To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
With the rapid development of artificial intelligence technology, human–AI interaction and collaboration have become important topics in the field of contemporary technology. The capabilities of AI have gradually expanded from basic task automation to complex decision support, content creation, and intelligent collaboration in high-risk scenarios. This technological evolution has provided unprecedented opportunities for industries in different fields, but also brought challenges, such as privacy protection, credibility issues, and the ethical and legal relationship between AI and humans. This book explores the role and potential of AI in human–AI interaction and collaboration from multiple dimensions and analyzes AI’s performance in privacy and credibility, knowledge sharing, search interaction, false information processing, and high-risk application scenarios in detail through different chapters.
Informal caregivers such as family members or friends provide much care to people with physical or cognitive impairment. To address challenges in care, caregivers often seek information online via social media platforms for their health information wants (HIWs), the types of care-related information that caregivers wish to have. Some efforts have been made to use Artificial Intelligence (AI) to understand caregivers’ information behaviors on social media. In this chapter, we present achievements of research with a human–AI collaboration approach in identifying caregivers’ HIWs, focusing on dementia caregivers as one example. Through this collaboration, AI techniques such as large language models (LLMs) can be used to extract health-related domain knowledge for building classification models, while human experts can benefit from the help of AI to further understand caregivers’ HIWs. Our approach has implications for the caregiving of various groups. The outcomes of human–AI collaboration can provide smart interventions to help caregivers and patients.
Misinformation on social media is a recognized threat to societies. Research has shown that social media users play an important role in the spread of misinformation. It is crucial to understand how misinformation affects user online interaction behavior and the factors that contribute to it. In this study, we employ an AI deep learning model to analyze emotions in user online social media conversations about misinformation during the COVID-19 pandemic. We further apply the Stimuli–Organism–Response framework to examine the relationship between the presence of misinformation, emotions, and social bonding behavior. Our findings highlight the usefulness of AI deep learning models to analyze emotions in social media posts and enhance the understanding of online social bonding behavior around health-related misinformation.
In Chapter 2, the classification of data processed by MDTs under the General Data Protection Regulation (GDPR) is examined. While the data processed by MDTs is typically linked to the category of biometric data, accurately classifying the data as special category biometric data is complex. As a result, substantial amounts of data lack the special protections afforded by the GDPR. Notably, data processed by text-based MDTs falls entirely outside the realm of special protection unless associated with another protected category. The book advocates for a shift away from focusing on the technological or biophysical parameters that render mental processes datafiable. Instead, it emphasises the need to prioritise the protection of the information itself. To address this, Chapter 2 proposes the inclusion of a new special category of ‘mind data’ within the GDPR. The analysis shows that classifying mind data as a sui generis special category aligns with the rationale and tradition of special category data in data protection law.
Generative AI based on large language models (LLM) currently faces serious privacy leakage issues due to the wide range of parameters and diverse data sources. When using generative AI, users inevitably share data with the system. Personal data collected by generative AI may be used for model training and leaked in future outputs. The risk of private information leakage is closely related to the inherent operating mechanism of generative AI. This indirect leakage is difficult to detect by users due to the high complexity of the internal operating mechanism of generative AI. By focusing on the private information exchanged during interactions between users and generative AI, we identify the privacy dimensions involved and develop a model for privacy types in human–generative AI interactions. This can provide a reference for generative AI to avoid training private data and help it provide clear explanations of relevant content for the types of privacy users are concerned about.
Use case 1 in Chapter 4 explores the regulation of MDTs in the context of mental health and well-being under the General Data Protection Regulation (GDPR), the Medical Devices Regulation (MDR), the Artificial Intelligence Act (AIA), and the European Health Data Space (EHDS) Regulation. The analysis reveals that data protection issues in this sector are not primarily due to deficiencies in the law, but rather stem from significant compliance weaknesses, particularly in applications extending beyond the traditional medical sector. Consumer mental health and well-being devices could greatly benefit from co-regulatory measures, such as a sector-specific data protection certification. Additionally, legislators need to tackle the issue of manufacturers circumventing MDR certification due to ambiguities in the classification model. The EU’s regulatory approach to non-medical Brain–Computer Interfaces (BCIs) within medical devices legislation is highlighted as a potential blueprint and should be advocated in ongoing global policy discussions concerning neurotechnologies.
As generative AI technologies continue to advance at a rapid pace, they are fundamentally transforming the dynamics of human–AI interaction and collaboration, a phenomenon that was once relegated to the realm of science fiction. These developments not only present unprecedented opportunities but also introduce a range of complex challenges. Key factors such as trust, transparency, and cultural sensitivity have emerged as essential considerations in the successful adoption and efficacy of these systems. Furthermore, the intricate balance between human and AI contributions, the optimization of algorithms to accommodate diverse user needs, and the ethical implications of AI’s role in society pose significant challenges that require careful navigation. This chapter will delve into these multifaceted issues, analyzing both user-level concerns and the underlying technical and psychological dynamics that are critical to fostering effective human–AI interaction and collaboration.
