21,418 research outputs found
Towards Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning
The next step for intelligent dialog agents is to escape their role as silent
bystanders and become proactive. Well-defined proactive behavior may improve
human-machine cooperation, as the agent takes a more active role during
interaction and takes off responsibility from the user. However, proactivity is
a double-edged sword because poorly executed pre-emptive actions may have a
devastating effect not only on the task outcome but also on the relationship
with the user. For designing adequate proactive dialog strategies, we propose a
novel approach including both social as well as task-relevant features in the
dialog. Here, the primary goal is to optimize proactive behavior so that it is
task-oriented - this implies high task success and efficiency - while also
being socially effective by fostering user trust. Including both aspects in the
reward function for training a proactive dialog agent using reinforcement
learning showed the benefit of our approach for more successful human-machine
cooperation
Boundaries of Logics in Municipality Communicators’ Facebook Practice: Towards a New Public Service Competence
With an increased use of external online platforms, digital government logics are gradually intertwined with external, algorithmic, crowd-influenced value logics of social media platforms. This new scene especially affects administration, which can no longer neutrally deliver public service, but becomes involved in processes of consideration and judging what rules and traditions seem most appropriate in the situation. Through deep interviews and workshops with municipal communicators, we examine this balancing act when communicators use social media for external communication. We use a practice perspective to characterize and conceptualize an emerging approach to public service
Designing the Health-related Internet of Things: Ethical Principles and Guidelines
The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols
Efficient XAI Techniques: A Taxonomic Survey
Recently, there has been a growing demand for the deployment of Explainable
Artificial Intelligence (XAI) algorithms in real-world applications. However,
traditional XAI methods typically suffer from a high computational complexity
problem, which discourages the deployment of real-time systems to meet the
time-demanding requirements of real-world scenarios. Although many approaches
have been proposed to improve the efficiency of XAI methods, a comprehensive
understanding of the achievements and challenges is still needed. To this end,
in this paper we provide a review of efficient XAI. Specifically, we categorize
existing techniques of XAI acceleration into efficient non-amortized and
efficient amortized methods. The efficient non-amortized methods focus on
data-centric or model-centric acceleration upon each individual instance. In
contrast, amortized methods focus on learning a unified distribution of model
explanations, following the predictive, generative, or reinforcement
frameworks, to rapidly derive multiple model explanations. We also analyze the
limitations of an efficient XAI pipeline from the perspectives of the training
phase, the deployment phase, and the use scenarios. Finally, we summarize the
challenges of deploying XAI acceleration methods to real-world scenarios,
overcoming the trade-off between faithfulness and efficiency, and the selection
of different acceleration methods.Comment: 15 pages, 3 figure
Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study
Auditing plays a pivotal role in the development of trustworthy AI. However,
current research primarily focuses on creating auditable AI documentation,
which is intended for regulators and experts rather than end-users affected by
AI decisions. How to communicate to members of the public that an AI has been
audited and considered trustworthy remains an open challenge. This study
empirically investigated certification labels as a promising solution. Through
interviews (N = 12) and a census-representative survey (N = 302), we
investigated end-users' attitudes toward certification labels and their
effectiveness in communicating trustworthiness in low- and high-stakes AI
scenarios. Based on the survey results, we demonstrate that labels can
significantly increase end-users' trust and willingness to use AI in both low-
and high-stakes scenarios. However, end-users' preferences for certification
labels and their effect on trust and willingness to use AI were more pronounced
in high-stake scenarios. Qualitative content analysis of the interviews
revealed opportunities and limitations of certification labels, as well as
facilitators and inhibitors for the effective use of labels in the context of
AI. For example, while certification labels can mitigate data-related concerns
expressed by end-users (e.g., privacy and data protection), other concerns
(e.g., model performance) are more challenging to address. Our study provides
valuable insights and recommendations for designing and implementing
certification labels as a promising constituent within the trustworthy AI
ecosystem
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