technique may have helped students identify increasingly many types of design bias over time and reflect on their unconscious biases about users. The activities also had lasting impacts, encouraging some students to adopt more inclusive approaches in subsequent design work. We discuss the implications of these findings, namely that educational techniques like CIDER can help designers learn to create equitable technology designs. 

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4. Fairness via AI: Bias Reduction in Medical Information

   Dori-Hacohen, S.; Montenegro, R.; Murai, F.; Hale, S. A.; Sung, K.; Blain, M.; Edwards-Johnson, J. (January 2021, The 4th FAccTRec Workshop on Responsible Recommendation at RecSys 2021)

   Most Fairness in AI research focuses on exposing biases in AI systems. A broader lens on fairness reveals that AI can serve a greater aspiration: rooting out societal inequities from their source. Specifically, we focus on inequities in health information, and aim to reduce bias in that domain using AI. The AI algorithms under the hood of search engines and social media, many of which are based on recommender systems, have an outsized impact on the quality of medical and health information online. Therefore, embedding bias detection and reduction into these recommender systems serving up medical and health content online could have an outsized positive impact on patient outcomes and wellbeing. In this position paper, we offer the following contributions: (1) we propose a novel framework of Fairness via AI, inspired by insights from medical education, sociology and antiracism; (2) we define a new term, bisinformation, which is related to, but distinct from, misinformation, and encourage researchers to study it; (3) we propose using AI to study, detect and mitigate biased, harmful, and/or false health information that disproportionately hurts minority groups in society; and (4) we suggest several pillars and pose several open problems in order to seed inquiry inthis new space. While part (3) of this work specifically focuses on the health domain, the fundamental computer science advances and contributions stemming from research efforts in bias reduction and Fairness via AI have broad implications in all areas of society. 

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5. Fairness via AI: Bias Reduction in Medical Information

   Dori-Hacohen, S.; Montenegro, R.; Murai, F.; Hale, S. A.; Sung, K.; Blain, M.; Edwards-Johnson, J. (January 2021, The 4th FAccTRec Workshop on Responsible Recommendation at RecSys 2021)

   Most Fairness in AI research focuses on exposing biases in AI systems. A broader lens on fairness reveals that AI can serve a greater aspiration: rooting out societal inequities from their source. Specifically, we focus on inequities in health information, and aim to reduce bias in that domain using AI. The AI algorithms under the hood of search engines and social media, many of which are based on recommender systems, have an outsized impact on the quality of medical and health information online. Therefore, embedding bias detection and reduction into these recommender systems serving up medical and health content online could have an outsized positive impact on patient outcomes and wellbeing. In this position paper, we offer the following contributions: (1) we propose a novel framework of Fairness via AI, inspired by insights from medical education, sociology and antiracism; (2) we define a new term, bisinformation, which is related to, but distinct from, misinformation, and encourage researchers to study it; (3) we propose using AI to study, detect and mitigate biased, harmful, and/or false health information that disproportionately hurts minority groups in society; and (4) we suggest several pillars and pose several open problems in order to seed inquiry inthis new space. While part (3) of this work specifically focuses on the health domain, the fundamental computer science advances and contributions stemming from research efforts in bias reduction and Fairness via AI have broad implications in all areas of society. 

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