The last decade has seen an exponential increase in the development and adoption of language technologies, from personal assistants such as Siri and Alexa, through automatic translation, to chatbots like ChatGPT. Yet questions remain about what we stand to lose or gain when we rely on them in our everyday lives. As a non-native English speaker living in an English-speaking country, Vered Shwartz has experienced both amusing and frustrating moments using language technologies: from relying on inaccurate automatic translation, to failing to activate personal assistants with her foreign accent. English is the world's foremost go-to language for communication, and mastering it past the point of literal translation requires acquiring not only vocabulary and grammar rules, but also figurative language, cultural references, and nonverbal communication. Will language technologies aid us in the quest to master foreign languages and better understand one another, or will they make language learning obsolete?
Speculative design is widely used in research contexts across multiple disciplines, emphasising problem-finding over problem-solving, and involves methods for exploring possibilities that challenge ingrained assumptions. This systematic literature review analyses speculative design methods used in 52 studies within disciplines such as human–computer interaction, fashion, urban planning, and healthcare, among other fields. It presents results about the common phases and methods of speculative design that are utilised in these studies. It identifies and characterises four core phases that appear to be common within speculative design processes, namely select, explore, transform, and provoke. It shares examples of how these phases are used to achieve the goals of speculative design. The discussion section considers the process of speculative design, leading to the synthesis of a framework that visually and conceptually organises these findings to facilitate their comprehension and application. This paper contributes to the understanding of speculative design by providing a clear process that addresses gaps in its theoretical and methodological foundations.
This article explores biophilic (nature-centred) instrument design and its intersection with architecture and music. While the connection between these disciplines is often discussed figuratively, they are less often combined in practice. The Biophilic Instrument Pavilion (BIP), a site-responsive sound and light installation, serves as a model for such a collaboration using biophilic design as a unifying principle. This multidisciplinary project demonstrates spatialisation in ecological, sonic, visual and social contexts, offering insights into environmental instrument practices and collaborative creative processes.
This study synthesized 65 (quasi-)experimental studies published between 2010 and 2024 that examined the use of mobile applications to develop language learners’ vocabulary learning. Bayesian meta-analysis was adopted to assess (1) overall effect size; (2) subgroup analyses (i.e. education level, vocabulary knowledge, aspects of vocabulary learning, learning environment, sample size, mobile application type, gender, and cultural background); and (3) publication bias. A large effect size of 1.28 was found for the overall effectiveness of using mobile applications for vocabulary learning when we restricted the studies to long-term treatment duration of 10 weeks or above. Each moderator was analyzed and discussed, and implications for language teaching and research were provided.
This paper considers two supercritical branching processes with immigration in different random environments, denoted by $\{Z_{1,n}\}$ and $\{Z_{2,m}\}$, with criticality parameters µ1 and µ2, respectively. Under certain conditions, it is known that $\frac{1}{n} \log Z_{1,n} \to \mu_1$ and $\frac{1}{m} \log Z_{2,m} \to \mu_2$ converge in probability as $m, n \to \infty$. We present basic properties about a central limit theorem, a non-uniform Berry–Esseen’s bound, and Cramér’s moderate deviations for $\frac{1}{n} \log Z_{1,n} - \frac{1}{m} \log Z_{2,m}$ as $m, n \to \infty$. To this end, applications to construction of confidence intervals and simulations are also given.
Let $K^r_n$ be the complete $r$-uniform hypergraph on $n$ vertices, that is, the hypergraph whose vertex set is $[n] \, :\! = \{1,2,\ldots ,n\}$ and whose edge set is $\binom {[n]}{r}$. We form $G^r(n,p)$ by retaining each edge of $K^r_n$ independently with probability $p$. An $r$-uniform hypergraph $H\subseteq G$ is $F$-saturated if $H$ does not contain any copy of $F$, but any missing edge of $H$ in $G$ creates a copy of $F$. Furthermore, we say that $H$ is weakly$F$-saturated in $G$ if $H$ does not contain any copy of $F$, but the missing edges of $H$ in $G$ can be added back one-by-one, in some order, such that every edge creates a new copy of $F$. The smallest number of edges in an $F$-saturated hypergraph in $G$ is denoted by ${\textit {sat}}(G,F)$, and in a weakly $F$-saturated hypergraph in $G$ by $\mathop {\mbox{$w$-${sat}$}}\! (G,F)$. In 2017, Korándi and Sudakov initiated the study of saturation in random graphs, showing that for constant $p$, with high probability ${\textit {sat}}(G(n,p),K_s)=(1+o(1))n\log _{\frac {1}{1-p}}n$, and $\mathop {\mbox{$w$-${sat}$}}\! (G(n,p),K_s)=\mathop {\mbox{$w$-${sat}$}}\! (K_n,K_s)$. Generalising their results, in this paper, we solve the saturation problem for random hypergraphs $G^r(n,p)$ for cliques $K_s^r$, for every $2\le r \lt s$ and constant $p$.
