Selective Response Strategies for GenAI

Boaz Taitler, Omer Ben-Porat
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:58189-58226, 2025.

Abstract

The rise of Generative AI (GenAI) has significantly impacted human-based forums like Stack Overflow, which are essential for generating high-quality data. This creates a negative feedback loop, hindering the development of GenAI systems, which rely on such data to provide accurate responses. In this paper, we provide a possible remedy: A novel strategy we call selective response. Selective response implies that GenAI could strategically provide inaccurate (or conservative) responses to queries involving emerging topics and novel technologies, thereby driving users to use human-based forums. We show that selective response can potentially have a compounding effect on the data generation process, increasing both GenAI’s revenue and user welfare in the long term. From an algorithmic perspective, we propose an approximately optimal approach to maximize GenAI’s revenue under social welfare constraints. From a regulatory perspective, we derive sufficient and necessary conditions for selective response to improve welfare.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-taitler25a, title = {Selective Response Strategies for {G}en{AI}}, author = {Taitler, Boaz and Ben-Porat, Omer}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {58189--58226}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/taitler25a/taitler25a.pdf}, url = {https://proceedings.mlr.press/v267/taitler25a.html}, abstract = {The rise of Generative AI (GenAI) has significantly impacted human-based forums like Stack Overflow, which are essential for generating high-quality data. This creates a negative feedback loop, hindering the development of GenAI systems, which rely on such data to provide accurate responses. In this paper, we provide a possible remedy: A novel strategy we call selective response. Selective response implies that GenAI could strategically provide inaccurate (or conservative) responses to queries involving emerging topics and novel technologies, thereby driving users to use human-based forums. We show that selective response can potentially have a compounding effect on the data generation process, increasing both GenAI’s revenue and user welfare in the long term. From an algorithmic perspective, we propose an approximately optimal approach to maximize GenAI’s revenue under social welfare constraints. From a regulatory perspective, we derive sufficient and necessary conditions for selective response to improve welfare.} }
Endnote
%0 Conference Paper %T Selective Response Strategies for GenAI %A Boaz Taitler %A Omer Ben-Porat %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-taitler25a %I PMLR %P 58189--58226 %U https://proceedings.mlr.press/v267/taitler25a.html %V 267 %X The rise of Generative AI (GenAI) has significantly impacted human-based forums like Stack Overflow, which are essential for generating high-quality data. This creates a negative feedback loop, hindering the development of GenAI systems, which rely on such data to provide accurate responses. In this paper, we provide a possible remedy: A novel strategy we call selective response. Selective response implies that GenAI could strategically provide inaccurate (or conservative) responses to queries involving emerging topics and novel technologies, thereby driving users to use human-based forums. We show that selective response can potentially have a compounding effect on the data generation process, increasing both GenAI’s revenue and user welfare in the long term. From an algorithmic perspective, we propose an approximately optimal approach to maximize GenAI’s revenue under social welfare constraints. From a regulatory perspective, we derive sufficient and necessary conditions for selective response to improve welfare.
APA
Taitler, B. & Ben-Porat, O.. (2025). Selective Response Strategies for GenAI. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:58189-58226 Available from https://proceedings.mlr.press/v267/taitler25a.html.

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