Project acronym and title

INTEGRATOR – Incorporating Demographic Factors into Natural Language Processing Models

Project summary

INTEGRATOR introduces demographic factors into language processing systems, which will improve algorithmic performance, avoid racism, sexism, and ageism, and open up new applications. What if I wrote that “winning an ERC Grant, Dirk Hovy got a sick result?”. Those familiar with the use of “sick” as a synonym for “great” or “awesome” among teenagers would think that Bocconi Knowledge hired a very young writer (or someone posing as such). The rest would think I went crazy. Current artificial intelligence-based language systems wouldn’t have a clue. “Natural language processing (NLP) technologies,” Prof. Hovy says, “fail to account for demographics both in understanding language and in generating it. And this failure prevents us from reaching human-like performance. It limits possible future applications and it introduces systematic bias against underrepresented demographic groups”.

Project description

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.




This website area is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme
(Grant agreement No. 949944)

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