Update human-AI track

_tracks/05_nlp_and_ir.md

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 ---
-layout: default
-track-id: 8
-title: Natural Language Processing and Information Retrieval
-leader: Claudia Hauff
+layout: track
+track-id: 5
+title: Human-AI Decision Making
+leader: Ujwali Gadiraju
+phd: Sara Salimzadeh
 ---
 
-# Track 8: Natural Language Processing and Information Retrieval
-
-RegTech is a subset of FinTech aimed at delivering regulatory requirements more efficiently and effectively than existing capabilities. This particularly relates to Know Your Customer (KYC) and Customer Due Diligence (CDD) requirements set in place to prevent banks from being used, intentionally or unintentionally, by criminal elements for money laundering activities.
+Application of artificial intelligence in high stakes domains centers around human-AI decision making in which human stakeholders interact with AI.
+Examples in the financial sector include Know Your Customer (KYC) and Customer Due Dilligence (CDD) processes set in place to prevent banks from being used, intentionally or unintentionally, by criminal elements for money laundering activities.
 
 Current advances in Natural Language Processing (NLP) open up new possibilities to strengthen KYC and CDD activities.
 Written documents from regulators describe the constraints and requirements banks and their customers must adhere to.
-The assessment of compliance is based on a series of data sources, which can be structured (stored in databases) or unstructured, in which case it is, again, written text.
-The route that will be explored in the context of AFR is to use NLP to distill the requirements from regulatory documents automatically. With that in place, customer-related data, can be used to assess compliance. Here, again, for the unstructured parts, NLP techniques will be applied to distill elements that can be used for formal compliance evaluation.
+The assessment of compliance is based on a series of data sources, which can be structured (stored in databases, queried by SQL) or unstructured, in which case it is, again, written plain text.
+The route explored in the context of AFR is to use NLP to distill the requirements from regulatory documents automatically. With that in place, customer-related data, can be used to assess compliance. Here, again, for the unstructured parts, NLP techniques need be applied to distill elements that can be used for formal compliance evaluation.
+
 
-One line of research in this track will focus on applying existing NLP techniques to the regulatory domain.
-Furthermore, we anticipate that current NLP techniques may have to be adjusted to the regulatory domain.
-Modern NLP techniques are largely statistical in nature, being trained on large bodies of existing text.
-We anticipate that regulatory documents will have unique characteristics, requiring tailor-made NLP models.
-ING's historic data of already conducted KYC and CDD assessments will provide the starting point for such tailor-made NLP models.
+## Selected Publications
 
--  Arner, Douglas W. and Barberis, Janos Nathan and Buckley, Ross P., FinTech, RegTech and the Reconceptualization of Financial Regulation. Northwestern Journal of International Law & Business, 2017.
+- Sara Salimzadeh, Gaole He, Ujwal Gadiraju: A Missing Piece in the Puzzle: Considering the Role of Task Complexity in Human-AI Decision Making. UMAP 2023: 215-227 ([preprint](https://research.tudelft.nl/en/publications/a-missing-piece-in-the-puzzle-considering-the-role-of-task-comple)).
 
-- Felipe Moraes, Kilian Grashoff, Claudia Hauff: On the impact of group size on collaborative search effectiveness. Inf. Retr. Journal 22(5): 476-498 (2019)
+- Sara Salimzadeh, Ujwal Gadiraju, Claudia Hauff, Arie van Deursen: Exploring the Feasibility of Crowd-Powered Decomposition of Complex User Questions in Text-to-SQL Tasks. HT 2022: 154-165 ([preprint](https://research.tudelft.nl/en/publications/exploring-the-feasibility-of-crowd-powered-decomposition-of-compl))
 
-**Track leader:** {{ page.leader }}
+- Sara Salimzadeh, David Maxwell, Claudia Hauff: The Impact of Entity Cards on Learning-Oriented Search Tasks. ICTIR 2021: 63-72 ([preprint](https://research.tudelft.nl/en/publications/the-impact-of-entity-cards-on-learning-oriented-search-tasks)).