Chapter 16Knowing when not to
Some calls are yours
In this chapter The decisions that should stay with a person, and why “a person checks it” is harder than it sounds.
Harness the body
Tools the handsPrompt the briefingModel the brain
Words to know
- Automation bias
- The habit of trusting an automated aid too much, and missing its mistakes.
AI can help with almost any task, but some decisions should stay with a person: ones about people’s jobs, grades, health or rights, where someone has to answer for the outcome and explain it.
- An old warning. In 1976 Joseph Weizenbaum, who wrote ELIZA (Chapter 4), argued that some decisions should never be left to computers, whatever they can be made to do.1
- “A person checks it” is harder than it sounds. People working with automated aids can over-trust them and miss their mistakes: automation bias (Chapter 14).2 A person who only rubber-stamps a ranking hasn’t really decided.
- Models carry old patterns. They learn from text people wrote, and NIST lists harmful bias among the risks of generative AI: the “amplification and exacerbation of historical, societal, and systemic biases.”3
- Lawmakers have drawn some lines. The European Union’s AI Act treats AI used to recruit, promote, fire, assign or monitor workers, and to admit students or evaluate their learning, as “high-risk,” with extra requirements; one is that such systems be built so people can effectively oversee them and stay aware of automation bias.4 Europe’s data-protection law, the GDPR, gives people a right, with exceptions, not to be subject to decisions based solely on automated processing that significantly affect them.5
Go deeper: the dates
The AI Act’s rules for these high-risk uses were to apply from August 2026; a 2026 amendment moved that to December 2027.6 Laws differ by country and change; this is the idea, not legal advice.
Notes (6)
- 1Joseph Weizenbaum, Computer Power and Human Reason: From Judgment to Calculation (San Francisco: W. H. Freeman, 1976).
- 2Raja Parasuraman and Dietrich H. Manzey, “Complacency and Bias in Human Use of Automation: An Attentional Integration,” Human Factors 52, no. 3 (June 2010): 381–410, https://doi.org/10.1177/0018720810376055. Cited at p. 381.
- 3National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1) (NIST, July 26, 2024), https://doi.org/10.6028/NIST.AI.600-1. Cited at p. 4.
- 4European Parliament and Council of the European Union, “Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending … (Artificial Intelligence Act),” Official Journal of the European Union, L series, June 13, 2024, http://data.europa.eu/eli/reg/2024/1689/oj (accessed October 27, 2026). Cited at Art. 6(2); Art. 14; Annex III.
- 5European Parliament and Council of the European Union, “Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation),” Official Journal of the European Union, April 27, 2016, http://data.europa.eu/eli/reg/2016/679/oj (accessed October 27, 2026). Cited at Art. 22.
- 6European Parliament and Council of the European Union, “Regulation (EU) 2026/1744 of the European Parliament and of the Council of 8 July 2026 amending Regulations (EU) 2024/1689, (EU) 2018/1139 and (EU) 2023/1230 as regards the simplification of the implementation of harmonised rules on artificial intelligence (Digital Omnibus on AI),” Official Journal of the European Union, L series, July 8, 2026, http://data.europa.eu/eli/reg/2026/1744/oj (accessed October 27, 2026).
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