Periodic Reporting for period 1 - AI4TRUST (AI-based-technologies for trustworthy solutions against disinformation)
Okres sprawozdawczy: 2023-01-01 do 2024-02-29
The AI4TRUST project aims to combat disinformation, misinformation, and malinformation in the European Union (EU) by fostering a trust-based environment that integrates automated monitoring of social and news media with advanced artificial intelligence (AI) technologies to support human fact-checkers. AI4TRUST will serve as a hybrid system, combining human expertise with machine intelligence to monitor multiple social media platforms in near real-time. Employing novel AI algorithms, it will analyse multimodal and multilingual content, flagging high-risk disinformation for expert review. Professional fact-checkers will then verify facts periodically, providing updated information to algorithms. Through this collaboration, we aim to generate trustworthy reports tailored to the needs of media professionals and policymakers, arming them with reliable information to mitigate the spread of disinformation.
This project strongly integrates key contributions from Social Science and Humanities. Our work focus on misinformation, disinformation, and malinformation, highlighting the necessity of a unified terminology sensitive to diverse cultural backgrounds. It proposes a methodological framework for cross-platform analysis, acknowledging the complexities posed by varying platform functionalities and user tracking limitations, and delving into the social dynamics of misinformation production. Furthermore, through stakeholder interviews and design research, the project aims to integrate socio-contextual insights, prioritizing aspects like explainability, transparency, and trust in human-AI interaction. Additionally, it identifies policy requirements tailored to policymakers and establishes transparency criteria for science communicators and media professionals.
This project strongly integrates key contributions from Social Science and Humanities. Our work focus on misinformation, disinformation, and malinformation, highlighting the necessity of a unified terminology sensitive to diverse cultural backgrounds. It proposes a methodological framework for cross-platform analysis, acknowledging the complexities posed by varying platform functionalities and user tracking limitations, and delving into the social dynamics of misinformation production. Furthermore, through stakeholder interviews and design research, the project aims to integrate socio-contextual insights, prioritizing aspects like explainability, transparency, and trust in human-AI interaction. Additionally, it identifies policy requirements tailored to policymakers and establishes transparency criteria for science communicators and media professionals.
In the first year all participants were engaged in establishing a unified vision and agreeing upon shared methodologies, crucial for developing technical tools but also for identifying, collecting, and producing ground truth data tailored for AI tool training within this project.
Moreover, this work addressed one of the project's major challenges: accessing social media data. Changes in policy for research access to Twitter/X API and the gradual discontinuation of Crowdtangle by Facebook/Meta necessitated diversifying data sources beyond the original plan. Consequently, alongside the continued access to Youtube, a novel methodology for collecting Telegram data was devised, customized to the topics outlined. Also, the progress associated with the Social Media Data Streams will be included in the upcoming phase.
This exploration of new data sources offers a fresh scientific perspective to be further explored, where we are currently evaluating novel applications of social network analysis utilizing perspectives distinct from those developed for more widely studied platforms like Twitter. Naturally, ensuring privacy compliance has been paramount in our work. All teams collaborated closely to uphold privacy by design principles in our data management and scientific activities. This was further reinforced through continuous alignment aimed at striking the correct balance between the requirements for scientific insight and explainability, and the minimization of personal information collected.
From a scientific perspective, during the reporting period, AI-driven methods were developed for the analysis of textual, speech, audio, visual, and multimodal data. To facilitate integration with the Disinformation Warning System and the AI4TRUST platform, technology-providing partners exposed accessible APIs. Efforts in understanding the output of data analysis technologies led to the design of a system architecture for producing an overall estimate of the trustworthiness of a given claim or media item. Furthermore, Socio-behavioral and human-centered research has been conducted to inform the development of AI4TRUST models and the overall platform design and aim. To this end, focus groups, interviews, and ethnographic fieldwork have been conducted to inform the requirements for human-AI interaction, specifically focusing on aspects such as trust and explainability, as well as to map the specific requirements different future users of the AI4TRUST platform have.
From a technical perspective, we worked toward design and development of the AI4TRUST platform, suitable solutions are being provisioned to ensure ethical and privacy compliance. The technical design and implementation of the platform adopt a microservice-based approach to facilitate integration of different modules implemented throughout the project and enhance support for concurrent data processing and analysis models. Ethical, privacy, and data protection requirements were taken into account during this process. Following the consolidation of platform specifications, development kicked off alongside the integration of partner tools. A preliminary version of the backend and frontend, encompassing a subset of features, is slated for the pilot phase.
