The Limitations of Data, Machine Learning and Us
Summary: Two-part keynote on data/ML limits: small data, datification, bias, harm-focused evaluation. Then explores human factors (cognitive biases, pseudoscience, unethical use) and advocates regulation and responsible principles to mitigate harm. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ricardo Baeza-Yates (Northeastern University; University of Chile)
BibTeX Citation
@inproceedings{baezayates_sigmod24,
title = {{The Limitations of Data, Machine Learning and Us}},
author = {Baeza-Yates, Ricardo},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626246.3656000},
url = {https://dl.acm.org/doi/10.1145/3626246.3656000},
year = {2024}
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