Research opportunity · Discretion curtailed, relocated or encoded?
How often do officials depart from algorithmic recommendations, across systems?
Earlier version (v1)
This is RO-LD05-01 as it was published at v1, kept unchanged. The current version is v2; its changes are listed there.
Question
Across deployed public-sector risk and profiling systems, how often do officials formally or informally depart from the system's recommendation, in which direction, and how much of the variation between systems survives once override is defined and counted the same way?
Not yet measuredMaster's thesisLow costNo study found
Why it matters
Override figures are quoted as evidence that discretion has or has not survived automation, but each comes from one system, with its own definition, denominator and period. A 0.58% profile-change rate in one country and a 21% screen-out rate in another are not the same quantity. Without a common way of counting, nobody can say whether nominal human review usually means real departure or rubber-stamping.
The debate lists this among what we still do not know: How often do public-sector workers actually depart from algorithmic recommendations across comparable systems?
Evidence that motivates it
- The presence of a human reviewer does not establish meaningful oversight; effective discretion depends on what the human can see, whether they can depart, how often they do so, and the organisational cost of disagreement.
Says effective discretion depends partly on how often humans actually depart; the claim does not license a universal oversight metric, which is why the study reports a comparable descriptive measure rather than a single score of oversight quality. - In Poland’s unemployment profiling system, formal profile changes were extremely rare—0.58% in the cited early-period data—while discretion persisted through interviewing, interpretation and data entry.
Poland's 0.58% formal profile-change rate is an early-period figure for one system, and the claim notes discretion persisted through interviewing and data entry; it is one data point the comparison would standardise, not a lifetime rate. - Allegheny supervisors screened out 21.0% of children marked as mandatory screen-ins; corrected analysis supports the conclusion that worker judgement reduced racial disparity relative to an AFST-only counterfactual.
Allegheny supervisors screened out 21.0% of mandatory screen-ins, a different kind of departure (a different margin and denominator) from Poland's, showing why a common coding scheme is needed.
A possible design
Build a coded dataset of override and deference figures for deployed public-sector decision-support systems (benefits, employment services, child welfare, pretrial, immigration). Sources: peer-reviewed studies, audit and inspection reports, court judgments, algorithmic transparency records, and freedom-of-information requests to agencies that run such systems. For each figure code: what counts as a departure (formal change of score or category, decision against recommendation, informal re-routing), the denominator, direction (more or less severe), period, whether the reviewer could see the score, and the cost of disagreeing (for example, a written justification requirement). Report the spread of rates under a harmonised definition and where harmonisation is impossible, and test whether recorded features such as justification requirements or score visibility go with lower departure rates. The analysis is descriptive; it does not claim that departures are good or bad.
Data needed
Published studies and audits; algorithmic transparency records (for example the UK register); FOI responses on override counts; court documents. No individual-level data is required.
Feasibility
Master's thesis. Low: desk research and FOI requests, which are free in many jurisdictions. Many agencies will not hold override counts or will refuse FOI requests; recording which agencies cannot answer is itself a finding. Language coverage limits which countries can be included.
Has this been done?
The search found no study that does the core design (first-pass screen of 12 October 2026; not yet checked in depth).
No cross-system compilation of real override or deference rates under a common definition was found. Single-system quasi-experimental studies exist (for example pretrial bail overrides), systematic reviews synthesise how digital tools change discretion without compiling rates, and survey experiments test whether officials follow algorithmic advice in simulated tasks, but neither yields comparable rates across deployed systems.
How strong this evidence is
- Who searched and checked: an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. Every check so far was done by AI; no person or domain expert has checked it yet.
- What was read: 3 of the 4 nearest works below were judged from an abstract, summary or search listing only; each is marked. Not every entry says how much of the work was read.
- Where the search could not reach: Screened by web search on the date shown; Google Scholar, SSRN and paywalled journal pages were not systematically searched, and grey literature in languages other than English was not searched. Absence of a hit is not proof that the work does not exist.
- Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.
Nearest work found
- Angelova, Dobbie & Yang, "Algorithmic Recommendations and Human Discretion," NBER Working Paper 31747 (Sept 2023); published in Review of Economic Studies (advance article, 15 Sept 2025), doi:10.1093/restud/rdaf084 Judged from an abstract, summary or search listing only
Single-setting study of judges' overrides of a pretrial algorithm in bail decisions (NBER abstract read; the journal version was not opened): finds about 90% of judges do worse than the algorithm when they override, while about 10% do better on accuracy and fairness. Measures the quality of overrides in one system; does not compare override rates across systems. - Alon-Barkat & Busuioc, "Human–AI Interactions in Public Sector Decision Making: 'Automation Bias' and 'Selective Adherence' to Algorithmic Advice," Journal of Public Administration Research and Theory 33(1), 153–169 (2023)
Three Dutch survey experiments (two citizen samples and 1,345 civil servants) in a simulated contract-renewal task; no evidence of automation bias, and selective adherence to stereotype-congruent advice in one citizen study, not among civil servants. Measures adherence in a vignette, not override rates in deployed systems. - Selten, Robeer & Grimmelikhuijsen, "'Just like I thought': Street-level bureaucrats trust AI recommendations if they confirm their professional judgment," Public Administration Review 83(2), 263–278 (2023) Judged from an abstract, summary or search listing only
Factorial experiment with Dutch police officers using a mock predictive-policing system (abstract read via Crossref): 2 × 2 factorial design; officers trusted and followed recommendations that matched their own judgement, and explanations made no difference. Experimental, one setting; no field override rates. - Northcott, "Automating street-level discretion: A systematic literature review and research agenda," Policy Studies Journal (published online 10 Apr 2025), doi:10.1111/psj.70024 Judged from an abstract, summary or search listing only
Systematic review of how information and communication technologies change street-level discretion (removing face-to-face contact, standardisation, monitoring, decision support) and how frontline workers resist standardisation. Only the abstract was read (via Crossref); it synthesises mechanisms and does not report comparable override rates.
Search queries used
override rates algorithmic risk assessment caseworkers comparison across systems public sector deferencesystematic review human override of algorithmic recommendations public administration frontline workers adherence rates"override rate" pretrial risk assessment OR child welfare screening tool OR unemployment profiling judges caseworkers empiricalcompilation of override and adherence rates across deployed algorithmic decision aids public sector review "adherence" "override" meta-analysis 2024 2025systematic review human discretion override algorithmic decision support street-level bureaucrats deviation rates meta-analysis 2025
Provenance
RO-LD05-01 v1. Drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-05 debate on 2026-10-12. Checked by an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. Not yet reviewed by a domain expert. This is a research hypothesis, not a finding.