arXiv subjects
Publications and source records attributed to Mark Cote.
We present key challenges and future research directions in the security and privacy of agentic AI, based on a horizon-scanning exercise that brought together thirty leading international experts from academia, industry, and government to engage in focused discussions and collaborative exercises on the emerging risks associated with the growing agency of AI.
LLM-driven conversational AI is beginning to disappear into the background, shifting from something used directly towards something increasingly integrated into existing workflows. In the process, markers of origin and training are smoothed away as LLMs become commodified in the eyes of users. We explore how people approach using a web browser with conversational AI built in, focusing on how they develop their understanding and determine whether to trust its outputs. We conducted a study where 20 participants used the Copilot AI features in Microsoft Edge to conduct information retrieval and planning tasks. Participants relied on a combination of existing perceptions of LLMs and internet search, tracing the effect of beliefs about how Copilot generated answers on prompting strategies. The inclusion of citations increased the trustworthiness of answers without participants feeling the need to be check them, with participants often reaching for the same information sources as the CAI when fact-checking.
This paper argues that a techno-philosophical reading of the EU AI Act provides insight into the long-term dynamics of data in AI systems, specifically, how the lifecycle from ingestion to deployment generates recursive value chains that challenge existing frameworks for Responsible AI. We introduce a conceptual tool to frame the AI pipeline, spanning data, training regimes, architectures, feature stores, and transfer learning. Using cross-disciplinary methods, we develop a technically grounded and philosophically coherent analysis of regulatory blind spots. Our central claim is that what remains absent from policymaking is an account of the dynamic of becoming that underpins both the technical operation and economic logic of AI. To address this, we advance a formal reading of AI inspired by Simondonian philosophy of technology, reworking his concept of individuation to model the AI lifecycle, including the pre-individual milieu, individuation, and individuated AI. To translate these ideas, we introduce futurity: the self-reinforcing lifecycle of AI, where more data enhances performance, deepens personalisation, and expands application domains. Futurity highlights the recursively generative, non-rivalrous nature of data, underpinned by infrastructures like feature stores that enable feedback, adaptation, and temporal recursion. Our intervention foregrounds escalating power asymmetries, particularly the tech oligarchy whose infrastructures of capture, training, and deployment concentrate value and decision-making. We argue that effective regulation must address these infrastructural and temporal dynamics, and propose measures including lifecycle audits, temporal traceability, feedback accountability, recursion transparency, and a right to contest recursive reuse.
The theme for CUI 2023 is 'designing for inclusive conversation', but who are CUIs really designed for? The field has its roots in computer science, which has a long acknowledged diversity problem. Inspired by studies mapping out the diversity of the CHI and voice assistant literature, we set out to investigate how these issues have (or have not) shaped the CUI literature. To do this we reviewed the 46 full-length research papers that have been published at CUI since its inception in 2019. After detailing the eight papers that engage with accessibility, social interaction, and performance of gender, we show that 90% of papers published at CUI with user studies recruit participants from Europe and North America (or do not specify). To complement existing work in the community towards diversity we discuss the factors that have contributed to the current status quo, and offer some initial suggestions as to how we as a CUI community can continue to improve. We hope that this will form the beginning of a wider discussion at the conference.
To improve user experience, Alexa now allows users to consent to data sharing via voice rather than directing them to the companion smartphone app. While verbal consent mechanisms for voice assistants (VAs) can increase usability, they can also undermine principles core to informed consent. We conducted a Delphi study with experts from academia, industry, and the public sector on requirements for verbal consent in VAs. Candidate requirements were drawn from the literature, regulations, and research ethics guidelines that participants rated based on their relevance to the consent process, actionability by platforms, and usability by end-users, discussing their reasoning as the study progressed. We highlight key areas of (dis)agreement between experts, deriving recommendations for regulators, skill developers, and VA platforms towards crafting meaningful verbal consent mechanisms. Key themes include approaching permissions according to the user's ability to opt-out, minimising consent decisions, and ensuring platforms follow established consent principles.
Since Siri's release in 2011 there have been a growing number of AI-driven domestic voice assistants that are increasingly being integrated into devices such as smartphones and TVs. But as their presence has expanded, a range of ethical concerns has been identified around the use of voice assistants, such as the privacy implications of having devices that are always listening and the ways that these devices are integrated into the existing social order of the home. This has created a burgeoning area of research across a range of fields including computer science, social science, and psychology. This paper takes stock of the foundations and frontiers of this work through a systematic literature review of 117 papers on ethical concerns with voice assistants. In addition to analysis of nine specific areas of concern, the review measures the distribution of methods and participant demographics across the literature. We show how some concerns, such as privacy, are operationalized to a much greater extent than others like accessibility, and how study participants are overwhelmingly drawn from a small handful of Western nations. In so doing we hope to provide an outline of the rich tapestry of work around these concerns and highlight areas where current research efforts are lacking.
The increasing reach and functionality of voice assistants has allowed them to become a general-purpose platform for tasks like playing music, accessing information, and controlling smart home devices. In order to maintain the quality of third-party skills and to protect children and other members of the public from inappropriate or malicious skills, platform providers have developed content policies and certification procedures that skills must undergo prior to public release. Unfortunately, research suggests that these measures have been ineffective at curating voice assistant platforms, with documented instances of skills with significant security and privacy problems. This provocation paper outlines how the underlying architectures of these platforms had turned skill certification into a seemingly intractable problem, as well as how current certification methods fall short of their full potential. We present a roadmap for improving the state of skill certification on contemporary voice assistant platforms, including research directions and actions that need to be taken by platform vendors. Promoting this change in domestic voice assistants is especially important, as developers of commercial and industrial assistants or other similar contexts increasingly look to these devices for norms and conventions.
Determining how voice assistants should broker consent to share data with third party software has proven to be a complex problem. Devices often require users to switch to companion smartphone apps in order to navigate permissions menus for their otherwise hands-free voice assistant. More in line with smartphone app stores, Alexa now offers "voice-forward consent", allowing users to grant skills access to personal data mid-conversation using speech. While more usable and convenient than opening a companion app, asking for consent 'on the fly' can undermine several concepts core to the informed consent process. The intangible nature of voice interfaces further blurs the boundary between parts of an interaction controlled by third-party developers from the underlying platforms. This provocation paper highlights key issues with current verbal consent implementations, outlines directions for potential solutions, and presents five open questions to the research community. In so doing, we hope to help shape the development of usable and effective verbal consent for voice assistants and similar conversational user interfaces.
Determining how voice assistants should broker consent to share data with third party software has proven to be a complex problem. Devices often require users to switch to companion smartphone apps in order to navigate permissions menus for their otherwise hands-free voice assistant. More in line with smartphone app stores, Alexa now offers "voice-forward consent", allowing users to grant skills access to personal data mid-conversation using speech. While more usable and convenient than opening a companion app, asking for consent 'on the fly' can undermine several concepts core to the informed consent process. The intangible nature of voice interfaces further blurs the boundary between parts of an interaction controlled by third-party developers from the underlying platforms. We outline a research agenda towards usable and effective voice-based consent to address the problems with brokering consent verbally, including our own work drawing on the GDPR and work on consent in Ubicomp.