Fairness and Abstraction in Sociotechnical Systems

resource thumbnail

Remove from Bookmarks

Do you really want to remove?
This action cannot be undone. Choose 'Cancel' to stop and go back.
Cancel
Ratings: 0
  • Which text to add here??

Added by Martin on 2025-01-06 17:07

» Viewed 72 times
» Favorited by 0 user(s)
» 0 Comments
» This resource has public visibility

Holder of Rights:

License: unknown

Creator(s): Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, Janet Vertesi

Description:
A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science - such as abstraction and modular design - are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.

Add to Collection

You don't have any collections yet. Click here to create your first collection!

Okay

Share to Group

You don't have any group you can share this resource with: the resource is already shared to all groups you are member in. Click here to see available groups!

Okay

Create QR Code

Please select the URI for the QR Code:




Create Cancel

Tags

sort: alphabetically • by frequency
use blanks to separate tags

Comments

Fairness and Abstraction in Sociotechnical Systems A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science - such as abstraction and modular design - are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones. Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, Janet Vertesi