AI-Assisted and Explainable Hate Speech Detection for Social Media Moderators – A Design Science Approach

Abstract

To date, the detection of hate speech is still primarily carried out by humans, yet there is great potential for combining human expertise with automated approaches. However, identified challenges include low levels of agreement between humans and machines due to the algorithms' missing expertise of, e.g., cultural, and social structures. In this work, a design science approach is used to derive design knowledge and develop an artifact, through which humans are integrated in the process of detecting and evaluating hate speech. For this purpose, explainable artificial intelligence (XAI) is utilized: the artifact will provide explanative information, why the deep learning model predicted whether a text contains hate. Results show that the instantiated design knowledge in form of a dashboard is perceived as valuable and that XAI features increase the perception of the artifact's usefulness, ease of use, trustworthiness as well as the intention to use it.

A nascent design theory for explainable intelligent systems

“…Lastly, matching theoretical foundations with our research findings also reveals differences. Comparing our findings with related design theories (Bunde 2021;Landwehr et al 2022;Schemmer et al 2022) shows that only four out of our ten design features have been mentioned earlier. This includes design features such as providing input information (DF3) and historical information (DF4) as well as using explanation techniques (DF8) and incorporating granularity and navigability (DF9).…”

“…Discussion. There are several contributions dealing with design approaches for EIS (e.g., Bunde 2021;Landwehr et al 2022;Schemmer et al 2022) to create a hybrid intelligence as Dellermann et al (2019) have called it.…”

“…In terms of the derived design principles, our study also extends the current body of design knowledge. That is, while recent research targets end-users and is thus limited to addressing local explainability (e.g., Bunde 2021;Landwehr et al 2022;, our nascent design theory does not only include local explainability (DP2) but also incorporates global explainability (DP1) for developers. In addition, while theoretical contributions (e.g., Mohseni et al 2021) are mainly assigning DP2 to end-users, our research indicate, that developers also benefit from using local explanations.…”

“…Apart IS-related contributions such as Förster et al ( 2020) who provide a design process for user-centric XAI systems and Herm, Wanner, et al (2022b) who introduce a taxonomy to assist user-centered XAI research, we were only able to identify a handful of DSR-based contributions that focus on user-based studies for EIS (Bunde 2021;Cirqueira et al 2021;Landwehr et al 2022;Schemmer et al 2022). and Bunde (2021) provide design principles for explainable DSS limited to detecting hate speech. Landwehr et al (2022) derive design knowledge for image-based DSS.…”

Towards a Human Values Dashboard for Software Development

“…Prototyping is commonly used for requirements elicitation to 'provide users with an idea of how a system will behave' [15]. Developing an artefact as a prototype to accompany an interview is also commonly used in the field of information systems [20] and considered as the first iteration in the design science research methodology [10,52,53]. Prototyping helped us communicate our ideas and obtain feedback from the interview participants to develop a human values dashboard.…”