Advancements in Human-AI Collaboration and Explainability

The field of artificial intelligence is moving towards a more collaborative and transparent approach, with a focus on human-AI partnerships and explainability. Recent research has emphasized the importance of developing AI systems that can provide clear and understandable explanations for their decisions and actions, in order to build trust and ensure accountability. This shift is driven by the need for more effective and responsible AI implementation in high-stakes domains, such as criminal justice, air traffic control, and emergency response. Noteworthy papers in this area include SynLang and Symbiotic Epistemology, which introduces a philosophical foundation for human-AI cognitive partnerships and a formal protocol for transparent human-AI collaboration. Another notable paper is Adaptive XAI in High Stakes Environments, which proposes a conceptual framework for adaptive explainability that operates non-intrusively by responding to users' real-time cognitive and emotional states through implicit feedback.

Sources

Demystifying AI in Criminal Justice

SynLang and Symbiotic Epistemology: A Manifesto for Conscious Human-AI Collaboration

NPO: Learning Alignment and Meta-Alignment through Structured Human Feedback

Adaptive XAI in High Stakes Environments: Modeling Swift Trust with Multimodal Feedback in Human AI Teams

Trustworthy AI: UK Air Traffic Control Revisited

Explainability Through Systematicity: The Hard Systematicity Challenge for Artificial Intelligence

Opacity as Authority: Arbitrariness and the Preclusion of Contestation

XABPs: Towards eXplainable Autonomous Business Processes

Transparent AI: The Case for Interpretability and Explainability

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