Based on the characteristics of the variable pivot gait during the human load-carrying, this paper proposes a double-leg coordination assistance principle for load-carrying: assisting support of the guiding leg at the heel-pivot stage by the spring to reduce the collision, which can reduce the ankle moment of the following leg that is performing the push-off at the toe-pivot stage. A novel unpowered load-carrying exoskeleton (ULE) with a double-support closed-chain configuration is designed, and the theoretical verification is carried out. Five subjects participate in the load-carrying and metabolic cost experiments for assisting and energy-saving effect evaluation, and the angle and moment of human joints, plantar pressure, spring compression and human net metabolic rate are analyzed. Compared with carrying load by the human alone, wearing the novel ULE with spring reduces the human peak ankle moment performing the push-off by up to 11.9 ± 1.6% (Mean±SE, 10 kg), average ankle moment over the support phase by up to 36.8 ± 9.1% (Mean±SE, 5 kg) and the average vertical plantar pressure by up to 8.1 ± 1%% (Mean±SE, 15 kg). Meanwhile, wearing the novel ULE reduces the human net metabolic rate by 5.6 ± 0.5% (Mean±SE, 10 kg), 4.1 ± 0.7% (Mean±SE, 15 kg) and 5.9 ± 1.6% (Mean±SE, 20 kg). The results show that the novel ULE can provide support and joint moment assistance over the whole support phase while reducing human net metabolic rate. This study can also be applied to the powered load-carrying exoskeleton, providing a new avenue.
Neural network (NN)-based control policies have proven their advantages in cyber-physical systems (CPS). When an NN-based policy fails to fulfill a formal specification, engineers leverage NN repair algorithms to fix its behaviors. However, such repair techniques risk breaking the existing correct behaviors, losing not only correctness but also verifiability of initial state subsets. That is, the repair may introduce new risks, previously unaccounted for. In response, we formalize the problem of Repair with Preservation (RwP) and develop Incremental Simulated Annealing Repair (ISAR). ISAR is an NN repair algorithm that aims to preserve correctness and verifiability — while repairing as many failures as possible. Our algorithm leverages simulated annealing on a barriered energy function to safeguard the already-correct initial states while repairing as many additional ones as possible. Moreover, formal verification is utilized to guarantee the repair results. ISAR is compared to a reviewed set of state-of-the-art algorithms, including (1) reinforcement learning based techniques (STLGym and F-MDP), (2) supervised learning-based techniques (MIQP and minimally deviating repair), and (3) online shielding techniques (tube MPC shielding). Upon evaluation on two standard benchmarks, OpenAI Gym mountain car and an unmanned underwater vehicle, ISAR not only preserves correct behaviors from previously verified initial state regions, but also repairs 81.4% and 23.5% of broken state spaces in the two benchmarks. Moreover, the signal temporal logic (STL) robustness of the ISAR-repaired policies is higher than the baselines.
This research examines whether a machine, specifically artificial intelligence (AI), can be creative by comparing design solutions for a practical competition – a light fixture for a pediatric waiting room – among AI, collaboration efforts and a human designer. Amazon Mechanical Turk and Prolific workers observed the design solutions throughout the design process, from sketches ($ S $) to three-dimensional renderings ($ 3D $) to fully developed models in virtual waiting rooms ($ VR $). Using the well-established Creative Product Semantic Scale (CPSS), the workers rated each design solution in three distinctive stages – $ S $, $ 3D $ and $ VR $ – on three criteria – novelty (freshness or newness), resolution (relevance and logic) and style (craftsmanship and desirability). Despite some demographic discrepancies, the workers expressed general senses of happiness and calmness, resonating with the competition’s requirements. Statistical results of CPSS ratings revealed that while AI excelled in style for $ 3D $, the human designer outperformed in novelty for both $ S $ and $ VR $. Collaboration efforts surprisingly finished last. Such findings challenge current assumptions of AI’s creative ability in design research and highlight the need to be agile in the age of disruptive technologies. This research also offers guidance for product and interior designers and educators on thoughtfully integrating AI into the design process.
While the relationship between space and openness has been explored in electroacoustic music since the 1960s, and contemporary composers have shown increasing interest in contingency, recent advancements in ambisonics, sound diffusion, and VR have granted composers greater control over the spatial image presented to the listener. This article revisits the discussion of space and openness through the lens of the author’s artistic practice and compositional experience, framed by new materialism, object-oriented philosophy and relational space theory. Through case studies from the author’s work, it examines spatialisation strategies that emphasise openness and the agency of sound materials. These strategies include sound source localisation, networks of family resemblances and parametric spatialisation, aiming to create an open sound experience that maintains identity while allowing agency for the sound material, the listener and the composer. In light of current global crises, partly driven by total control and exploitation, this article advocates for rethinking compositional practices to foster open sound experiences that reflect dynamic interactions between composer, material and listener.