An Invitation to System-wide Algorithmic Fairness

Cortés, Efrén Cruz; Ghosh, Debashis

doi:10.1145/3375627.3375860

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, ABM responds to calls for a stronger integration of the social environment of ADM systems to grasp their impact appropriately. ABM has already been used to study the governance of socio-technical systems (Adelt et al, 2018), and Cruz Cortés and Ghosh (2019: 3), for example, apply ABM in the context of criminal recidivism risk for a “[s]ystematic analysis […] [which] implies analyzing the data generating process, the decision-making stage, and its consequences all under the same framework.” In conclusion, simulations are promising tools to assess macro-level outcomes of ADM applications from a social science perspective.…”

Section: Sources Of Bias and Social Impacts Along The Adm Processmentioning

confidence: 99%

Social impacts of algorithmic decision-making: A research agenda for the social sciences

et al. 2022

View full text Add to dashboard Cite

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making (ADM) may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM.

show abstract

Section: Sources Of Bias and Social Impacts Along The Adm Processmentioning

confidence: 99%