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AI4Dignity

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Collaborative AI Counters Hate

Online hate speech and disinformation have emerged as a major problem for democratic societies worldwide. Governments, companies and civil society groups have responded to this phenomenon by increasingly turning to Artificial Inte... see more

30/06/2022

LMU MUENCHEN

150K€

Project Budget: 150K€

Project leader

LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo H2020 notifico la concesión del proyecto The day 2022-06-30

ERC-2020-POC: Call for proposals for ERC Proof of... Scope:Objectives:

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Cerrada does 5 years

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characteristics of the participant

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

LMU MUENCHEN

150.00K€ | Leader

FINI GOLOSINAS ESPAÑA

586.48K€ | participant

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Project duration: 22 months Date Start: 2020-08-10
End date: 2022-06-30

Project leader

LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 150K€

participation deadline Sin fecha límite de participación.

Project description Online hate speech and disinformation have emerged as a major problem for democratic societies worldwide. Governments, companies and civil society groups have responded to this phenomenon by increasingly turning to Artificial Intelligence (AI) as a tool that can detect, decelerate and remove online extreme speech. However, such efforts confront many challenges. One of the key challenges is the quality, scope, and inclusivity of training data sets. The second challenge is the lack of procedural guidelines and frameworks that can bring cultural contextualization to these systems. Lack of cultural contextualization has resulted in false positives, over-application and systemic bias. The ongoing ERC project has identified the need for a global comparative framework in AI-assisted solutions in order to address cultural variation, since there is no catch-all algorithm that can work for different contexts. Following this, the proposed project will address major challenges facing AI assisted extreme speech moderation by developing an innovative solution of collaborative bottom-up coding. The model, AI4Dignity, moves beyond keyword-based detection systems by pioneering a community-based classification approach. It identifies fact-checkers as critical human interlocutors who can bring cultural contextualization to AI-assisted speech moderation in a meaningful and feasible manner. AI4Dignity will be a significant step towards setting procedural benchmarks to operationalize the human in the loop principle and bring inclusive training datasets for AI systems tackling urgent issues of digital hate and disinformation.

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