mlsanityData to 13 Oct 2026

Who Wrote this? How Smart Replies Impact Language and Agency in the Workplace

arXiv 2210.06470 ↗First posted 7 Oct 2022cs.HC cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

AI-mediated communication is designed to help us do our work more quickly and efficiently. But does it come at a cost? This study uses smart replies (SRs) to show how AI influences humans without any intent on the part of the developer - the very use of AI is sufficient. I propose a loss of agency theory as a viable approach for studying the impact of AI on human agency. This theory focusses on the transfer of agency that is forced by circumstances (such as time pressure), human weaknesses (such as complacency), and conceptual priming. Mixed methods involving a crowdsourced experiment test that theory. The quantitative results reveal that machine agency affects the content we author and the behavior we generate. But it is a non-zero-sum game. The transfers between human and machine agency are fluid; they complement, replace, and reinforce each other at the same time.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 10 of its 40 references that are in the map. First posted on arXiv 7 Oct 2022.

Builds on

10 of 40 references.

References · 10

  1. 2016Semantics derived automatically from language corpora contain human-like biasescited by 895
  2. 2017Efficient Natural Language Response Suggestion for Smart Replycited by 229
  3. 2019Gmail Smart Compose: Real-Time Assisted Writingcited by 98
  4. 2021The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writerscited by 61
  5. 2016Smart Reply: Automated Response Suggestion for Emailcited by 54
  6. 2021Artificial intelligence in communication impacts language and social relationshipscited by 22
  7. 2019Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencodercited by 14
  8. 2019OCC: A Smart Reply System for Efficient In-App Communicationscited by 7
  9. 2016Machine Agency in Human-Machine Networks; Impacts and Trust Implicationscited by 2
  10. 2021Language Scaling for Universal Suggested Replies Modelcited by 2

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 1.

5 papers.