Calibrating Reliance: Two Questions Before a Family Office Delegates Judgment to an Algorithm Family Office Private Wealth Journal · Editorial research desk Family Office Private Wealth Journal · 2026-10-22 CC BY 4.0 · https://creativecommons.org/licenses/by/4.0/ Source: https://journal.research.university/journals/family-office-private-wealth/articles/expansion-10-1371-journal-pone-0247084-solutions Abstract People may reject an algorithm because they expect weak performance or because they demand a different threshold before trusting it. This companion turns that distinction into an onboarding and evaluation protocol that seeks warranted reliance rather than higher adoption. The protocol preserves human responsibility and evaluates expectations and reliance separately. Why a single trust score is inadequate A committee member who declines an algorithmic recommendation may be making a careful judgment, misunderstanding the tool, or applying a stricter standard to automated advice. These possibilities require different responses. A training session designed simply to increase trust could weaken an appropriate safeguard. The operating aim should instead be calibrated reliance: people use a tool when its evidence and authorized purpose justify doing so, and they challenge it when those conditions are absent. A family office can begin with two questions: what does the user expect the system to do well, and what evidence would the user require before relying on it? The empirical distinction Fenneman and colleagues used a repeated experimental judgment task involving human and algorithmic fund managers [1]. They found that performance predictions and relative trust thresholds both contributed to individual differences in adherence and were separable. Initial exposure also mattered: the order in which participants encountered manager types affected subsequent reliance. These findings concern behavior in a controlled task, not audited wealth-management performance. The online sample may differ from an office’s professional staff or family principals. Nevertheless, the distinction offers a useful hypothesis for implementation: poor adoption can originate in different mechanisms, and polished demonstrations may alter willingness without establishing reliability. Elicit expectations before demonstrating the tool Before onboarding, ask each authorized user to describe the tasks for which they expect the system to be helpful, the errors they anticipate and the consequences of a missed error. Use concrete cases rather than a general opinion about AI. Then record the evidence each user would need to accept a recommendation, including circumstances requiring another human review. These responses should be private learning inputs rather than personnel rankings. A beneficiary may reasonably require an explanation suited to personal obligations, while an analyst may focus on data coverage. Record both perspectives and make disagreements discussable. Do not infer competence or risk tolerance from enthusiasm for technology. Use balanced exposure and independent scoring Demonstrations should include successes, ordinary errors and cases where the tool appropriately cannot answer. If every early example is favorable, later reliance may reflect presentation order rather than operating quality. A practical pilot can alternate the order of human and automated assistance across comparable training cases. Specialists should score decision quality using criteria fixed before the demonstration, without allowing a confident explanation to define its own success. Measure whether users distinguish supported from unsupported outputs and whether they request help on unresolved cases. Keep actual transactions outside the experiment until the workflow has a separate operational authorization. A training exercise does not create an investment mandate. Diagnose a mismatch before changing behavior If users overestimate accuracy, show the relevant failure distribution and teach the boundary of the approved task. If they underestimate demonstrated performance, explain the evidence and invite them to challenge its relevance. If predictions are accurate but trust thresholds differ, discuss consequences, accountability and available recourse. Some thresholds should remain high, especially where errors are difficult to reverse. Familiarity is not a substitute for control. Monitor whether exposure to imperfect outputs changes expectations and thresholds in opposite directions, a possibility highlighted by the research. Separate these measures from adoption counts so that a rise in usage does not conceal declining skepticism. Implementation ownership and review Owner: Learning lead. Action: Record task-specific performance expectations and reliance conditions before onboarding. Evidence: Private expectation forms and approved task boundaries. Measure: Differences between expected and observed performance. Owner: Independent reviewer. Action: Use balanced case exposure and score supported versus unsupported decisions. Evidence: Reference-case decisions, order assignment and scoring criteria. Measure: Appropriate acceptance, appropriate rejection and unresolved-case escalation. Owner: Control owner. Action: Review reliance after changes and pause consequential overreliance. Evidence: Versioned pilot report and suspension decisions. Measure: Unsupported outputs accepted and corrective-action completion. Evaluate the relationship, not the sales pitch Review the pilot after a defined number of cases and again after a material system change. The review should compare expected performance, demonstrated performance, actual reliance and the quality of the resulting decisions. Retain cases where a user correctly rejected the tool as well as cases where it helped. Pause use if unsupported recommendations are routinely accepted or if responsibility has drifted from the authorized decision maker to the interface. The proposed protocol is an editorial application of the study, not an intervention tested by its authors. Its value lies in making reliance inspectable and contestable while preserving legitimate reasons to decline automation. Source, attribution and limits [1] Achiel Fenneman; Joern Sickmann; Thomas Pitz; Alan G. Sanfey. Two distinct and separable processes underlie individual differences in algorithm adherence: Differences in predictions and differences in trust thresholds. PLOS ONE (2021-02-25). https://doi.org/10.1371/journal.pone.0247084. CC BY 4.0. Online experimental participants and a single simplified task limit generalization; findings do not establish stable individual traits or performance of real investment systems. Increasing adherence is not itself an investment-quality outcome. Prepared by the Family Office Private Wealth Journal editorial research desk with AI assistance at the direction of Dr. Freedom Cheteni. Original research authors retain full credit and are not authors or endorsers of this companion. This proposed process has not been evaluated in a new empirical study; no independent peer-review acceptance is claimed. This is educational analysis, not individualized investment, legal or tax advice. Rights: © 2026 The Research University. © 2026 The Research University. This original editorial text is licensed Creative Commons Attribution 4.0 International. Attribute the title, editorial research desk and publisher, link to the license and indicate changes. Underlying research, talks and recordings retain their separate attribution and rights. Edition changes: Original AI-assisted editorial synthesis. Research findings, transcript themes and proposed practices remain distinct. No new participant data or independent peer-review acceptance is claimed.