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Inappropriate content

As explained in Starting Data Analytics with Generative AI and Python

Generated material that reflects unfair distortions or violates contextual expectations of acceptable content.

Inappropriate content is generated material that reflects bias or violates expectations for acceptable communication. Generative AI can produce it despite efforts to reduce harmful outputs, because biases may remain in training data or be introduced through prompts. section 1.1

Treat generated responses as outputs requiring review:

  • Inspect responses for unfair patterns, harmful language, or contextually unacceptable material. section 1.1
  • Check whether prompt wording or hidden assumptions may be shaping the result. section 1.1
  • Balance the model’s usefulness with awareness that mitigation efforts do not eliminate the problem. section 1.1

A practical check

Before using an AI-generated response in an analysis or communication, read it critically rather than assuming it is safe or neutral. GPT-4’s System Card is cited as an example of efforts to balance model responses. section 1.1

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Defines the risk and identifies training data and prompts as causes, but stops short of prescribing a detailed detection procedure.
Examples
Uses the GPT-4 System Card as an example of efforts to balance model responses.
What it assumes
Assumes familiarity with interacting with generative AI and teaches that model outputs require critical awareness.
What you can do after
Evaluate generated responses with awareness that bias and inappropriate material may remain after mitigation.
How it connects
Connects inappropriate content with generative AI, bias, training data, prompts, and harmful content.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.