Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Rastogi, Charvi; Zhang, Yunfeng; Wei, Dennis; Varshney, Kush R.; Dhurandhar, Amit; Tomsett, Richard

doi:10.1145/3512930

Cited by 45 publications

(33 citation statements)

References 40 publications

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“…In terms of our taxonomy, this work considers a setting where there is overall performance complementarity between the human and the AI, but the mechanisms behind this complementary performance are not well understood. In another recent work, Rastogi et al (2022) attempt to achieve complementary performance between the human and the machine by providing each with access to different task-relevant information. They also showed how training the machine on different features led to complementarity in the accuracy of the human alone versus the AI alone.…”

Section: Prior Work Through the Lens Of Our Taxonomymentioning

confidence: 99%

A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making

Rastogi¹,

Leqi²,

Holstein³

et al. 2022

Preprint

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Hybrid human-ML systems are increasingly in charge of consequential decisions in a wide range of domains. A growing body of work has advanced our understanding of these systems by providing empirical and theoretical analyses. However, existing empirical results are mixed, and theoretical proposals are often incompatible with each other. Our goal in this work is to bring much-needed organization to this field by offering a unifying framework for understanding conditions under which combining complementary strengths of human and ML leads to higher quality decisions than those produced by them individually-a state to which we refer to as human-ML complementarity. We focus specifically on the context of human-ML predictive decision-making systems and investigate optimal ways of combining human and ML-based predictive decisions, accounting for the underlying causes of variation in their judgments. Within this scope, we present two crucial contributions. First, drawing upon prior literature in human psychology, machine learning, and human-computer interaction, we introduce a taxonomy characterizing a wide variety of criteria across which human and machine decision-making differ. Building on our taxonomy, our second contribution presents a unifying optimizationbased framework for formalizing how human and ML predictive decisions should be aggregated optimally. We show that our proposed framework encompasses several existing models of human-ML complementarity as special cases. Last but not least, the exploratory analysis of our framework offers a critical piece of insight for future work in this area: the mechanism by which we combine human-ML judgments should be informed by the underlying causes of their diverging decisions. * indicates equal contribution. The ordering was decided by a coin toss.

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Section: Prior Work Through the Lens Of Our Taxonomymentioning

confidence: 99%

A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making

Rastogi¹,

Leqi²,

Holstein³

et al. 2022

Preprint

Self Cite

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“…The AI advice can be presented after some delay which provides the decision-maker additional time to reflect on the problem and improve their own decision-making accuracy (Park et al, 2019). Another possibility is to vary the amount of time available for people to process the AI prediction after it is shown making it more likely for people to detect AI errors (Rastogi et al, 2022). Overall, more research is needed to understand the effects of soliciting independent human predictions and varying the timing of the AI recommendation.…”

Section: Classifier Cmentioning

confidence: 99%

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

et al. 2022

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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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“…Text CNN text classification model based on a one-dimensional convolution structure, which first maps the text into vectors and then uses one-dimensional convolutions of different sizes to capture the local semantic information of the text and capture the local semantic information of the text through pooling. Important feature information is input to the classifier to obtain the probability distribution of the labels [7]. Gerards and Borgesius proposed a text classifier based on a deep convolutional structure.…”

Section: Et Al Proposed Amentioning

confidence: 99%

Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network

2022

Mobile Information Systems

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With the arrival of the third revolution of artificial intelligence, the applications of artificial intelligence in the fields of automatic driving, image recognition, smart home, machine translation, medical services, e-sports, and so on can be seen everywhere, and topics about artificial intelligence are constantly emerging. Since 2017, the discussion on artificial intelligence in the field of law has become more and more active. In this context, the application of artificial intelligence in the field of legal judgment and the hypothetical system based on this technology in court judgment has also become the object of discussion from time to time. In this paper, based on the artificial intelligence decision-making method of the deep neural network, aiming at the three subtasks of legal judgment prediction, namely, crime prediction, law recommendation, and sentence prediction, a multi-task judgment prediction model BERT12multi and a sentence interval prediction model BERT-Text CNN are proposed, which improve the prediction accuracy and adopt the knowledge distillation strategy to compress the model parameters and improve the reasoning speed of the judgment model. Experiments on the CAIL2018 data set show that the performance of the deep neural network model in crime prediction and law recommendation tasks can be significantly improved by adopting the pre training model adaptive training, grouping focus loss, and gradient confrontation training strategies. Using a step-by-step sentence prediction strategy can realize the weight sharing of pre training model and make use of the prediction results of charges and laws in sentence prediction. The recall training-prediction strategy can avoid error accumulation and improve the accuracy of sentence prediction. By integrating the artificial intelligence decision-making method, the case reasoning speed can be greatly improved, the highest compressible model volume can be about 11% of the original one, and the reasoning speed can be increased by about 8 times. At the same time, performance close to that of the deep neural model can be obtained, which is superior to other legal decision prediction models based on word embedding.

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Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Cited by 45 publications

References 40 publications

A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making

A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making

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

Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network

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