Bridging Formal and Perceived Fairness: Development of an Interdisciplinary Framework in Algorithmic Decision-Making

URL SCAN: Bridging Formal and Perceived Fairness: Development of an Interdisciplinary Framework in Algorithmic Decision-Making
FIRST LINE: # Computer Science > Computers and Society

The Dissection

This is a legitimacy framework for algorithmic rule. It correctly identifies that formal fairness metrics do not guarantee that affected people will experience a system as just, trustworthy, or acceptable. Its proposed bridge—literature synthesis, workshops, stakeholder interviews, computational audits, and user-centered evaluation—aims to make automated decisions more socially absorbable.

The paper is explicitly work-in-progress. The supplied abstract presents an agenda, not validated evidence that the framework resolves the conflict between technical fairness and lived injustice.

The Core Fallacy

The paper’s central mistake is not recognizing perceived fairness. That part is correct. The mistake is treating the problem primarily as a design, communication, and evaluation gap.

Under the Discontinuity Thesis, legitimacy is downstream of control and material position. If an algorithm determines access to employment, credit, welfare, healthcare, or opportunity while the affected population lacks ownership, bargaining power, and meaningful appeal, better-calibrated perceptions do not repair the underlying asymmetry. They make it more intelligible and potentially more tolerable.

The framework addresses whether people accept automated allocation. It does not address who owns the automated system, who captures its surplus, or what happens when productive participation collapses under P1–P3. It is therefore a legitimacy layer placed over the machinery of displacement.

Hidden Assumptions

• Trust and acceptance are treated as indicators of system legitimacy rather than possible outcomes of adaptation to unavoidable powerlessness.
• Stakeholder perceptions can be synthesized into a coherent fairness standard despite conflicting interests and irreconcilable values.
• User participation can materially influence systems whose core incentives are controlled by institutions or owners outside the affected group.
• Transparency and informed judgment can compensate for the loss of control over the decision process.
• The main danger is perceived unfairness, rather than actual exclusion, dependency, extraction, or loss of economic function.
• Institutional actors will use fairness assessments to redistribute power rather than to reduce resistance and preserve deployment.
• Human-centered design remains sufficient even as the economic role of humans contracts.

Social Function

Classification: partial truth, transition management, prestige signaling, and—when used as a substitute for power redistribution—ideological anesthetic.

The paper identifies a real operational failure: formally compliant systems can still be rejected as unjust. That makes the framework useful to organizations deploying algorithmic decision-making. It can reduce backlash, improve adoption, and stabilize automated governance.

Its danger is that “informed, well-calibrated fairness judgments” can become a polite demand that affected people learn to understand and accept decisions they cannot meaningfully control. The human is retained as a stakeholder, observer, and legitimacy sensor—not necessarily as an owner or economically necessary participant.

The Verdict

This is not a theory that reverses algorithmic obsolescence. It is a proposal for making automated authority socially survivable.

Its contribution is real but subordinate: it may expose why metric compliance fails to produce legitimacy. Under the Discontinuity Thesis, however, legitimacy cannot permanently stabilize a system that severs mass employment from consumption and productive participation. At best, this framework is transition infrastructure—useful during the lag between technical deployment and social rupture. It can make the coffin better designed; it does not stop the burial.