Technological systems, especially social robots in public spaces, need to interact with a diverse audience as citizens differ, e.g., in gender, educations, beliefs, and experiences with different technologies. However, a wide array of technological systems do not yet have the ability to cater for this human diversity, as evidenced by cases of algorithmic bias (e.g., a decreased usability of various systems for people of colour (Hankerson et al., 2016). At the same time, an inclusion of all audiences is imperative when deploying new technologies to avoid creating a digital divide between citizens who are affected by algorithmic bias and those who are not, as such a digital divide might have dire consequences for public life (Singh & Singh, 2021). The inclusion of diverse user audiences is an interdisciplinary challenge that has, so far, been neglected. Through the lens of media psychology, it is especially relevant to examine the influence that individuals’ diversity characteristics might have on the experience of algorithmic bias, and how these biased experiences could force individuals into selfselection and consequently cause digital divide. Additionally, when facing situations of algorithmic bias, do humans detect discriminations and how do they attribute a failed interaction to the system or themselves? Addressing these questions, focus groups with Ruhr area citizens are conducted. Participants’ diversity features and attitudes towards technological systems are captured and put into context of their individual experiences with algorithmic bias. Additionally, scenarios of algorithmic bias are be discussed to learn how citizens interpret these scenarios, to capture their attribution of blame and learn of possible coping strategies. Finally, participants are asked to picture a utopian and dystopian scenario in which a social robot is deployed in a public library to examine possible biases and fears of discrimination.

The deployment of social robots in public spaces has received increased interest over the past years. These robots need to process a wide array of personal data to offer services that are tailored to users’ requirements. While much research has been carried out regarding the creation of explainable content, little research has dealt with how data transparency - as a way to address uncertainty and concerns regarding the handling of personal data - is conveyed to users. To examine the impact of different transparency declarations on trust, performance, and robot perception, we conducted a virtual reality (VR) supported laboratory experiment with N = 53 participants who interacted with a robot in a public setting (a library). The interaction between users and robots was accompanied by information on the handling of users’ personal data using three different modalities (via posters, the robot’s tablet, or verbally). The results imply that, while all modalities are understandable and perceived as useful, there is no preference for any modality. Our findings contribute to HRI research by examining different modalities for transparency declarations, in an effort to foster understandable and transparent processing of data.

Street harassment, especially against women, is a prevalent phenomenon also known as catcalling. Numerous campaigns have tried to raise awareness for practices like publicly commenting on women's bodies, whistling or unwanted sexual advances. Recently, activists against catcalling have been especially present on Social Media, outreaching to large international audiences. However, effective ways still have to be found to stimulate an intense reflexion and raise empathy, especially among aggressors and bystanders. Using a virtual reality gallery, including diverse multimedia material and feedback options to stimulate a discussion among visitors, we create an immersive, educating and interactive experience. Rather than moralizing, the gallery promotes a self-paced exploration of the material, combines personal stories with instructive facts to sensitize to street harassment.

Die zunehmende Beliebtheit von "Buy Now Pay Later" (BNPL) Zahlungsmethoden hat den E-Commerce-Sektor bereichert, indem sie den Kunden eine flexible Kaufoption bieten. Trotz seiner wachsenden Beliebtheit steht diese jüngste Entwicklung auch in der Kritik, da sie Bedenken hinsichtlich Überschuldung ausgelöst hat. Diese Arbeit beschäftigt sich mit den Hintergründen der Wahl für BNPL-Zahlungsmethoden, um Erkenntnisse über die zugrundeliegenden Einflussfaktoren zu gewinnen. Zusätzlich können aus diesen Erkenntnissen, Händlern Ratschläge bezüglich ihrer verschiedenen angebotenen Zahlungsmethoden gegeben werden. Die Arbeit erforscht mithilfe von Eye-Tracking den Ursache-Wirkungs-Zusammenhang zwischen dem Bestellwert, den angebotenen Zahlungsmethoden und der Bestellabbruchsrate. Außerdem wird in der Studie eine quantitative Umfrage verwendet, um die Einstellungen und Verhaltensweisen von Konsumenten im E-Commerce zu analysieren. Angehörige der Generation Z wurden befragt und die Daten wurden mit statistischen Tests, wie Chi-Quadrat-Tests, ausgewertet. Die Analyse konzentrierte sich auf Faktoren wie Bestellwert, Konsumentenverhalten und demographische Merkmale. Die Studie zeigt, dass keine bestimmte Methode bei höheren Bestellwerten bevorzugt wird, während etablierte Zahlungsmethoden wie PayPal und Kreditkarte bei niedrigeren Beträgen dominieren. Es besteht kein signifikanter Zusammenhang zwischen der Wahl der Zahlungsmethode und dem Konsumentenverhalten oder demographischen Merkmalen wie Einkommen, Geschlecht und sozialem Status.