Search

Radboud University, 17 februari 2023, Promotores : Weitzel, G.U., Sanfey, A.G., Sickmann, J., Contains fulltext : 289481.pdf (Publisher’s version ) (Open Access)

Radboud University, 17 februari 2023, Promotores : Weitzel, G.U., Sanfey, A.G., Sickmann, J., Contains fulltext : 289481.pdf (Publisher’s version ) (Open Access)

Item does not contain fulltext, Algorithmic decision-making systems are becoming increasingly prominent in the mobility sector through navigation systems, autonomous driving vehicles, infrastructure management and even through their implementation in customer services. However, the advancements in mobility will only be successful if they are accepted and adopted by the majority of the public. In this paper, we test the perception of public towards algorithmic decision-making systems and their willingness to delegate the task within the mobility sector using a factorial survey approach. Unlike the standard one-factor-at-a-time survey analysis, factorial survey gives us an opportunity to test the perception of trust through various dimensions including personality, task and algorithm related factors, spread over different levels. For example, each participant is given a series of scenarios consisting of a combination of dimensions; with every new scenario in the series, the levels of the dimensions are changed. This allows us to reduce internal biases of the participants by affiliating them to the scenario and thus increasing the internal and external validity of our results. Our results indicate that consumers are less algorithm averse when they have more information about the algorithm (increased transparency), when they have some control over the algorithm, when the algorithm has higher accuracy in performing the task and when it is characterized by the ability to learn. Our findings could act as a starting point for a discussion on ways in which consumer trust in algorithmic decision-making systems can be improved.

18 augustus 2022, Contains fulltext : 252826.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 232109.pdf (Publisher’s version ) (Open Access), Algorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectively avoid algorithmic systems. The mechanisms that give rise to these individual differences are currently poorly understood. Previous studies have suggested two possible effects that may underlie this variability: users may differ in their predictions of the efficacy of algorithmic systems, and/or in the relative thresholds they hold to place trust in these systems. Based on a novel judgment task with a large number of within-subject repetitions, here we report evidence that both mechanisms exert an effect on experimental participant’s degree of algorithm adherence, but, importantly, that these two mechanisms are independent from each-other. Furthermore, participants are more likely to place their trust in an algorithmically managed fund if their first exposure to the task was with an algorithmic manager. These findings open the door for future research into the mechanisms driving individual differences in algorithm adherence, and allow for novel interventions to increase adherence to algorithms.

Contains fulltext : 232109.pdf (Publisher’s version ) (Open Access), Algorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectively avoid algorithmic systems. The mechanisms that give rise to these individual differences are currently poorly understood. Previous studies have suggested two possible effects that may underlie this variability: users may differ in their predictions of the efficacy of algorithmic systems, and/or in the relative thresholds they hold to place trust in these systems. Based on a novel judgment task with a large number of within-subject repetitions, here we report evidence that both mechanisms exert an effect on experimental participant’s degree of algorithm adherence, but, importantly, that these two mechanisms are independent from each-other. Furthermore, participants are more likely to place their trust in an algorithmically managed fund if their first exposure to the task was with an algorithmic manager. These findings open the door for future research into the mechanisms driving individual differences in algorithm adherence, and allow for novel interventions to increase adherence to algorithms.

Contains fulltext : 232109.pdf (Publisher’s version ) (Open Access), Algorithms play an increasingly ubiquitous and vitally important role in modern society. However, recent findings suggest substantial individual variability in the degree to which people make use of such algorithmic systems, with some users preferring the advice of algorithms whereas others selectively avoid algorithmic systems. The mechanisms that give rise to these individual differences are currently poorly understood. Previous studies have suggested two possible effects that may underlie this variability: users may differ in their predictions of the efficacy of algorithmic systems, and/or in the relative thresholds they hold to place trust in these systems. Based on a novel judgment task with a large number of within-subject repetitions, here we report evidence that both mechanisms exert an effect on experimental participant’s degree of algorithm adherence, but, importantly, that these two mechanisms are independent from each-other. Furthermore, participants are more likely to place their trust in an algorithmically managed fund if their first exposure to the task was with an algorithmic manager. These findings open the door for future research into the mechanisms driving individual differences in algorithm adherence, and allow for novel interventions to increase adherence to algorithms.

Item does not contain fulltext

Item does not contain fulltext

Item does not contain fulltext

The municipality of Amersfoort wants to construct an endurable and sustainable eco-town in the Soesterkwartier neighbourhood, by taking future climate change into account. The impact of climate change at the location of the proposed eco-town was studied by a literature review.