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Pitfalls and risks of generative AI in machine learning.

Michael A Lones1

  • 1School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.

Patterns (New York, N.Y.)
|May 14, 2026
PubMed
Summary
This summary is machine-generated.

Generative artificial intelligence (AI) is transforming machine learning (ML) workflows, introducing new risks. This tutorial guides users through potential pitfalls in evaluation, security, and costs for informed decision-making.

Keywords:
LLMsfoundation modelsgenerative AIguidancemachine learningpractice

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Generative AI, including large language models (LLMs) and foundation models, is increasingly integrated into machine learning (ML) workflows.
  • This integration presents novel opportunities alongside significant challenges.
  • Potential issues include difficulties in evaluation, security vulnerabilities, data provenance concerns, technical debt, regulatory compliance hurdles, and unforeseen financial expenses.

Purpose of the Study:

  • To provide a concise overview of emerging pitfalls and risks associated with generative AI in ML workflows.
  • To offer practical guidance for navigating these challenges and making informed decisions.
  • To help readers avoid costly mistakes when implementing generative AI.

Main Methods:

  • The tutorial explores four primary applications of generative AI within ML workflows.
  • These applications include generative AI as a component of ML pipelines, as a designer of ML pipelines, as a data synthesizer, and as a data analyst.
  • The content is presented in plain language, assuming no deep prior knowledge of the subject.

Main Results:

  • Identifies key areas where generative AI can introduce risks into ML workflows.
  • Highlights challenges related to model evaluation, data integrity, and system security.
  • Discusses the potential for hidden costs and regulatory non-compliance.

Conclusions:

  • Generative AI offers powerful capabilities but requires careful management within ML workflows.
  • Understanding and mitigating associated risks is crucial for successful and responsible AI implementation.
  • This tutorial serves as a foundational guide for practitioners to navigate the complexities of generative AI in ML.