# On Transparency and Accountability of Smart Assistants in Smart Cities
## 7 Limitations  
In this paper, we focus on transparency issues in smart assistants and explore their impact on the privacy protection of smart city inhabitants and the accountability of these products. In this regard, we take the example of GA--a state-of-the-art smart assistant and perform its diagnostic analysis. We followed the standard method to learn the expectations of Android users and compared them with the actual permission requirements of GA and also recorded their related risk perceptions. The decisions and choices of Android users while evaluating an app are influenced by the information offered to them and their privacy awareness and skills [62]. However, the role of the offered information cannot be ignored [64]. Moreover, it is known that user decisions while reading app descriptions and reviewing its permission requirements for making choices at the download stage represent their expectations [67; 68]. It is also important to note that our analysis does not evaluate the post-installation behaviors of GA. Future research can assess the post-installation behaviors and operations of GA from a transparency point of view. Moreover, transparency of risk communication also influences trust [62]. The impact of the lack of transparency in risk communication by GA to its potential users on their trust can also be an interesting area for future research. Moreover, we assessed the Android Sensitive API usage in GA for its validation and accountability, treating it as an independent app. Using Android Sensitive APIs to evaluate underlying implementations of an app is a widely-used approach [46; 56; 89]. However, its more thorough analysis can reveal its behaviors such as whether it colludes with other apps installed on Android smartphones. If it colludes with other apps installed on an Android smartphone, its ethical implications need to be assessed. Its behavior on devices other than smartphones should also be investigated. Finally, transparency issues in other smart assistants and their impact on the lives of smart city inhabitants also need to be assessed. In the case of user studies, we used web-based surveys, which are believed to be cost-effective and speedy and can generate large samples [90]. However, User Study 1 had a lower response rate (53%). We believe that it was due to two reasons. (1) The access to Google Forms was restricted (based on our observation, we had assumed that international students had means to access Google Forms). (2) It asked a difficult question, that is, relating app description to the required permissions. A need to consider app description and given permissions concurrently to establish a relationship makes it difficult from a problem-solving perspective [91]. However, this is exactly what is expected from a smartphone user in the privacy self-management model followed in Android devices [38; 92]. In User Study 2, we followed a controlled approach and obtained a better response rate (99%). Likewise, such studies cannot create a user experience equivalent to the real situations encountered by users while installing apps. However, they are a useful tool to measure estimates. Our results can help developers of smart city products who \"end up designing applications either based on their assumptions on user privacy expectations or relating to their own expectations of privacy as a user\" [93]. Further, the participants in this study were university students, and their ages varied, particularly in the first user study. Previous research shows that younger users of technology are comparatively more privacy-aware, and university education also increases privacy concerns [94]. Thus, the sample of this study could have introduced a bias in the research where increased privacy awareness and skills of the participants would lead to positive results. However, our findings indicate no correlation between age and responses in the first user study.