Many call for algorithmic systems to be more transparent, yet it is often unclear for designers how to do so in practice. Standards are emerging that aim to support designers in building transparent systems, e.g by setting testable transparency levels, but their efficacy in this regard is not yet understood. In this paper, we use the ‘Standard for Transparency of Autonomous Systems’ (IEEE 7001) to explore designers’ understanding of algorithmic system transparency, and the degree to which their perspectives align with the standard’s recommendations. Our mixed-method study reveals participants consider transparency important, difficult to implement, and welcome support. However, despite IEEE 7001’s potential, many did not find its recommendations particularly appropriate. Given the importance and increased attention on transparency, and because standards like this purport to guide system design, our findings reveal the need for ‘bridging the gap’, through (i) raising designers’ awareness about the importance of algorithmic system transparency, alongside (ii) better engagement between stakeholders (i.e. standards bodies, designers, users). We further identify opportunities towards developing transparency best practices, as means to help drive more responsible systems going forward.

As our physical environments become ever-more connected, instrumented, and automated, it can be increasingly difficult for users to understand what is happening within them and why. This warrants attention; with the pervasive and physical nature of the Internet of Things (IoT) comes risks of data misuse, privacy, surveillance, and even physical harm. Such concerns come amid increasing calls for more transparency surrounding technologies (in general), as a means for supporting scrutiny and accountability. This article explores the practical dimensions to transparency mechanisms within the consumer IoT. That is, we consider how smart homes might be made more meaningfully transparent, so as to support users in gaining greater understanding, oversight, and control. Through a series of three user-centric studies, we: 1) survey prospective smart home users to gain a general understanding of what meaningful transparency within smart homes might entail; 2) identify categories of user-derived requirements and design elements (design features for supporting smart home transparency) that have been created through two co-design workshops; and 3) validate these through an evaluation with an altogether new set of participants. In all, these categories of requirements and interface design elements provide a foundation for understanding how meaningful transparency might be achieved within smart homes, and introduces several wider considerations for doing so.

Given the surge in interest in AI, we now see the emergence of Artificial Intelligence as a Service (AIaaS). AIaaS entails service providers offering remote access to ML models and capabilities at arms-length', through networked APIs. Such services will grow in popularity, as they enable access to state-of-the-art ML capabilities, 'on demand', 'out of the box', at low cost and without requiring training data or ML expertise. However, there is much public concern regarding AI. AIaaS raises particular considerations, given there is much potential for such services to be used to underpin and drive problematic, inappropriate, undesirable, controversial, or possibly even illegal applications. A key way forward is through service providers monitoring their AI services to identify potential situations of problematic use. Towards this, we elaborate the potential for 'misuse indicators' as a mechanism for uncovering patterns of usage behaviour warranting consideration or further investigation. We introduce a taxonomy for describing these indicators and their contextual considerations, and use exemplars to demonstrate the feasibility analysing AIaaS usage to highlight situations of possible concern. We also seek to draw more attention to AI services and the issues they raise, given AIaaS' increasing prominence, and the general calls for the more responsible and accountable use of AI.

XR (Virtual, Augmented and Mixed Reality) technologies are growing in prominence. However, they are increasingly being used in sectors and in situations that can result in harms. As such, this paper argues the need for auditability to become a key consideration of XR systems. Auditability entails capturing information of a system’s operation to enable oversight, inspection or investigation. Things can and will go wrong, and information that helps unpack situations of failure or harm, and that enables accountability and recourse, will be crucial to XR’s adoption and acceptance. In drawing attention to the urgent need for auditability, we illustrate some risks associated with XR technology and their audit implications, and present some initial findings from a survey with developers indicating the current ‘haphazard’ approach towards such concerns. We also highlight some challenges and considerations of XR audit in practice, as well as areas of future work for taking this important area of research forward.

Social networking sites (SNSs) are becoming increasingly popular as a method for social interaction. While research has reported benefits associated with components of SNS usage, a digital divide has emerged between younger and older users. SNSs can be useful for communicating with family members and helping one feel digitally included; however, there are a wide range of reasons why many older adults choose not to use this kind of technology. We present a series of user studies investigating the barriers and challenges that SNSs can present to older users. These user studies led to the derivation of user recommendations to mitigate these barriers. The recommendations were then evaluated within a comparative evaluation which involved 25 older adults completing tasks on two interface versions of a simulation SNS. We present the recommendations and the methods of their creation and evaluation. Implications for developers of SNSs are discussed.