Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness

Catalog evidence and recorded chart appearances. Inclusion is not an endorsement.

Source snapshot imported 2026-10-20. This is not a live feed. Source and contributions.

Evidence and limits

Finding: Complementarity is not always achievable, and lower average loss can conflict with fair distribution of benefit or increase group error disparities.

Impact: Strengthen impossibility checks and subgroup outcomes. Reject average complementarity as sufficient benefit.

Boundary: The model abstracts human behavior and task structure. Next: which empirical conditions reveal that a workflow cannot beat its better component?

Flags: modeled losses and groups; no open-ended workflow

Record State: Catalog review recorded; not a performance endorsement

What was reviewed: Structure and relevance screened; not a full evaluation

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Recorded chart appearances

  • 2026-10-13: position 80. Relevant to the recorded review topic (see source); Structure and relevance screened; not a full evaluation evidence
  • 2026-10-12: position 79. Relevant to the recorded review topic (see source); Structure and relevance screened; not a full evaluation evidence