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1. Strategic Adversarial Attacks in AI-assisted Decision Making to Reduce Human Trust and Reliance.

   Lu, Zhuoran; Li, Zhuoyan; Chiang, Chun-Wei; Yin, Ming (August 2023, Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23))

   With the increased integration of AI technologies in human decision making processes, adversarial attacks on AI models become a greater concern than ever before as they may significantly hurt humans’ trust in AI models and decrease the effectiveness of human-AI collaboration. While many adversarial attack methods have been proposed to decrease the performance of an AI model, limited attention has been paid on understanding how these attacks will impact the human decision makers interacting with the model, and accordingly, how to strategically deploy adversarial attacks to maximize the reduction of human trust and reliance. In this paper, through a human-subject experiment, we first show that in AI-assisted decision making, the timing of the attacks largely influences how much humans decrease their trust in and reliance on AI—the decrease is particularly salient when attacks occur on decision making tasks that humans are highly confident themselves. Based on these insights, we next propose an algorithmic framework to infer the human decision maker’s hidden trust in the AI model and dynamically decide when the attacker should launch an attack to the model. Our evaluations show that following the proposed approach, attackers deploy more efficient attacks and achieve higher utility than adopting other baseline strategies. 

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2. Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary

   Li, Zhuoyan; Yin, Ming (December 2024, Advances in Neural Information Processing Systems 37 (NeurIPS 2024))

   Recent advances in AI models have increased the integration of AI-based decision aids into the human decision making process. To fully unlock the potential of AI-assisted decision making, researchers have computationally modeled how humans incorporate AI recommendations into their final decisions, and utilized these models to improve human-AI team performance. Meanwhile, due to the black-box'' nature of AI models, providing AI explanations to human decision makers to help them rely on AI recommendations more appropriately has become a common practice. In this paper, we explore whether we can quantitatively model how humans integrate both AI recommendations and explanations into their decision process, and whether this quantitative understanding of human behavior from the learned model can be utilized to manipulate AI explanations, thereby nudging individuals towards making targeted decisions. Our extensive human experiments across various tasks demonstrate that human behavior can be easily influenced by these manipulated explanations towards targeted outcomes, regardless of the intent being adversarial or benign. Furthermore, individuals often fail to detect any anomalies in these explanations, despite their decisions being affected by them. 

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3. Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary

   Li, Zhuoyan; Yin, Ming (December 2024, The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS))

   Recent advances in AI models have increased the integration of AI-based decision aids into the human decision making process. To fully unlock the potential of AI- assisted decision making, researchers have computationally modeled how humans incorporate AI recommendations into their final decisions, and utilized these models to improve human-AI team performance. Meanwhile, due to the “black-box” nature of AI models, providing AI explanations to human decision makers to help them rely on AI recommendations more appropriately has become a common practice. In this paper, we explore whether we can quantitatively model how humans integrate both AI recommendations and explanations into their decision process, and whether this quantitative understanding of human behavior from the learned model can be utilized to manipulate AI explanations, thereby nudging individuals towards making targeted decisions. Our extensive human experiments across various tasks demonstrate that human behavior can be easily influenced by these manipulated explanations towards targeted outcomes, regardless of the intent being adversarial or benign. Furthermore, individuals often fail to detect any anomalies in these explanations, despite their decisions being affected by them. 

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4. AI-Assisted Decision-making: a Cognitive Modeling Approach to Infer Latent Reliance Strategies

   https://doi.org/10.1007/s42113-022-00157-y  

   Tejeda, Heliodoro; Kumar, Aakriti; Smyth, Padhraic; Steyvers, Mark (December 2022, Computational Brain & Behavior)

   Abstract AI assistance is readily available to humans in a variety of decision-making applications. In order to fully understand the efficacy of such joint decision-making, it is important to first understand the human’s reliance on AI. However, there is a disconnect between how joint decision-making is studied and how it is practiced in the real world. More often than not, researchers ask humans to provide independent decisions before they are shown AI assistance. This is done to make explicit the influence of AI assistance on the human’s decision. We develop a cognitive model that allows us to infer the latent reliance strategy of humans on AI assistance without asking the human to make an independent decision. We validate the model’s predictions through two behavioral experiments. The first experiment follows a concurrent paradigm where humans are shown AI assistance alongside the decision problem. The second experiment follows a sequential paradigm where humans provide an independent judgment on a decision problem before AI assistance is made available. The model’s predicted reliance strategies closely track the strategies employed by humans in the two experimental paradigms. Our model provides a principled way to infer reliance on AI-assistance and may be used to expand the scope of investigation on human-AI collaboration. 

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5. 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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