Challenges and Opportunities in Using Data Science for Homelessness Service Provision

Author:

Chelmis Charalampos1,Qi Wenting1,Lee Wonhyung1

Affiliation:

1. University at Albany State University of New York

Publisher

ACM

Reference56 articles.

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2. Data and algorithmic bias in the web

3. Solon Barocas Moritz Hardt and Arvind Narayanan. 2017. Fairness in machine learning. NIPS Tutorial 1(2017). Solon Barocas Moritz Hardt and Arvind Narayanan. 2017. Fairness in machine learning. NIPS Tutorial 1(2017).

4. Aggression-Related Hostility Bias and Social Problem-Solving Deficits in Adult Males With Mental Retardation

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2. A Human-Centered Review of Algorithms in Homelessness Research;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

3. “Who is the right homeless client?”: Values in Algorithmic Homelessness Service Provision and Machine Learning Research;Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems;2023-04-19

4. Label Denoising and Counterfactual Explanation with A Plug and Play Framework;2022 IEEE International Conference on Big Data (Big Data);2022-12-17

5. A feeling for the data: How government and nonprofit stakeholders negotiate value conflicts in data science approaches to ending homelessness;Journal of the Association for Information Science and Technology;2022-10-05

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