Ashia Wilson, Kathleen Creel, Shomik Jain
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| shomikj/randomization_for_fairness | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| experiment | Ran | shomikj/randomization_for_fairness/claims_uncertain/randomization_proposals.py pointer only (licence: NONE) · get_code("d3e5b0ffd95f7890") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.08592")
get_code_for_paper("2404.08592")
have("2404.08592")
Connect an agent — have() is free.