From: Big Data and discrimination: perils, promises and solutions. A systematic review
Suggested solutions | Paper references |
---|---|
1. Computer science and technical solutions | |
 1.1. Pre-processing | Kamiran and Calders 2012 [42], Hajian and Domingo-Ferrer 2013 [33], Kamiran et al. 2013 [43], Hajian et al. 2014 [32] |
 1.2. In-processing | Calders and Verwer 2010 [17], Pope and Sydnor 2011 [66], Kamiran et al. 2013 [43], Zliobaite and Custers 2016 [95], Kroll et al. 2017 [45] |
 1.3. Post-processing | Hajian et al. 2015 [34] |
 1.4.Mixed methods | d'Alessandro et al. 2017 [25] |
 1.5. Implementation of transparency | Hildebrandt and Koops 2010 [35], Schermer 2011 [73], Citron and Pasquale 2014 [21], Kroll et al. 2017 [45] |
 1.6. Privacy preserving strategies | |
 1.7. Exploratory fairness analysis | Veale and Binns 2017 [84] |
2. Legal solutions | Hildebrandt and Koops 2010 [35], Hoffman 2010 [37], Citron and Pasquale 2014 [21], Peppet 2014 [62], Hirsch 2015 [36], Kuempel 2016 [46], Hoffman 2017 [38] |
3. Human based solutions | |
 3.1. Human in the loop | Zarsky 2014 [93], Berendt and Preibusch 2017 [11], d'Alessandro et al. 2017 [25] |
 3.2. Third parties | |
 3.3. Multidisciplinary involvement | Cohen et al. 2014 [22], Taylor 2016 [77, 78], Taylor 2017 [79] |
 3.4. Education | |
 3.5. Implementing EHR flexibility | Hoffman 2010 [37] |