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1. How can geologic decision-making under uncertainty be improved?

   https://doi.org/10.5194/se-10-1469-2019  

   Wilson, Cristina G.; Bond, Clare E.; Shipley, Thomas F. (January 2019, Solid Earth)

   Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision-making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision-making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, the availability bias occurs when people make judgments based on what is most dominant or accessible in memory; geoscientists who have spent the past several months studying strike-slip faults will have this terrain most readily available in their mind when interpreting new seismic data. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we outline the key insights from decision-making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision-making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists' decision-making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision-making, wherein debiased decision-making is an emergent property of the coordinated and integrated processing of human–AI collaborative teams. 

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2. Toward A Two-Sided Fairness Framework in Search and Recommendation

   https://doi.org/10.1145/3576840.3578332  

   Liu, Jiqun (March 2023, CHIIR '23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval)

   As artificial intelligence (AI) assisted search and recommender systems have become ubiquitous in workplaces and everyday lives, understanding and accounting for fairness has gained increasing attention in the design and evaluation of such systems. While there is a growing body of computing research on measuring system fairness and biases associated with data and algorithms, the impact of human biases that go beyond traditional machine learning (ML) pipelines still remain understudied. In this Perspective Paper, we seek to develop a two-sided fairness framework that not only characterizes data and algorithmic biases, but also highlights the cognitive and perceptual biases that may exacerbate system biases and lead to unfair decisions. Within the framework, we also analyze the interactions between human and system biases in search and recommendation episodes. Built upon the two-sided framework, our research synthesizes intervention and intelligent nudging strategies applied in cognitive and algorithmic debiasing, and also proposes novel goals and measures for evaluating the performance of systems in addressing and proactively mitigating the risks associated with biases in data, algorithms, and bounded rationality. This paper uniquely integrates the insights regarding human biases and system biases into a cohesive framework and extends the concept of fairness from human-centered perspective. The extended fairness framework better reflects the challenges and opportunities in users’ interactions with search and recommender systems of varying modalities. Adopting the two-sided approach in information system design has the potential to enhancing both the effectiveness in online debiasing and the usefulness to boundedly rational users engaging in information-intensive decision-making. 

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3. The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets

   https://doi.org/10.24963/ijcai.2023/343  

   Wang, Xinru; Liang, Chen; Yin, Ming (August 2023, Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23))

   The use of AI-based decision aids in diverse domains has inspired many empirical investigations into how AI models’ decision recommendations impact humans’ decision accuracy in AI-assisted decision making, while explorations on the impacts on humans’ decision fairness are largely lacking despite their clear importance. In this paper, using a real-world business decision making scenario—bidding in rental housing markets—as our testbed, we present an experimental study on understanding how the bias level of the AI-based decision aid as well as the provision of AI explanations affect the fairness level of humans’ decisions, both during and after their usage of the decision aid. Our results suggest that when people are assisted by an AI-based decision aid, both the higher level of racial biases the decision aid exhibits and surprisingly, the presence of AI explanations, result in more unfair human decisions across racial groups. Moreover, these impacts are partly made through triggering humans’ “disparate interactions” with AI. However, regardless of the AI bias level and the presence of AI explanations, when people return to make independent decisions after their usage of the AI-based decision aid, their decisions no longer exhibit significant unfairness across racial groups. 

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4. Toward a Design Space for Mitigating Cognitive Bias in Vis

   https://doi.org/10.1109/VISUAL.2019.8933611  

   Wall, Emily; Stasko, John; Endert, Alex (October 2019, IEEE VIS)

   The use of cognitive heuristics often leads to fast and effective decisions. However, they can also systematically and predictably lead to errors known as cognitive biases. Strategies for minimizing or mitigating these biases, however, remain largely non-technological (e.g., training courses). The growing use of visual analytic (VA) tools for analysis and decision making enables a new class of bias mitigation strategies. In this work, we explore the ways in which the design of visualizations (vis) may be used to mitigate cognitive biases. We derive a design space comprised of 8 dimensions that can be manipulated to impact a user's cognitive and analytic processes and describe them through an example hiring scenario. This design space can be used to guide and inform future vis systems that may integrate cognitive processes more closely. 

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5. Bias Mitigation for Toxicity Detection via Sequential Decisions

   https://doi.org/10.1145/3477495.3531945  

   Cheng, L.; Mosallanezhad, A.; Silva, Y. N.; Hall, D. (January 2022, The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR))

   Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with marginalized or minority identities (e.g., females and African Americans). Established research in debiasing toxicity classifiers often (1) takes a static or batch approach, assuming that all information is available and then making a one-time decision; and (2) uses a generic strategy to mitigate different biases (e.g., gender and racial biases) that assumes the biases are independent of one another. However, in real scenarios, the input typically arrives as a sequence of comments/words over time instead of all at once. Thus, decisions based on partial information must be made while additional input is arriving. Moreover, social bias is complex by nature. Each type of bias is defined within its unique context, which, consistent with intersectionality theory within the social sciences, might be correlated with the contexts of other forms of bias. In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. In particular, we study debiasing toxicity detection with two aims: (1) to examine whether different biases tend to correlate with each other; and (2) to investigate how to jointly mitigate these correlated biases in an interactive manner to minimize the total amount of bias. At the core of our approach is a framework built upon theories of sequential Markov Decision Processes that seeks to maximize the prediction accuracy and minimize the bias measures tailored to individual biases. Evaluations on two benchmark datasets empirically validate the hypothesis that biases tend to be correlated and corroborate the effectiveness of the proposed sequential debiasing strategy. 

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