Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

Partnering People with Deep Learning Systems: Human Cognitive Effects of Explanations

Advances in “deep learning” algorithms have led to intelligent systems that provide automated classifications of unstructured data. Until recently these systems could not provide the reasons behind a classification. This lack of “explainability” has led to resistance in applying these systems in some contexts. An intensive research and development effort to make such systems more transparent and interpretable has proposed and developed multiple types of explanation to address this challenge. Relatively little research has been conducted into how humans process these explanations. Theories and measures from areas of research in social cognition were selected to evaluate attribution of mental processes from intentional systems theory, measures of working memory demands from cognitive load theory, and self-efficacy from social cognition theory. Crowdsourced natural disaster damage assessment of aerial images was employed using a written assessment guideline as the task. The “Wizard of Oz” method was used to generate the damage assessment output of a simulated agent. The output and explanations contained errors consistent with transferring a deep learning system to a new disaster event. A between-subjects experiment was conducted where three types of natural language explanations were manipulated between conditions. Counterfactual explanations increased intrinsic cognitive load and made participants more aware of the challenges of the task. Explanations that described boundary conditions and failure modes (“hedging explanations”) decreased agreement with erroneous agent ratings without a detectable effect on cognitive load. However, these effects were not large enough to counteract decreases in self-efficacy and increases in erroneous agreement as a result of providing a causal explanation. The extraneous cognitive load generated by explanations had the strongest influence on self-efficacy in the task. Presenting all of the explanation types at the same time maximized cognitive load and agreement with erroneous simulated output. Perceived interdependence with the simulated agent was also associated with increases in self-efficacy; however, trust in the agent was not associated with differences in self-efficacy. These findings identify effects related to research areas which have developed methods to design tasks that may increase the effectiveness of explanations

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data