Year: 2020
Type: preprint
Abstract: Machine learning systems impact many stakeholders and groups of users, often disparately. Prior studies have reconciled conflicting user preferences by aggregating a high volume of manually labeled pairwise comparisons, but this technique may be costly or impractical. How can we lower the barrier to participation in algorithm design? Instead of creating a simplified labeling task for a crowd, we suggest collecting ranked decision-making heuristics from a focused sample of affected users. With empirical data from two use cases, we show that our weak learning approach, which requires little to no manual labeling, agrees with participants' pairwise choices nearly as often as fully supervised approaches.
Source: arXiv (Cornell University)
Authors: Steed, Ryan, Williams, Benjamin
Institution: Carnegie Mellon University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3040776857