To facilitate the alignment and experimentation of pilots, we compiled a comprehensive collection of state-of-the-art fact-checking methodologies and best practices sourced directly from media professionals within the Consortium. These insights informed tailored high-level requirements for each pilot, ensuring alignment with their unique needs. Additionally, we established a structured work plan and guidelines for the piloting sessions, delineating activities across preparatory, execution, and post-pilot phases. For evaluating the pilots, we defined evaluation objectives, KPIs, and feedback mechanisms. To streamline upcoming pilot phases, we've initiated measures such as strategically assigning contact points for each pilot, crucial for maintaining regular updates. Moreover, we've developed a preliminary set of instructional materials outlining actions during training sessions and setting clear expectations for platform performance.
Moreover, this work addressed one of the project's major challenges: accessing social media data. Changes in policy for research access to Twitter/X API and the gradual discontinuation of Crowdtangle by Facebook/Meta necessitated diversifying data sources beyond the original plan. Consequently, alongside the continued access to Youtube, a novel methodology for collecting Telegram data was devised, customized to the topics outlined. Also, the progress associated with the Social Media Data Streams will be included in the upcoming phase.
This exploration of new data sources offers a fresh scientific perspective to be further explored, where we are currently evaluating novel applications of social network analysis utilizing perspectives distinct from those developed for more widely studied platforms like Twitter. Naturally, ensuring privacy compliance has been paramount in our work. All teams collaborated closely to uphold privacy by design principles in our data management and scientific activities. This was further reinforced through continuous alignment aimed at striking the correct balance between the requirements for scientific insight and explainability, and the minimization of personal information collected.
From a scientific perspective, during the reporting period, AI-driven methods were developed for the analysis of textual, speech, audio, visual, and multimodal data. To facilitate integration with the Disinformation Warning System and the AI4TRUST platform, technology-providing partners exposed accessible APIs. Efforts in understanding the output of data analysis technologies led to the design of a system architecture for producing an overall estimate of the trustworthiness of a given claim or media item. Furthermore, Socio-behavioral and human-centered research has been conducted to inform the development of AI4TRUST models and the overall platform design and aim. To this end, focus groups, interviews, and ethnographic fieldwork have been conducted to inform the requirements for human-AI interaction, specifically focusing on aspects such as trust and explainability, as well as to map the specific requirements different future users of the AI4TRUST platform have.
From a technical perspective, we worked toward design and development of the AI4TRUST platform, suitable solutions are being provisioned to ensure ethical and privacy compliance. The technical design and implementation of the platform adopt a microservice-based approach to facilitate integration of different modules implemented throughout the project and enhance support for concurrent data processing and analysis models. Ethical, privacy, and data protection requirements were taken into account during this process. Following the consolidation of platform specifications, development kicked off alongside the integration of partner tools. A preliminary version of the backend and frontend, encompassing a subset of features, is slated for the pilot phase.
To facilitate the alignment and experimentation of pilots, we compiled a comprehensive collection of state-of-the-art fact-checking methodologies and best practices sourced directly from media professionals within the Consortium. These insights informed tailored high-level requirements for each pilot, ensuring alignment with their unique needs. Additionally, we established a structured work plan and guidelines for the piloting sessions, delineating activities across preparatory, execution, and post-pilot phases. For evaluating the pilots, we defined evaluation objectives, KPIs, and feedback mechanisms. To streamline upcoming pilot phases, we've initiated measures such as strategically assigning contact points for each pilot, crucial for maintaining regular updates. Moreover, we've developed a preliminary set of instructional materials outlining actions during training sessions and setting clear expectations for platform performance.
We're actively developing a digital platform to gather and process data from social media and news aggregators. We've made significant strides in curating Ground Truth datasets and implementing text-based, audio, visual, and multimodal analysis methods to combat disinformation. Our efforts include developing multilingual models, speech-to-text technologies, and deepfake detection methods, surpassing State of the Art performance.
Additionally, we're building a disinformation warning system integrating various AI tools and designing a toolkit for AI information verification systems tailored to user communities' needs. Progress on the AI4TRUST Platform is ongoing, with the backend design outlined, technology framing established, and a user interface mock-up developed for the first pilot session. We've also devised a comprehensive methodology for validating AI4TRUST solutions, currently conducting evaluations for the first pilot session involving over 50 users and preparing for a larger-scale second pilot session.
Additionally, we're building a disinformation warning system integrating various AI tools and designing a toolkit for AI information verification systems tailored to user communities' needs. Progress on the AI4TRUST Platform is ongoing, with the backend design outlined, technology framing established, and a user interface mock-up developed for the first pilot session. We've also devised a comprehensive methodology for validating AI4TRUST solutions, currently conducting evaluations for the first pilot session involving over 50 users and preparing for a larger-scale second pilot session.