2-year Postdoc position in Natural Language Processing on Incorporating Demographic Factors into Natural Language Processing Models Funded by ERC Starting grant INTEGRATOR https://milanlproc.github.io/project/integrator/ Start: from September 2022 Dirk Hovy, Bocconi University and MilanLP group

Posting: https://bit.ly/3tk5UR6 https://bit.ly/3tk5UR6 Application Form: https://bit.ly/3Q5j7qv https://bit.ly/3Q5j7qv

Project: The goal of the INTEGRATOR project 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.

Successful candidates will work actively on novel directions in NLP, machine learning, and neural networks for representation learning, and transfer learning in various languages, and collaborate closely with Prof. Hovy as well as the lab. The candidates will innovate in both NLP and social sciences.

Successful candidates will have to prove * excellent programming skills in Python (additional languages like C++, R, Julia are a plus), * knowledge of current neural network models for transfer and few-shot learning and * implementation tools for neural networks (e.g. PyTorch, Tensorflow, etc.) * prove strong track record in top-tier venues in the field of NLP/ Machine Learning. * fluency in spoken and written English. Knowledge of Italian is NOT a requirement.

INFORMATION

* Application deadline: July 7 2022

* Skype interviews will take place during July 2022

* Starting date: from September 2022, or any time thereafter

* Duration: 2 years, 1 year extension possible

* Salary: 42k EUR gross per annum (median salary in Milan is 37k EUR). Applicants from outside Italy may qualify for a researcher taxation scheme with reduced tax load.

HOW TO APPLY

The official application must be sent via https://bit.ly/3Q5j7qv https://bit.ly/3Q5j7qv

Informal enquiries can be sent by email to Dirk Hovy (dirk.hovy@unibocconi.it mailto:dirk.hovy@unibocconi.it).

You can find more information about the call here: https://bit.ly/3tk5UR6 https://bit.ly/3tk5UR6