Prf Harith Alani

“Explainability is of significant importance in the move towards trusted, responsible and ethical AI, yet remains in infancy. Most relevant efforts focus on the increased transparency of AI model design and training data, and on statistics-based interpretations of resulting decisions. The understandability of such explanations and their suitability to particular users and application domains received very little attention so far. Hence there is a need for an interdisciplinary and drastic evolution in XAI methods, to design more understandable, reconfigurable and personalisable explanations. Knowledge Graphs offer significant potential to better structure the core of AI models, and to use semantic representations when producing explanations for their decisions. By capturing the context and application domain in a granular manner, such graphs offer a much needed semantic layer that is currently missing from typical brute-force machine learning approaches. Human factors are key determinants of the success of relevant AI models. In some contexts, such as misinformation detection, existing XAI technical explainability methods do not suffice as the complexity of the domain and the variety of relevant social and psychological factors can heavily influence users’ trust in derived explanations. Past research has shown that presenting users with true / false credibility decisions is inadequate and ineffective, particularly when a black-box algorithm is used. To this end, CIMPLE aims to experiment with innovative social and knowledge-driven AI explanations, and to use computational creativity techniques to generate powerful, engaging, and easily and quicky understandable explanations of rather complex AI decisions and behaviour. These explanations will be tested in the domain of detection and tracking of manipulated information, taking into account social, psychological and technical explainability needs and requirements.”

The Corona-virus outbreak is continuing to spread. By beginning of February, the number of infected people surpasses 42,000 infections, and the death toll continues to rise. As authorities and responders are struggling to contain the spread, news about mass quarantine camps or shortages of personal protective equipment threaten the health systems globally, fueled by rumors and mis-information. The disruptions of (medical) supply chains, the lack of capacity to treat patients and the spread of rumours fuel an atmosphere of uncertainty and mistrust, hampering an effective response. While traditional models of disease outbreaks largely focus on infection rates, new methods are needed to integrate behaviour from the bottom up, and integrated in macro-level models to coordinate the response world-wide.

This research project aims to tackle the pressing issue of misinformation, especially concerning climate change, by leveraging the powers of GIS. Despite numerous efforts to combat misinformation, its prevalence continues to rise, influencing public perceptions and derailing climate policy debates and decisions. This proposal identifies and addresses several key challenges: the absence of effective tools for geospatial mapping and analyses of climate misinformation alongside climate data, the limited understanding of how climate misinformation spreads geographically, and the lack of effective tools to visualise the interplay of geospatial, temporal, and topical patterns of climate misinformation. To address these challenges, we propose the development of a Multidimensional Geographic Information System (GIS). This GIS will integrate climate data (temperature, precipitation, wind, carbon emission), climate misinformation data from fact-checkers and traditional and social media platforms, analyse its correlations with geographical and climate information, and enhance our predictive capabilities to counter the spread of misinformation effectively and proactively. The next three Conference Of the Parties (COP) will be used as use cases to train and evaluate our research and outcomes. This project, ClimateSense, aims to maximise the capabilities of GIS, enabling it to significantly influence societal understanding and policymaking in the face of climate change and misinformation.

The aim of this proposal is, firstly and fore-mostly, to create an open-source, community-driven resilience platform, designed by the communities, for the communities, to enable them to self-organise during crisis, to co-create and share knowledge and trustworthy advise, and to identify assess, and validate humanitarian needs at the community (macro) and citizen (micro) levels, to target their relief and recovery efforts more effectively and rapidly. Secondly, the project will foster social innovation during crises by developing and integrating automated methods for advanced processing and linking of crowdsourced information, and for safeguarding communities during critical scenarios from inaccurate, distrusted, and overhyped information, and for arming humanitarian communities with enriched, high quality, and actionable information. More specifically, the objectives are to: 1. Extract the socio-technical requirements for community resilience platforms by reviewing the role of technology during previous crises scenarios, and by engaging communities in participatory design and requirements gathering initiatives 2. Produce novel automated methods for identifying and semantically representing the geographical, temporal, and topic clusters of citizen emergency events, and the community network broadcasting these events. 3. Design and develop programs and algorithms for measuring the trustworthiness, informativeness, and veracity of information in multiple languages, gathered from distributed social data sources and communities during crisis 4. Integrate and release project output with Ushahidi;7 a popular open-source platform for crisis situations. The platform will extensible, and grounded on open data, open source, and open hardware. 5. Train and evaluate project tools and algorithms with real communities and in live events, as well as with over 10 million multilingual historical social media messages collected for nearly 30 crises including floods, earthquakes, terrorist attacks, hurricanes, and wildfires. The proposal aims to target the objectives above with an interdisciplinary consortium of computer scientists, social scientists, and humanitarian research organisations, through user driven designs and pilots, and involving several existing communities.