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Table 6 Barriers to fair data analytics

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

Obstacles to fair data analytics Paper references
1. Black box Hildebrandt and Koops 2010 [35], Ruggieri et al. 2010 [71], Schermer 2011 [73], Berendt and Preibusch 2014 [10], Citron and Pasquale 2014 [21], Cohen et al. 2014 [22], Leese 2014 [48], Zarsky 2014 [93], Kennedy and Moss 2015 [44], Newell and Marabelli 2015 [58], Turow, McGuigan et al. 2015 [81], Mantelero 2016 [54], Zarsky 2016 [92], Brannon 2017 [13], Brayne 2017 [14], d'Alessandro et al. 2017 [25], Kroll et al. 2017 [45], Taylor 2017 [79]
2. Human bias Boyd and Crawford 2012 [12], Kamiran and Calders 2012 [42], Citron and Pasquale 2014 [21], Zarsky 2014 [93], Ajana 2015 [1], Ajunwa et al. 2016 [2], Barocas and Selbst 2016 [8], Berendt and Preibusch 2017 [11], Brayne 2017 [14], d'Alessandro et al. 2017 [25], Veale and Binns 2017 [84], Voigt 2017 [85]
3. Conceptual challenges de Vries 2010 [27], Hoffman 2010 [37], Lerman 2013 [49], Leese 2014 [48], Zarsky 2014 [93], Ajana 2015 [1], Hirsch 2015 [36], MacDonnell 2015 [53], Barocas and Selbst 2016 [8], Kuempel 2016 [46], Mantelero 2016 [54], Francis and Francis 2017 [30], Hoffman 2017 [38], Kroll et al. 2017 [45], Taylor 2017 [79]
4. Inadequate legislation Hildebrandt and Koops 2010 [35], Hoffman 2010 [37], Ruggieri et al. 2010 [71], Lerman 2013 [49], Citron and Pasquale 2014 [21], Peppet 2014 [62], Barocas and Selbst 2016 [8], Kuempel 2016 [46], Zliobaite and Custers 2016 [95], Hoffman 2017 [38], Zliobaite 2017 [94]