The Promises and Pitfalls of Using Algorithms in Organizations
Bibliographic record
Abstract
Organizations are increasingly using algorithms to aid with decision-making. However, these algorithms often ignore human psychology, which can lead to both biased algorithms and missed opportunities. The first set of presentations focuses on how algorithms learn from human behavior data. They show how training algorithms on human behavior data without taking psychology into account can lead to unintended consequences, such as discrimination in resume screening or distorted social perceptions fueled by social media algorithms. The latter talks study how employees learn from algorithms. They uncover insights into how people leverage algorithmic advice in real-world situations versus hypothetical scenarios, and present a method for using algorithms to generate novel hypotheses about behavior. Algorithms that Misunderstand Us Author: Sophia Pink; The Wharton School, U. of Pennsylvania Author: Sendhil Mullainathan; U. of Chicago Booth School of business Author: Katherine Milkman; U. of Pennsylvania Algorithm-Mediated Social Learning Author: William Brady; Kellogg School of Management, Northwestern U. Author: Joshua Jackson; Northwestern Kellogg School of Management Author: Silvan Baier; Kellogg School of Management, Northwestern U. Author: Joseph Abruzzo; Kellogg School of Management, Northwestern U. Words that Work: Combining Machine Learning and Psychology to Generate Hypotheses from Text Author: Rafael Batista; U. of Chicago Booth School of business Author: James Ross; U. of Chicago Booth School of business Author: Sendhil Mullainathan; U. of Chicago Booth School of business Author: Jens Ludwig; U. Of Chicago A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation” Author: Jennifer Marie Logg; Georgetown U. Author: Rachel Schlund; Cornell U.
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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 | 0.055 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.015 | 0.038 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".