Socially Responsible NLP
Yulia Tsvetkov, Vinodkumar Prabhakaran, Rob Voigt · 2018
Socially Responsible NLPAs language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies.This tutorial will provide an overview of real-world applications of language technologies and the potential ethical implications associated with them.We will discuss philosophical foundations of ethical research along with state-of-the art techniques.Discussion topics include: ■ Philosophical foundations: what is ethics, history, medical and psychological experiments, IRB and human subjects, ethical decision making.■ Misrepresentation and bias: algorithms to identify biases in models and data and adversarial approaches to debiasing.■ Civility in communication: monitoring explicit abusive language and implicit microaggression.Through this tutorial, we intend to provide the NLP researcher with an overview of tools to ensure that the data, algorithms, and models that they build are socially responsible.These tools will include a checklist of common pitfalls that one should avoid (e.g., demographic bias in data collection), as well as methods to adequately mitigate these issues (e.g., adjusting sampling rates or debiasing through regularization).