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FairER

Financiado

Fairness in Language Models: Equally right for the right reasons

Most of us use technology related to natural language processing (NLP) such as Google Search or virtual assistants in phones and other devices on a daily basis. Large-scale pre-trained language models hereby play a crucial role as... ver más

31/08/2024

UCPH

215K€

Presupuesto del proyecto: 215K€

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-08-31

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021

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1 Participantes

UCPH

214.93K€ | Lider

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 26 meses Fecha Inicio: 2022-06-16
Fecha Fin: 2024-08-31

Líder del proyecto

KOBENHAVNS UNIVERSITET

TRL 4-5

Presupuesto del proyecto 215K€

Descripción del proyecto Most of us use technology related to natural language processing (NLP) such as Google Search or virtual assistants in phones and other devices on a daily basis. Large-scale pre-trained language models hereby play a crucial role as they often form the basis of those technologies. Those models are trained on a large amount of training data (e.g. the entire English Wikipedia and the Brown corpus) which makes it impossible to curate the training corpus and potential stereotypes and biases will be implemented into the model, often without researchers noticing. This can lead to problematic and unfair behaviour towards certain demographics, often those who already suffer from implicit biases in society. With FairER, I aim to get a deeper understanding of the inner workings of these language models. In particular, I want to investigate how well their solution strategies align with those of humans and whether this depends on certain demographic attributes such as gender, race, age but also reading abilities and level of education. I will also probe those language models for fairness and inclusiveness, i.e., find out whether the performance of an NLP application depends on demographic attributes of the user. Furthermore, I will conduct this project in a multilingual setting and apply interpretability methods to better understand the rationale behind a model’s decision. The main impact of FairER will be a better understanding of how language models treat different demographics. These insights will help to improve the fairness and inclusiveness of NLP applications. Furthermore, the datasets I will record and publish along with the code will encourage other researchers to replicate my findings and continue this line of research. Ultimately, this project will have both a scientific and societal impact on the NLP community and users of NLP applications.

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