Search results
Filter
5 results
Sort by:
SSRN
Working paper
Negativity spreads faster: A large-scale multilingual twitter analysis on the role of sentiment in political communication
In: Online social networks and media: OSNEM, Volume 33, p. 100242
ISSN: 2468-6964
Ten "Rs" of Social Reaction: Using Social Media to Analyse the "Post-Event" Impacts of the Murder of Lee Rigby
In: Terrorism and political violence, Volume 30, Issue 3, p. 454-474
ISSN: 1556-1836
Understanding the characteristics of COVID-19 misinformation communities through graphlet analysis
In: Online social networks and media: OSNEM, Volume 27, p. 100178
ISSN: 2468-6964
Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI
Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the particular challenges for AI decision support posed in military coalition operations. These include having to deal with limited, low-quality data, which inevitably compromises AI performance. We suggest that these problems can be mitigated by taking steps that allow rapid trust calibration so that decision makers understand the AI system's limitations and likely failures and can calibrate their trust in its outputs appropriately. We propose that AI services can achieve this by being both interpretable and uncertainty-aware. Creating such AI systems poses various technical and human factors challenges. We review these challenges and recommend directions for future research.
BASE