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  "author": "Andrija Petrovi\u0107",
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  "author": "Andrija Petrovi\u0107",
            
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  "doi": "10.57702/89ahsu4z",
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  "doi": "10.57702/89ahsu4z",
            
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Mladen Nikoli\u0107",
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      "extra_author": "Mladen Nikoli\u0107",
            
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    },
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    {
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      "extra_author": "Sandro Radovanovi\u0107",
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      "extra_author": "Sandro Radovanovi\u0107",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Boris Delibasi\u0107",
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      "extra_author": "Boris Delibasi\u0107",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Milo\u0161 Jovanovi\u0107",
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      "extra_author": "Milo\u0161 Jovanovi\u0107",
            
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      "orcid": ""
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      "description": "",
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      "name": "fairness-in-machine-learning",
            
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      "title": "Fairness in machine learning"
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      "title": "Fairness in machine learning"
            
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  "name": "fair-adversarial-instance-re-weighting--fair-",
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  "notes": "The proposed framework was tested on four datasets, three 
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  "notes": "The proposed framework was tested on four datasets, three 
            
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of which are commonly used benchmarks. Two datasets (German credit and 
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of which are commonly used benchmarks. Two datasets (German credit and 
            
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Adult income) come from the UCI ML repository [44]. To our knowledge 
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Adult income) come from the UCI ML repository [44]. To our knowledge 
            
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the Hospital readmission dataset was used in this paper for the 
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the Hospital readmission dataset was used in this paper for the 
            
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\ufb01rst time in the context of fairness.",
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\ufb01rst time in the context of fairness.",
            
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  "title": "Fair Adversarial Instance Re-weighting (FAIR)",
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}