How cognitive biases affect XAI-assisted decision-making: A systematic review
The field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to complex AI
systems. Although it is usually considered an essentially technical field, effort has been …
systems. Although it is usually considered an essentially technical field, effort has been …
Xair: A systematic metareview of explainable ai (xai) aligned to the software development process
Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in
regard to its practical implementation in various application domains. To combat the lack of …
regard to its practical implementation in various application domains. To combat the lack of …
A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
[HTML][HTML] How the different explanation classes impact trust calibration: The case of clinical decision support systems
Abstract Machine learning has made rapid advances in safety-critical applications, such as
traffic control, finance, and healthcare. With the criticality of decisions they support and the …
traffic control, finance, and healthcare. With the criticality of decisions they support and the …
Scalar reward is not enough: A response to silver, singh, precup and sutton (2021)
The recent paper “Reward is Enough” by Silver, Singh, Precup and Sutton posits that the
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …
Explainable, domain-adaptive, and federated artificial intelligence in medicine
Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in
each domain is driven by a growing body of annotated data, increased computational …
each domain is driven by a growing body of annotated data, increased computational …
Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty
In recent years, AI explainability (XAI) has received wide attention. Although XAI is expected
to play a positive role in decision-making and advice acceptance, various opposing effects …
to play a positive role in decision-making and advice acceptance, various opposing effects …
Explainable reinforcement learning for broad-xai: a conceptual framework and survey
Broad-XAI moves away from interpreting individual decisions based on a single datum and
aims to provide integrated explanations from multiple machine learning algorithms into a …
aims to provide integrated explanations from multiple machine learning algorithms into a …
On selective, mutable and dialogic XAI: A review of what users say about different types of interactive explanations
Explainability (XAI) has matured in recent years to provide more human-centered
explanations of AI-based decision systems. While static explanations remain predominant …
explanations of AI-based decision systems. While static explanations remain predominant …
[HTML][HTML] Modeling of particle sizes for industrial HPGR products by a unique explainable AI tool-A “Conscious Lab” development
Abstract High-Pressure Grinding Rolls (HPGR), as a modified type of roll crushers, could
intensively reduce the energy consumptions in the mineral processing comminution units …
intensively reduce the energy consumptions in the mineral processing comminution units …