Human-Centered AI, by Ben Shneiderman. Oxford: Oxford University Press, 2022. 305 pp.
Bibliographic record
Abstract
B en Shneiderman presents a compelling argument for why the future of artificial intelligence (AI) design should be human-centric.He begins with the tragic backstories of two Boeing 737 MAX airplane crashes in 2018 and 2019 that were largely due to algorithmic error.Investigations revealed that the cause of both crashes was the installation of autonomous software meant to prevent stalls, which kept pointing the nose of the planes downwards.Disturbingly, the developers of the autonomous control system believed that it was so reliable that pilots were not even informed of its presence, and thus they had no knowledge of what to do to retake control.This example illustrates the danger of prioritizing computer automation over human control.There has been a long-held belief, admittedly even by Shneiderman himself, that by granting greater autonomy to machines there must be a corresponding decline in human control.Sometimes this is desired-advances in vehicle airbags keep people safe by deploying in a fraction of a second, far quicker than any human's reaction time.However, Shneiderman shows that excessive automation can lead to disaster.As another example, in 2016, one of Tesla's self-driving vehicles failed to distinguish between a white vehicle and the sky, resulting in a fatal head-on collision.Unfortunately, this is not an isolated event.As of April 2024, 1 there have been 44 Tesla autopilot deaths and hundreds of collisions.The consequences associated with the pursuit of automation drove Shneiderman to change his perspective.Instead of viewing human control and computer automation as extremes along the same axis, he posits that reliable, safe, and trustworthy AI systems are designed with high automation and high human control.Shneiderman proposes a human-centered AI (HCAI) framework where he writes, "the goal is not to replace people but to empower them by making design choices that give humans 1 Source: Tesladeaths.com.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.043 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".