Disparate impact

FAIR: Fair Adversarial Instance Re-weighting
With growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning …
Andrija Petrović, Mladen Nikolić, Sandro Radovanović, Boris Delibašić, Miloš Jovanović
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FairDEA - Removing Disparate Impact from Efficiency Scores
Achieving fairness in algorithmic decision-making tools is an issue constantly gaining in need and popularity. Today, unfair decisions …
Sandro Radovanović, Gordana Savić, Boris Delibašić, Milija Suknović
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Eliminating Disparate Impact in MCDM: The case of TOPSIS
In today’s business, decision-making is heavily dependent on algorithms. Algorithms may originate from operational research, machine …
Sandro Radovanović, Andrija Petrović, Boris Delibašić, Milija Suknović
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A fair classifier chain for multi-label bank marketing strategy classification
Recently, the usage of machine learning algorithms is subject to discussion from a legal and ethical point of view. Unwanted …
Sandro Radovanović, Andrija Petrović, Boris Delibašić, Milija Suknović
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Investigating Oversampling Techniques for Fair Machine Learning Models
Applying machine learning in real-world applications may have various implications on companies, but individuals as well. Besides …
Sanja Rančić, Sandro Radovanović, Boris Delibašić
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Enabling Equal Opportunity in Logistic Regression Algorithm
Research Question - This paper aims at adjusting the logistic regression algorithm to mitigate unwanted discrimination shown towards …
Sandro Radovanović, Marko Ivić
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Enforcing fairness in logistic regression algorithm
Machine learning has been subject to discussion from the legal and ethical points of view in recent years. Automation of the …
Sandro Radovanović, Andrija Petrović, Boris Delibašić, Milija Suknović
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