# Interactive Storytelling for Children: A Case-study of Design and Development Considerations for Ethical Conversational AI

arXiv: https://arxiv.org/abs/2107.13076 · HTML: https://mlsanity.com/paper/2107.13076/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 139 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://mlsanity.com/paper/1911.03842/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https://mlsanity.com/paper/1809.05972/) (2018)
- [Neural Voice Cloning with a Few Samples](https://mlsanity.com/paper/1802.06006/) (2018)
- [Does Gender Matter? Towards Fairness in Dialogue Systems](https://mlsanity.com/paper/1910.10486/) (2019)
- [Deep Voice: Real-time Neural Text-to-Speech](https://mlsanity.com/paper/1702.07825/) (2017)
- [Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination](https://mlsanity.com/paper/1906.00285/) (2019)
- [Deep Voice 2: Multi-Speaker Neural Text-to-Speech](https://mlsanity.com/paper/1705.08947/) (2017)
- [Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://mlsanity.com/paper/1710.07654/) (2017)
- [Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses](https://mlsanity.com/paper/2001.09528/) (2020)
- [Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU](https://mlsanity.com/paper/1910.00912/) (2019)
- [An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniques](https://mlsanity.com/paper/2103.14757/) (2021)
- [Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems](https://mlsanity.com/paper/1703.07055/) (2017)
- [Does Removing Stereotype Priming Remove Bias? A Pilot Human-Robot Interaction Study](https://mlsanity.com/paper/1807.00948/) (2018)
- [From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices](https://mlsanity.com/paper/1905.06876/) (2019)

## Built on by (most cited first, 5 of 5)

- [Can Machines Learn Morality? The Delphi Experiment](https://mlsanity.com/paper/2110.07574/) (2021)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Helpful or Harmful? Exploring the Efficacy of Large Language Models for Online Grooming Prevention](https://mlsanity.com/paper/2403.09795/) (2024)
- [Once Upon an AI: Six Scaffolds for Child-AI Interaction Design, Inspired by Disney](https://mlsanity.com/paper/2504.08670/) (2025)
- [Plural Voices, Single Agent: Towards Inclusive AI in Multi-User Domestic Spaces](https://mlsanity.com/paper/2510.19008/) (2025)

## Abstract (arXiv)

Conversational Artificial Intelligence (CAI) systems and Intelligent Personal Assistants (IPA), such as Alexa, Cortana, Google Home and Siri are becoming ubiquitous in our lives, including those of children, the implications of which is receiving increased attention, specifically with respect to the effects of these systems on children's cognitive, social and linguistic development. Recent advances address the implications of CAI with respect to privacy, safety, security, and access. However, there is a need to connect and embed the ethical and technical aspects in the design. Using a case-study of a research and development project focused on the use of CAI in storytelling for children, this paper reflects on the social context within a specific case of technology development, as substantiated and supported by argumentation from within the literature. It describes the decision making process behind the recommendations made on this case for their adoption in the creative industries. Further research that engages with developers and stakeholders in the ethics of storytelling through CAI is highlighted as a matter of urgency.