PhD defence B. (Begüm) Çeliktutan

Below is a brief summary of the dissertation: 

People often compare themselves to others, and these comparisons are often inaccurate. This PhD thesis explores the evaluation biases that arise when people assess themselves versus others, especially in the context of technology and consumption.

The research includes three main projects. First, it investigates how people recognize more of their own biases—such as those based on age, gender, race, and attractiveness—in the decisions of algorithms trained on their decisions than in their own decisions. Strikingly, people are also more likely to correct these biases in the decisions of algorithms than their own decisions, offering a novel way to reduce discrimination and improve decision-making.

Second, the thesis reveals that consumers consistently underestimate how much they buy products compared to others. This misperception affects how they respond to loyalty programs and whether they engage in sustainable consumption. Practical strategies are offered to help consumers benefit more from such programs and to encourage them to adopt more sustainable conssumption behaviors.

Third, this research uncovers a bias in how people evaluate their own and others’ contributions when collaborating with Generative AI (GenAI). People believe they contribute more than others when they collaborate with GenAI, leading them to judge their own use of GenAI as more acceptable than others’. These findings can help policymakers, educators, and businesses manage the challenges posed by emerging technologies.

Overall, this dissertation contributes new insights into self-other biases and suggests actionable ways to improve consumer decision-making and societal well-being.