mlsanityData to 7 Oct 2026

Fairer Software Made Easier (using "Keys")

arXiv 2107.05088 ↗First posted 11 Jul 2021cs.SE cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Can we simplify explanations for software analytics? Maybe. Recent results show that systems often exhibit a "keys effect"; i.e. a few key features control the rest. Just to say the obvious, for systems controlled by a few keys, explanation and control is just a matter of running a handful of "what-if" queries across the keys. By exploiting the keys effect, it should be possible to dramatically simplify even complex explanations, such as those required for ethical AI systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 48 references that are in the map. First posted on arXiv 11 Jul 2021.

Builds on

12 of 48 references.

References · 12

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2020Making Fair ML Software using Trustworthy Explanationcited by 24
  4. 2019Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)cited by 13
  5. 2018Improving Vulnerability Inspection Efficiency Using Active Learningcited by 10
  6. 2020Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Modelscited by 9
  7. 2016"Sampling"' as a Baseline Optimizer for Search-based Software Engineeringcited by 8
  8. 2017Faster Discovery of Faster System Configurations with Spectral Learningcited by 6
  9. 2020Defect Reduction Planning (using TimeLIME)cited by 4
  10. 2017Learning Actionable Analytics from Multiple Software Projectscited by 3
  11. 2013Some Properties of Joint Probability Distributionscited by 1
  12. 2021Preference Discovery in Large Product Linescited by 1

Built on by

Not cited yet.