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Online Handbook of Argumentation for AI: Volume 4
This volume contains revised versions of the papers selected for the fourth volume of the Online Handbook of Argumentation for AI (OHAAI).
Identifying Reasons for Bias: An Argumentation-Based Approach
As algorithmic decision-making systems become more prevalent in society, ensuring the fairness of these systems is becoming increasingly important.
Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations
Bias mitigation methods for binary classification decision-making systems have been widely researched due to the ever-growing importance of designing fair machine learning processes that are impartial and do not discriminate against individuals or groups based on protected personal characteristics.
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