Figure 1From “Hybrid intelligence needs a boundary between capability and outcome”
The hybrid-intelligence construct boundary
Define complementary capability before treating a team outcome as evidence of hybrid intelligence.
Figure 1The hybrid-intelligence construct boundary
| Construct question | Observable evidence | Missing evidence | Outcome measure | Interpretation limit |
|---|---|---|---|---|
| Who contributes? | Human and machine roles are described | Contribution quality is unknown | None yet | A role list is not complementarity |
| What is complementary? | Capabilities differ for the task | No matched task comparison | Task-level performance | Difference is not superiority |
| How is work governed? | Trust, transparency, and responsibility are named | Incentives and challenge paths are unclear | Review and correction rate | Governance is not proof of safety |
| What improved? | A team outcome is declared | No baseline or comparison | Declared decision outcome | A label cannot supply the result |
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Source: Author's synthetic evaluation framework grounded in Dellermann, Ebel, Söllner and Leimeister (2019), Jarrahi (2018), and Seeber et al. (2020). The rows are illustrative and do not score a current team.
Source and evidence
Each line names a claim related to this exhibit, its source, and how it was checked.
- A DEL19-C1 The definition, in the authors' words: "the most likely paradigm for the division of labor between humans and machines in the next decades is Hybrid" Intelligence, a concept that aims "at using the complementary strengths of human intelligence and AI, so that they can perform better than each of the two could separately"Dellermann, Ebel, Sollner & Leimeister. (2019)
- A DEL19-C2 The boundary against collective intelligence, verbatim: "collective intelligence typically refers to large groups of homogenous individuals", whereas "Hybrid Intelligence combines the complementary intelligence of heterogeneous agents"Dellermann, Ebel, Sollner & Leimeister. (2019)
- A DEL19-C3 Why the division of labour is the unit: "humans and computers have complementary capabilities that can be combined to augment each other", and "These complementary strengths of humans and machi"nes lead to two different forms of interplay, with trust, transparency, governance and incentives left as open design issuesDellermann, Ebel, Sollner & Leimeister. (2019)
- A JAR18-C1 Augmentation, not replacement, and the article says so: it "highlights the complementarity of humans and AI", mirroring "the idea of intelligence augmentation which states that AI systems should be designed with the intention of augmenting not replacing human contributions"Jarrahi. (2018)
- A JAR18-C2 The division is drawn on the kind of problem: "with a greater computational information processing capacity and an analytical approach AI can extend humans cognition when addressing complexity whereas humans can still offer a more holistic intuitive approach in dealing with uncertainty and equivocality"Jarrahi. (2018)
- A JAR18-C3 The context is named in the same sentence: decision processes "typically characterized by uncertainty complexity and equivocality", which is what makes the division of labour a task property rather than a ruleJarrahi. (2018)
- A SEE20-C1 A research agenda, and it counts itself: "this paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates", from "819 research questions"Seeber et al. (2020)
- A SEE20-C2 The structure is three areas and seventeen dualities: "three design areas machine artifact collaboration and institution and 17 dualities significant effects with the potential for benefit or harm"Seeber et al. (2020)
- A SEE20-C3 It is a question set, not a finding: the agenda "offers a structure and archetypal research questions to organize early thought and research in this new" areaSeeber et al. (2020)
Grades: A, verified against the printed page of the primary source · B, primary source, text layer only · C, authoritative secondary · D, reported.
From the essay
From the research bench
Hybrid intelligence needs a boundary between capability and outcome
Hybrid intelligence names complementary human and machine capability, not proof of better performance: define the construct before measuring the outcome.
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