id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajis02-3835	Nadeem, Ayesha; Marjanovic, Olivera ; Abedin, Babak 	Gender bias in AI-based decision-making systems: a systematic literature review	2022	34	.pdf	application/pdf	13791	667	44	Australasian Journal of Information Systems Nadeem, Marjanovic & Abedin 2022, Vol 26, Selected Papers from ACIS 2020 Gender Bias in AI 14 Removing proxy variables of protected attributes and ensuring fair datasets from all groups and members of a community i.e., diverse and inclusive datasets, are reported to be effective in mitigating gender bias in the design and implementation of AI systems (Bellamy et al., 2018; Feuerriegel et al., 2020; Grari et al., 2020; Hayes et al., 2020; Miron et al., 2020; Veale & Binns, 2017). Recognising the need for knowledge sharing, researchers propose collaborative ethical AI online platforms for all stakeholders, which would permit demographically diverse organisations to collaborate and share knowledge regarding the appropriate and practical strategies to promote fairness in AI systems (Soleimani et al., 2021; Veale & Binns, 2017).	cache/ajis02-3835.pdf	txt/ajis02-3835.txt