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Table 4 Causes of discrimination in data analytics

From: Big Data and discrimination: perils, promises and solutions. A systematic review

Causes of discrimination Related articles
1. Algorithmic causes
 1.1. Definition of the target variable Barocas and Selbst 2016 [8], d'Alessandro et al. 2017 [25]
 1.2. Data issues Training data (Historically biased data sets) Kamiran and Calders 2012 [42], Barocas and Selbst 2016 [8], Brayne 2017 [14], d'Alessandro et al. 2017 [25]
 1.3. Data issues Training data (manual assignment of class labels) Barocas and Selbst 2016 [8], d'Alessandro et al. 2017 [25]
 1.4. Data issues Data collection (Overrepresentation and underrepresentation) Barocas and Selbst 2016 [8], d'Alessandro et al. 2017 [25]
 1.5. Proxies Schermer 2011 [73], Kamiran and Calders 2012 [42], Barocas and Selbst 2016 [8], Zliobaite and Custers 2016 [95], d'Alessandro et al. 2017 [25]
 1.6. Feedback loop Mantelero 2016 [54], Brayne 2017 [14], d'Alessandro et al. 2017 [25]
 1.7. Overfitting Kamiran and Calders 2012 [42], Mantelero 2016 [54]
 1.8. Feature selection Barocas and Selbst 2016  [8]
 1.9. Cost function Error by omission d'Alessandro et al. 2017 [25]
 1.10 Masking Proxies Peppet 2014 [ 61], Zarsky 2014 [93], Barocas and Selbst 2016 [8], Zliobaite and Custers 2016 [95], Kroll et al. 2017 [45]
2. Digital divide
 2.1. Skills Boyd and Crawford 2012 [12], Casanas i Comabella and Wanat 2015[18]
 2.2. Resources Barocas and Selbst 2016 [8], Pak et al. 2017 [60]
 2.3. Geographical location Casanas i Comabella and Wanat 2015 [18], Barocas and Selbst 2016 [8], Pak et al. 2017 [60]
 2.4. Age Casanas i Comabella and Wanat 2015 [18]
 2.5. Income Barocas and Selbst 2016 [8], Pak et al. 2017 [60]
 2.6 Gender Boyd and Crawford 2012 [12]
 2.7. Education Boyd and Crawford 2012 [12]
 2.8 Race Bakken and Reame 2016 [6], Sharon 2016 [74]
3. Data linkage Susewind 2015 [76], Cato et al. 2016 [19], Zarate et al. 2016 [91], Ploug and Holm 2017 [64]