How should regulators think about "AI"?

Emily M. Bender2 minutes read

AI is essentially a marketing term for automation created in the 1950s to attract funding, with automation being a more accurate replacement term for clearer discussions on its implications and consequences for society. Various types of automation, from decision-making to language translation, can exhibit biases, promote mass surveillance, and serve corporate interests, emphasizing the need for accountability and ethical considerations.

Insights

  • The term "AI" was initially a marketing strategy in the 1950s to secure research funding, emphasizing the need to replace it with "Automation" for clearer discussions on its impact, accountability, and beneficiaries.
  • Automation encompasses various systems like decision-making, classification, recommendation, and translation, built on data and algorithms that may introduce biases, surveillance, and automation bias, ultimately serving corporate agendas.

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Recent questions

  • What is the origin of AI?

    Marketing term in the 1950s

  • How can discussions on AI be clarified?

    Replace with Automation

  • What are some types of automation?

    Automatic decision systems, recommender systems, etc.

  • How are automation systems built?

    Using training data and algorithms

  • What are the potential risks of automation?

    Biases, mass surveillance, automation bias, corporate interests

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Summary

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AI vs Automation: Marketing Term Evolution and Impact

  • AI is a marketing term used to make automation seem advanced and autonomous, but it originated as a marketing term in the 1950s to attract research funding.
  • The term AI can be replaced with Automation to clarify discussions on what is being automated, who benefits, how well it works, who is harmed, and who is accountable.
  • Different types of automation include automatic decision systems, classification automation, recommender systems, access to human labor, translation automation, and synthetic media machines like chat GPT.
  • Automation systems are built using training data and algorithms to reproduce patterns, but they can exhibit biases and promote mass surveillance, tapping into automation bias and serving corporate interests.
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