19,311 research outputs found
Fostering Effective Human-AI Collaboration: Bridging the Gap Between User-Centric Design and Ethical Implementation
The synergy between humans and artificial intelligence (AI) systems has become pivotal in contemporary technological landscapes. This research paper delves into the multifaceted domain of Human-AI collaboration, aiming to decipher the intricate interplay between user-centric design and ethical implementation. As AI systems continue to permeate various facets of society, the significance of seamless interaction and ethical considerations has emerged as a critical axis for exploration.
This study critically examines the pivotal components of successful Human-AI collaboration, emphasizing the importance of user experience design that prioritizes intuitive interfaces and transparent interactions. Furthermore, ethical implications encompassing privacy, fairness, bias mitigation, and accountability in AI decision-making are thoroughly investigated, emphasizing the imperative need for responsible AI deployment.
The paper presents an analysis of diverse scenarios where Human-AI collaboration manifests, elucidating the impact on various sectors such as education, healthcare, workforce augmentation, and problem-solving domains. Insights into the cognitive augmentation offered by AI systems and the consequential implications on human decision-making processes are also probed, offering a comprehensive understanding of collaborative problem-solving and decision support mechanisms.
Through an integrative approach merging user-centric design philosophies and ethical frameworks, this research advocates for a paradigm shift in AI development. It underscores the necessity of incorporating user feedback, participatory design methodologies, and transparent ethical guidelines into the development life cycle of AI systems. Ultimately, the paper proposes a roadmap towards fostering a symbiotic relationship between humans and AI, fostering trust, reliability, and enhanced performance in collaborative endeavors.
This abstract outline the scope, key areas of investigation, and proposed outcomes of a research paper centered on Human-AI collaboration, providing a glimpse into the depth and breadth of the study
Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research
Visualization for machine learning (VIS4ML) research aims to help experts
apply their prior knowledge to develop, understand, and improve the performance
of machine learning models. In conceiving VIS4ML systems, researchers
characterize the nature of human knowledge to support human-in-the-loop tasks,
design interactive visualizations to make ML components interpretable and
elicit knowledge, and evaluate the effectiveness of human-model interchange. We
survey recent VIS4ML papers to assess the generalizability of research
contributions and claims in enabling human-in-the-loop ML. Our results show
potential gaps between the current scope of VIS4ML research and aspirations for
its use in practice. We find that while papers motivate that VIS4ML systems are
applicable beyond the specific conditions studied, conclusions are often
overfitted to non-representative scenarios, are based on interactions with a
small set of ML experts and well-understood datasets, fail to acknowledge
crucial dependencies, and hinge on decisions that lack justification. We
discuss approaches to close the gap between aspirations and research claims and
suggest documentation practices to report generality constraints that better
acknowledge the exploratory nature of VIS4ML research
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning
[EN] Data analysis is a key process to foster knowledge generation in particular domains
or fields of study. With a strong informative foundation derived from the analysis of
collected data, decision-makers can make strategic choices with the aim of obtaining
valuable benefits in their specific areas of action. However, given the steady growth
of data volumes, data analysis needs to rely on powerful tools to enable knowledge
extraction.
Information dashboards offer a software solution to analyze large volumes of
data visually to identify patterns and relations and make decisions according to the
presented information. But decision-makers may have different goals and,
consequently, different necessities regarding their dashboards. Moreover, the variety
of data sources, structures, and domains can hamper the design and implementation
of these tools.
This Ph.D. Thesis tackles the challenge of improving the development process of
information dashboards and data visualizations while enhancing their quality and
features in terms of personalization, usability, and flexibility, among others.
Several research activities have been carried out to support this thesis. First, a
systematic literature mapping and review was performed to analyze different
methodologies and solutions related to the automatic generation of tailored
information dashboards. The outcomes of the review led to the selection of a modeldriven
approach in combination with the software product line paradigm to deal with
the automatic generation of information dashboards.
In this context, a meta-model was developed following a domain engineering
approach. This meta-model represents the skeleton of information dashboards and
data visualizations through the abstraction of their components and features and has
been the backbone of the subsequent generative pipeline of these tools.
The meta-model and generative pipeline have been tested through their
integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully
integrated with other meta-model to support knowledge generation in learning
ecosystems, and as a framework to conceptualize and instantiate information
dashboards in different domains.
In terms of the practical applications, the focus has been put on how to transform
the meta-model into an instance adapted to a specific context, and how to finally
transform this later model into code, i.e., the final, functional product. These practical
scenarios involved the automatic generation of dashboards in the context of a Ph.D.
Programme, the application of Artificial Intelligence algorithms in the process, and
the development of a graphical instantiation platform that combines the meta-model
and the generative pipeline into a visual generation system.
Finally, different case studies have been conducted in the employment and
employability, health, and education domains. The number of applications of the
meta-model in theoretical and practical dimensions and domains is also a result itself.
Every outcome associated to this thesis is driven by the dashboard meta-model, which
also proves its versatility and flexibility when it comes to conceptualize, generate, and
capture knowledge related to dashboards and data visualizations
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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