Type

Status

in process

Tutor

• M.Sc. Luca Gemballa

Abstract

Artificial Intelligence (AI) is being increasingly utilized in various domains. The high performance of deep learning models in tasks such as prediction and classification has largely facilitated this trend. However, this evolution of models becoming increasingly complex has led to a problem of low model understandability, and thus a lack of trust, concerns about possible biases, and even potential regulatory obstacles. This can become a problem when marketers use AI to optimize their campaigns, e.g., by asking it for feedback on the effectiveness of their product branding or by applying AI to guide their resource allocation strategy. Using AI responsibly in this context requires understanding how the respective model reaches its conclusions. Which input features have a positive effect on the predicted buying power of potential customers? How large would the predicted size of the target population be under slightly different circumstances? Explainable AI (XAI) provides a range of methods to enhance model interpretability and promote understanding, enabling answers to these and related questions. To make better use of the available methods, research calls for the development of interactive systems that support a variety of follow-up and drill-down actions. Interactivity is designed to make explanations more human-centric, enabling users to engage in a dialogue with the AI system. 

This thesis project reviews existing XAI methods for applications in the marketing domain in a systematic literature review (SLR). Based on these insights and interviews to elicit requirements for the project, the student develops an AI-based interactive system for marketing data that utilizes a selection of XAI methods found in the SLR. The student evaluates their developed system by conducting a series of expert interviews. These will be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).