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1. The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models

   https://doi.org/10.1177/17456916231180099  

   Osborne, Merrick_R; Omrani, Ali; Dehghani, Morteza (July 2023, Perspectives on Psychological Science)

   Technological innovations have become a key driver of societal advancements. Nowhere is this more evident than in the field of machine learning (ML), which has developed algorithmic models that shape our decisions, behaviors, and outcomes. These tools have widespread use, in part, because they can synthesize massive amounts of data to make seemingly objective recommendations. Yet, in the past few years, the ML community has been drawing attention to the need for caution when interpreting and using these models. This is because these models are created by humans, from data generated by humans, whose psychology allows for various biases that impact how the models are developed, trained, tested, and interpreted. As psychologists, we thus face a fork in the road: Down the first path, we can continue to use these models without examining and addressing these critical flaws and rely on computer scientists to try to mitigate them. Down the second path, we can turn our expertise in bias toward this growing field, collaborating with computer scientists to reduce the models’ deleterious outcomes. This article serves to light the way down the second path by identifying how extant psychological research can help examine and curtail bias in ML models. 

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2. Biased permutative equivariant categories

   https://doi.org/10.4310/hha.2021.v23.n1.a6  

   Bangs, Kayleigh; Binegar, Skye; Kim, Young; Ormsby, Kyle; Osorno, Angélica M.; Tamas-Parris, David; Xu, Livia (January 2021, Homology, Homotopy and Applications)

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3. Batching on biased estimators

   https://doi.org/10.1109/WSC57314.2022.10015356  

   He, Shengyi; Lam, Henry (December 2022, Proceedings of the Winter Simulation Conference (WSC))
4. Entropic Uncertainty for Biased Measurements

   https://doi.org/10.1109/QCE57702.2023.00138  

   Krawec, Walter O. (September 2023, IEEE QCE)

   Entropic Uncertainty relations are powerful tools, especially in quantum cryptography. They typically bound the amount of uncertainty a third-party adversary may hold on a measurement outcome as a result of the measurement overlap. However, when the two measurement bases are biased towards one another, standard entropic uncertainty relations do not always provide optimal lower bounds on the entropy. Here, we derive a new entropic uncertainty relation, for certain quantum states, which can provide a significantly higher bound even if the two measurement bases are no longer mutually unbiased. We evaluate our bound on two different quantum cryptographic protocols, including BB84 with faulty/biased measurement devices, and show that our new bound can produce substantially higher key-rates under several scenarios when compared with prior work using standard entropic uncertainty relations. 

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5. Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics

   https://doi.org/10.1145/3664190.3672520  

   Wang, Ben; Liu, Jiqun (August 2024, ACM)

   When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues. To understand the judgments and attitude changes of users searching online, our study examined how cognitively biased users interact with algorithmically biased search engine result pages (SERPs). We designed three-query search sessions on debated topics under various bias conditions. We recruited 1,321 crowdsourcing participants and explored their attitude changes, search interactions, and the effects of confirmation bias. Three key findings emerged: 1) most attitude changes occur in the initial query of a search session; 2) Confirmation bias and result presentation on SERPs affect the number and depth of clicks in the current query and perceived familiarity with clicked results in subsequent queries; 3) The bias position also affects attitude changes of users with lower perceived openness to conflicting opinions. Our study goes beyond traditional simulation-based evaluation settings and simulated rational users, sheds light on the mixed effects of human biases and algorithmic biases in information retrieval tasks on debated topics, and can inform the design of bias-aware user models, human-centered bias mitigation techniques, and socially responsible intelligent IR systems. 

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