TY  - JOUR
UR  - https://www.scopus.com/pages/publications/85208026453?origin=resultslist
A1  - Mamman, Hussaini
A1  - Basri, Shuib
A1  - Balogun, Abdullateef Oluwagbemiga
A1  - Imam, Abdullahi Abubakar
A1  - Kumar, Ganesh
A1  - Capretz, Luiz Fernando
KW  - Adversarial machine learning; Decision making; Economic and social effects; Machine learning; Real time systems; Counterfactuals; Critical domain; Decisions makings; Dynamic settings; Further works; Human oversight; Human-in-the-loop; Machine-learning; On-the-fly; Real- time
PB  - Springer Science and Business Media Deutschland GmbH
EP  -  311
SN  - 23673370
SP  - 300 
Y1  - 2024///
N1  - Cited by: 0
JF  - Lecture Notes in Networks and Systems
AV  - none
N2  - The widespread adoption of ML systems across critical domains like hiring, finance, and healthcare raises growing concerns about their potential for discriminatory decision-making based on protected attributes. While efforts to ensure fairness during development are crucial, they leave deployed ML systems vulnerable to potentially exhibit discrimination during their operations. To address this gap, we propose a novel framework for on-the-fly tracking and correction of discrimination in deployed ML systems. Leveraging counterfactual explanations, the framework continuously monitors the predictions made by an ML system and flags discriminatory outcomes. When flagged, post-hoc explanations related to the original prediction and the counterfactual alternatives are presented to a human reviewer for real-time intervention. This human-in-the-loop approach empowers reviewers to accept or override the ML system decision, enabling fair and responsible ML operation under dynamic settings. While further work is needed for validation and refinement, this framework offers a promising avenue for mitigating discrimination and building trust in ML systems deployed in a wide range of domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
VL  - 1119 L
ID  - scholars20557
TI  - Unbiasing on the Fly: Explanation-Guided Human Oversight of Machine Learning Decisions
ER  -