INTEGRATOR
Financiado
Incorporating Demographic Factors into Natural Language Processing Models
The goal of INTEGRATOR is to develop novel data sets, theories, and algorithms to incorporate demographic factors into language technology. This will improve performance of existing tools for all users, reduce demographic bias, an...
The goal of INTEGRATOR is to develop novel data sets, theories, and algorithms to incorporate demographic factors into language technology. This will improve performance of existing tools for all users, reduce demographic bias, and enable completely new applications.
Language reflects demographic factors like our age, gender, etc. People actively use this information to make inferences, but current language technology (NLP) fails to account for demographics, both in language understanding (e.g., sentiment analysis) and generation (e.g., chatbots). This failure prevents us from reaching human-like performance, limits possible future applications, and introduces systematic bias against underrepresented demographic groups.
Solving demographic bias is one of the greatest challenges for current language technology. Failing to do so will limit the field and harm public trust in it. Bias in AI systems recently emerged as a severe problem for privacy, fairness, and ethics of AI. It is especially prevalent in language technology, due to language's rich demographic information. Since NLP is ubiquitous (translation, search, personal assistants, etc.), demographically biased models creates uneven access to vital technology.
Despite increased interest in demographics in NLP, there are no concerted efforts to integrate it: no theory, data sets, or algorithmic solutions. INTEGRATOR will address these by identifying which demographic factors affect NLP systems, devising a bias taxonomy and metrics, and creating new data. These will enable us to use transfer and reinforcement learning methods to build demographically aware input representations and systems that incorporate demographics to improve performance and reduce bias.
Demographically aware NLP will lead to high-performing, fair systems for text analysis and generation. This ground-breaking research advances our understanding of NLP, algorithmic fairness, and bias in AI, and creates new research resources and avenues.
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Duración del proyecto: 69 meses
Fecha Inicio: 2020-11-13
Fecha Fin: 2026-08-31
Fecha Fin: 2026-08-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2020-11-13
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2020-STG:
ERC STARTING GRANTS
Cerrada
hace 5 años
Presupuesto
El presupuesto total del proyecto asciende a
1M€
Líder del proyecto
UNIVERSITA COMMERCIALE LUIGI BOCCONI
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
180.40K€ |
Participante