×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

User: a-rittatore

FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines. (arXiv:2307.15475v1 [cs.HC])

Click here to flash read.

Even though machine learning (ML) pipelines affect an increasing array of
stakeholders, there is little work on how input from stakeholders is recorded
and incorporated. We propose FeedbackLogs, addenda to existing documentation of
ML pipelines, to track the input of multiple stakeholders. Each log records
important details about the feedback collection process, the feedback itself,
and how the feedback is used to update the ML pipeline. In this paper, we
introduce and formalise a process for collecting a FeedbackLog. We also provide
concrete use cases where FeedbackLogs can be employed as evidence for
algorithmic auditing and as a tool to record updates based on stakeholder
feedback.

Click here to read this post out
ID: 301701; Unique Viewers: 0
Voters: 0
Latest Change: July 31, 2023, 7:31 a.m. Changes:
Dictionaries:
Words:
Spaces:
Comments:
Newcom
<0:100>