2,492 research outputs found
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
Three Levels of AI Transparency
Transparency is generally cited as a key consideration towards building Trustworthy AI. However, the concept of transparency is fragmented in AI research, often limited to transparency of the algorithm alone. While considerable attempts have been made to expand the scope beyond the algorithm, there has yet to be a holistic approach that includes not only the AI system, but also the user, and society at large. We propose that AI transparency operates on three levels, (1) Algorithmic Transparency, (2) Interaction Transparency, and (3) Social Transparency, all of which need to be considered to build trust in AI. We expand upon these levels using current research directions, and identify research gaps resulting from the conceptual fragmentation of AI transparency highlighted within the context of the three levels
The Survey, Taxonomy, and Future Directions of Trustworthy AI: A Meta Decision of Strategic Decisions
When making strategic decisions, we are often confronted with overwhelming
information to process. The situation can be further complicated when some
pieces of evidence are contradicted each other or paradoxical. The challenge
then becomes how to determine which information is useful and which ones should
be eliminated. This process is known as meta-decision. Likewise, when it comes
to using Artificial Intelligence (AI) systems for strategic decision-making,
placing trust in the AI itself becomes a meta-decision, given that many AI
systems are viewed as opaque "black boxes" that process large amounts of data.
Trusting an opaque system involves deciding on the level of Trustworthy AI
(TAI). We propose a new approach to address this issue by introducing a novel
taxonomy or framework of TAI, which encompasses three crucial domains:
articulate, authentic, and basic for different levels of trust. To underpin
these domains, we create ten dimensions to measure trust:
explainability/transparency, fairness/diversity, generalizability, privacy,
data governance, safety/robustness, accountability, reproducibility,
reliability, and sustainability. We aim to use this taxonomy to conduct a
comprehensive survey and explore different TAI approaches from a strategic
decision-making perspective
Towards Modelling and Verification of Social Explainable AI
Social Explainable AI (SAI) is a new direction in artificial intelligence
that emphasises decentralisation, transparency, social context, and focus on
the human users. SAI research is still at an early stage. Consequently, it
concentrates on delivering the intended functionalities, but largely ignores
the possibility of unwelcome behaviours due to malicious or erroneous activity.
We propose that, in order to capture the breadth of relevant aspects, one can
use models and logics of strategic ability, that have been developed in
multi-agent systems. Using the STV model checker, we take the first step
towards the formal modelling and verification of SAI environments, in
particular of their resistance to various types of attacks by compromised AI
modules
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