Table 1.

Distribution of papers collected for this review based on the focus of paper.

Focus of papers	Paper count (percent)	Sub-themes
Gender bias analysis	48 (39.7%)	AI [4], NLP [14], Facial Data Analysis [8], Legal & Ethical Implication [9], Recommender Systems [3], Healthcare & Medicine [2], Policy & Government [2], Search & Ranking [2], Marketing [1], Automated Systems [1], Automated Recruitment [1]
Mitigation methods	34 (28.1%)	NLP [14], Facial Data Analysis [8], Recommender Systems [3], Classification [2], Legal & Ethical Implication [1], Marketing [1], NA [1]
Detection methods	19 (15.7%)	NLP [11], Facial Data Analysis [3], Automated Recruitment [2], Individual Fairness [1], Unwanted Associations [1]
User studies	8 (6.6%)	NLP [2], Facial Data Analysis [1], Legal & Ethical Implication [1], Search & Ranking [1], Recommender Systems [1], Others [2]
Case studies	5 (4.1%)	NLP [2], Legal & Ethical Implication [1], Classification [1], Recommender Systems [1]
Literature reviews	7 (5.8%)	NLP [3], Facial Data Analysis [1], Healthcare & Medicine [1], Search & Ranking [1], Bias Mitigation Frameworks [1]