Schedule:

2:00 – 2:10 pm: 

Introduction

Mike Schneider

2:10 – 3:10 pm:

Title TBA

Gaia Belardinelli, Stanford University

Abstract coming soon

3:10 – 3:20 pm:

Break

3:20 – 4:20 pm:

Title TBA

Will Stafford, Kansas State University

Abstract coming soon

4:20 – 4:30 pm:

Break

4:30pm – 5:30 pm:

Data quality in the machine learning age

Kino Zhao, Simon Fraser University

Data quality, or the lack thereof, is often blamed for inferential failures. The garbage in, garbage out (GIGO) principle serves to remind us that no amount of fancy mathematical footwork can save a good model from bad data. Yet there exists remarkably little consensus on the precise nature of good data. In this talk, I discuss several popular accounts of data quality -- notably representationalism, contextualism, and fit-for-purpose accounts -- and argue that all of them make assumptions that are not generally true in the context of opaque ML training. I then revisit the question "what is the point of data quality?" and argue that the inferential opacity typical of ML algorithms provide new reasons to understand data quality from the producer's perspective -- that is, independently of data's ability to support inference.