The effect of individual differences and task context on trust in AI-based algorithms

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2021-09-14
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en
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Abstract
Since algorithms have already covered many areas of social life and are being globally used by large corporations, such as Amazon, Netflix, Spotify, Linked In, and many others, the question of whether people are inclined to trust algorithmic recommendations is highly relevant. This Thesis looks at participants’ attitudes to algorithmic advice on a number of decisions concerning different areas of life. The main goal is to explore factors that influence people’s response to algorithmic advice. Based on previous research, the Thesis focuses on individual characteristics and task context as the factors potentially influencing trust. The core of this research is a survey composed of general socio-demographic questions, specific trust questions, and the Big Five personality questions. Regression analyses show that individual differences do not affect overall users trust in algorithms, but in some cases, personality dimensions can predict trust in specific algorithms. The t-test reveals that in the case of objective decision tasks, the level of trust in algorithmic advice is higher than in subjective tasks.
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Faculteit der Managementwetenschappen