{"type":"ScholarlyArticle","title":"Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification","authors":[{"name":"Nicolò Tamagnone","slug":"nicolo-tamagnone","given":"Nicolò","family":"Tamagnone","affiliation":"Data Friendly Space; {nico ,; ISI Foundation , Turin , Italy"},{"name":"Selim Fekih","slug":"selim-fekih","given":"Selim","family":"Fekih","affiliation":"Data Friendly Space; {nico ,"},{"name":"Ximena Contla","slug":"ximena-contla","given":"Ximena","family":"Contla","affiliation":"Data Friendly Space; {nico ,"},{"name":"Nayid Orozco","slug":"nayid-orozco","given":"Nayid","family":"Orozco","affiliation":"Data Friendly Space; {nico ,"},{"name":"Navid Rekabsaz","slug":"navid-rekabsaz","given":"Navid","family":"Rekabsaz","affiliation":"Johannes Kepler University Linz , LIT AI Lab , Austria"}],"abstract":"Accurate and rapid situation analysis during humanitarian crises is critical to delivering humanitarian aid efficiently and is fundamental to humanitarian imperatives and the Leave No One Behind (LNOB) principle. This data analysis can highly benefit from language processing systems, e.g., by classifying the text data according to a humanitarian ontology. However, approaching this by simply fine-tuning a generic large language model (LLM) involves considerable practical and ethical issues, particularly the lack of effectiveness on data-sparse and complex subdomains, and the encoding of societal biases and unwanted associations. In this work, we aim to provide an effective and ethically-aware system for humanitarian data analysis. We approach this by (1) introducing a novel architecture adjusted to the humanitarian analysis framework, (2) creating and releasing a novel humanitarian-specific LLM called HumBert, and (3) proposing a systematic way to measure and mitigate biases. Our experiments' results show the better performance of our approach on zero-shot and full-training settings in comparison with strong baseline models, while also revealing the existence of biases in the resulting LLMs. Utilizing a targeted counterfactual data augmentation approach, we significantly reduce these biases without compromising performance.","doi":"10.24963/ijcai.2023/690","url":"https://ijcai.org/proceedings/2023/690","pdf":"https://ijcai.org/proceedings/2023/0690.pdf","markdown":"https://ijcai.org/proceedings/2023/690.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/690","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":690,"pages":{"first":6219,"last":6227},"keywords":["AI for Good: AI Ethics, Trust, Fairness","AI for Good: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Adel and Wang, 2020] Ghadah Adel and Yuping Wang. 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