| Title | Algorithmic Accountability in Context. Socio-Technical Perspectives on Structural Causal Models |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Authors | Poechhacker, Nikolaus, Kacianka, Severin |
| Journal | Frontiers in Big Data |
| Volume | 3 |
| Pagination | 55 |
| ISSN | 2624-909X |
| Keywords | causal models, PIRE, Societal Design |
| Abstract | The increasing use of automated decision making (ADM) and machine learning sparked an ongoing discussion about algorithmic accountability. Within computer science, a new form of producing accountability has been discussed recently: causality as an expression of algorithmic accountability, formalized using structural causal models (SCMs). However, causality itself is a concept that needs further exploration. Therefore, in this contribution we confront ideas of SCMs with insights from social theory, more explicitly pragmatism, and argue that formal expressions of causality must always be seen in the context of the social system in which they are applied. This results in the formulation of further research questions and directions. |
| URL | https://www.frontiersin.org/article/10.3389/fdata.2020.519957 |
| DOI | 10.3389/fdata.2020.519957 |
| Citation Key | 10.3389/fdata.2020.519957 |
Algorithmic Accountability in Context. Socio-Technical Perspectives on Structural Causal Models