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The reanimation of pseudoscience in machine learning and its ethical repercussions
Mel Andrews1,2, Andrew Smart3, Abeba Birhane4,5
1The University of Cincinnati, Cincinnati, OH, USA.
Abstract:
The present perspective outlines how epistemically baseless and ethically pernicious paradigms are recycled back into the scientific literature via machine learning (ML) and explores connections between these two dimensions of failure. We hold up the renewed emergence of physiognomic methods, facilitated by ML, as a case study in the harmful repercussions of ML-laundered junk science. A summary and analysis of several such studies is delivered, with attention to the means by which unsound research lends itself to social harms. We explore some of the many factors contributing to poor practice in applied ML. In conclusion, we offer resources for research best practices to developers and practitioners.
Insights
Machine learning (ML) can inadvertently revive flawed scientific ideas, like physiognomy, leading to harmful junk science. This perspective examines how poor ML practices cause social harm and offers solutions for better research.
Area of Science:
- Scientific integrity
- Machine learning ethics
- Research methodology
Background:
- Epistemically baseless and ethically pernicious paradigms are being reintroduced into scientific literature.
- Machine learning (ML) facilitates the recycling of flawed scientific concepts.
- Physiognomic methods, a pseudoscientific approach, are resurfacing due to ML advancements.
Purpose of the Study:
- To analyze how machine learning launders junk science.
- To explore the link between ML failures and ethical/epistemological issues.
- To use the resurgence of physiognomy as a case study for ML-driven scientific harm.
Main Methods:
- Perspective piece analyzing existing literature and ML practices.
- Case study analysis of ML-facilitated physiognomic studies.
- Examination of factors contributing to poor applied ML.
Main Results:
- ML can inadvertently legitimize and disseminate unsound scientific research.
- The application of ML to flawed paradigms results in "ML-laundered junk science."
- Poor practices in applied ML contribute to the propagation of harmful research.
Conclusions:
- Addressing poor practices in applied machine learning is crucial for scientific integrity.
- Developing and implementing best practices in ML research can mitigate the spread of junk science.
- Resources are provided for developers and practitioners to improve research quality and ethical considerations.
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