Add BibTeX of ACROCPoLis

docs/bibtex-acrocpolis-2023.md

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+
+```bibtex
+@inproceedings{AlerTubella.et.al-2023-FAccT,
+    author = {Aler Tubella, Andrea and Coelho Mollo, Dimitri and Dahlgren Lindstr\"{o}m, Adam and Devinney, Hannah and Dignum, Virginia and Ericson, Petter and Jonsson, Anna and Kampik, Timotheus and Lenaerts, Tom and Mendez, Julian Alfredo and Nieves, Juan Carlos},
+    title = { {ACROCPoLis: A Descriptive Framework for Making Sense of Fairness} },
+    year = {2023},
+    isbn = {9798400701924},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3593013.3594059},
+    doi = {10.1145/3593013.3594059},
+    abstract = {Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available. However, many of the fairness solutions proposed revolve around technical considerations and not the needs of and consequences for the most impacted communities. We therefore want to take the focus away from definitions and allow for the inclusion of societal and relational aspects to represent how the effects of AI systems impact and are experienced by individuals and social groups. In this paper, we do this by means of proposing the ACROCPoLis framework to represent allocation processes with a modeling emphasis on fairness aspects. The framework provides a shared vocabulary in which the factors relevant to fairness assessments for different situations and procedures are made explicit, as well as their interrelationships. This enables us to compare analogous situations, to highlight the differences in dissimilar situations, and to capture differing interpretations of the same situation by different stakeholders.},
+    booktitle = {Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency},
+    pages = {1014--1025},
+    numpages = {12},
+    keywords = {socio-technical processes, social impact of AI, Algorithmic fairness, responsible AI},
+    location = {Chicago, IL, USA},
+    series = {FAccT '23}
+}
+```
+
+