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John T. Richards

13 papers hereh-index 143.2k citations24 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author10
  • last author2

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • cs.AI3
  • cs.CY3
  • cs.HC3
  • cs.SE2
  • cs.CL1
  • cs.LG1
same name
  • John T. Richards — 4 papers, h 3
  • John T. Richards — 3 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182022
most citedOne Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

270 citations · 752 across the 12 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.SE2020★ 3 cited

Quality Estimation & Interpretability for Code Translation

Mayank Agarwal, Kartik Talamadupula, Stephanie Houde +5

Recently, the automated translation of source code from one programming language to another by using automatic approaches inspired by Neural Machine Translation (NMT) methods for n…

cs.SE2020★ 2 cited

Towards evaluating and eliciting high-quality documentation for intelligent systems

David Piorkowski, Daniel González, John Richards +1

A vital component of trust and transparency in intelligent systems built on machine learning and artificial intelligence is the development of clear, understandable documentation.…

cs.HC2020★ 6 cited

A Methodology for Creating AI FactSheets

John Richards, David Piorkowski, Michael Hind +2

As AI models and services are used in a growing number of highstakes areas, a consensus is forming around the need for a clearer record of how these models and services are develop…

cs.CY2020★ 24 cited

Business (mis)Use Cases of Generative AI

Stephanie Houde, Vera Liao, Jacquelyn Martino +5

Generative AI is a class of machine learning technology that learns to generate new data from training data. While deep fakes and media-and art-related generative AI breakthroughs…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.