# Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism in the Design and Perception of Conversational Assistants

arXiv: https://arxiv.org/abs/2106.02578 · HTML: https://mlsanity.com/paper/2106.02578/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [On Measuring Social Biases in Sentence Encoders](https://mlsanity.com/paper/1903.10561/) (2019)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Intrinsic Bias Metrics Do Not Correlate with Application Bias](https://mlsanity.com/paper/2012.15859/) (2020)
- [Revealing Persona Biases in Dialogue Systems](https://mlsanity.com/paper/2104.08728/) (2021)
- [Living Machines: A study of atypical animacy](https://mlsanity.com/paper/2005.11140/) (2020)
- [A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents](https://mlsanity.com/paper/1909.04387/) (2019)

## Built on by (most cited first, 16 of 16)

- [The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems](https://mlsanity.com/paper/2204.03021/) (2022)
- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications](https://mlsanity.com/paper/2205.06828/) (2022)
- [Mirages: On Anthropomorphism in Dialogue Systems](https://mlsanity.com/paper/2305.09800/) (2023)
- [Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance](https://mlsanity.com/paper/2407.07950/) (2024)
- [Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing](https://mlsanity.com/paper/2507.10587/) (2025)
- [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://mlsanity.com/paper/2311.05552/) (2023)
- [Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems](https://mlsanity.com/paper/2210.12429/) (2022)
- ["Alexa doesn't have that many feelings": Children's understanding of AI through interactions with smart speakers in their homes](https://mlsanity.com/paper/2305.05597/) (2023)
- ["I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI](https://mlsanity.com/paper/2410.08526/) (2024)
- [Understanding Public Perceptions of AI Conversational Agents: A Cross-Cultural Analysis](https://mlsanity.com/paper/2402.16039/) (2024)
- [What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigma](https://mlsanity.com/paper/2505.12727/) (2025)
- [State-of-the-art in Open-domain Conversational AI: A Survey](https://mlsanity.com/paper/2205.00965/) (2022)
- [Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America](https://mlsanity.com/paper/2507.01719/) (2025)
- [Sexualised synthetic personas encode and amplify gendered power asymmetries through voice](https://mlsanity.com/paper/2606.21366/) (2026)

## Abstract (arXiv)

Technology companies have produced varied responses to concerns about the effects of the design of their conversational AI systems. Some have claimed that their voice assistants are in fact not gendered or human-like -- despite design features suggesting the contrary. We compare these claims to user perceptions by analysing the pronouns they use when referring to AI assistants. We also examine systems' responses and the extent to which they generate output which is gendered and anthropomorphic. We find that, while some companies appear to be addressing the ethical concerns raised, in some cases, their claims do not seem to hold true. In particular, our results show that system outputs are ambiguous as to the humanness of the systems, and that users tend to personify and gender them as a result.