Ethics in Natural Language Processing: Addressing Bias and Misinformation

This lecture discusses the ethical considerations in natural language processing (NLP), focusing on the implications of bias, toxicity, and misinformation. It begins with an overview of the ethical landscape, emphasizing that NLP systems do not operate in isolation but are influenced by human communication and societal norms. The instructor highlights the case of Microsoft's Tay chatbot, which quickly learned and propagated toxic behavior due to its design flaws. The lecture then explores various forms of bias present in language models, including racial and gender stereotypes, and how these biases can be amplified through data and model training. The discussion extends to the ethical responsibilities of developers in creating NLP systems that avoid harmful outputs and respect privacy. The lecture concludes with a critical examination of the potential harms of NLP technologies, including disinformation and the ethical implications of using such systems in sensitive contexts, urging practitioners to consider the broader societal impact of their work.