In contracts for public decision systems, who controls thresholds, variables and updates?

Research question

In contracts and tender documents for public-sector risk-scoring, profiling and case-routing systems in several countries, who holds authority over thresholds, input variables, retraining and updates, what audit and documentation rights does the agency keep, and how does this differ between vendor-built and in-house systems?

Why it matters

If consequential judgement relocates into system design, the question of who designs matters. Legal scholars have argued that procurement hands policy choices to vendors, and interview studies show procurement staff struggling to govern AI purchases, but no one has systematically read the contracts to see where authority over thresholds, updates and documentation is actually placed. That is a researchable, document-based question.

The debate lists this among what we still do not know: How much substantive discretion sits with vendors and procurement contracts rather than public agencies?

Evidence that motivates it

1. CNAF’s DMDE 2026 is a targeting model rather than an automatic final decision-maker; its source code and model documentation are public and the redesign excludes several sensitive/profile variables. (LD05-HQ-C12 v1.0)
   CNAF publishes the source code and documentation of its targeting model and excluded several sensitive variables in a redesign, showing that design choices can be documented and publicly held; it is a contrast case, and the claim does not say the model is fair or lawful.
   Cites: CNAF/CAF, DMDE 2026 transparency and source-code…
2. For AI-mediated administrative processes, effective contestability depends partly on process provenance—knowing whether a system selected the case, influenced routing, or generated part of the evidentiary record. (LD05-HQ-C17 v1.0)
   Contestability depends on knowing whether and how a system selected or routed a case; contract terms on documentation and audit rights determine whether the agency can even supply that information.
   Cites: DWP; Home Office; Ada Lovelace; Robodebt synthesis

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Assemble a corpus of contracts, statements of work and tender documents for public-sector decision-support systems (benefits fraud, employment profiling, child welfare, immigration routing, policing) from open procurement portals (UK Find a Tender and Contracts Finder, EU Tenders Electronic Daily, US state and city portals) and FOI requests for unpublished schedules. Target 60 to 100 contracts across at least three countries. Develop and pilot a codebook: who sets and may change thresholds; who chooses input variables; who may retrain or update the model and with what notice; performance and error targets; agency rights to inspect code, data and documentation; audit and bias-testing obligations; trade-secret and confidentiality clauses; liability; exit and data-return terms. Double-code a subsample for reliability. Compare vendor-built with in-house or publicly documented systems, and follow up with a few interviews with procurement officers to check how terms work in practice. Position the codebook as extending the US state-level contract review by Cusenza (2026, listed below) to decision-authority terms, keep the corpus to decision-support systems, and include at least two non-US jurisdictions.

Data needed

Public procurement records and FOI responses (redactions expected); a codebook; a second coder; optional interviews with procurement staff.

Feasibility

One PhD chapter or paper. Low: documents are free or obtainable by FOI; costs are mainly coder time. Contracts are often redacted or withheld on commercial-confidentiality grounds; the pattern of redaction should be coded as data. Contracts show formal allocation of authority, not how it is exercised, so the interviews matter.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 12 October 2026, checked again on 13 October 2026).

The argument that procurement shifts policy choices to vendors is established in legal scholarship, and records-request and interview studies exist, including a CHI 2026 interview study of procurement as a governance mechanism. A 2026 systematic review of US state-level AI procurement contracts examines data access, intellectual property, testing and cybersecurity terms and finds algorithmic-governance risks rarely addressed; other recent work analyses vendor self-disclosures or model clauses rather than executed contracts. No study was found that codes, across countries, where authority over thresholds, input variables, retraining and updates, and documentation sits in contracts for public-sector decision-support systems, or compares vendor-built with in-house systems.

Literature check. A systematic review of publicly available US state-level AI procurement contracts now exists (Cusenza, SSRN 6812838, forthcoming Drexel Law Review), covering data access, intellectual property, stakeholder engagement, testing and cybersecurity provisions and finding that contracts rarely address algorithmic-governance risks. That covers part of the proposed contract analysis, but on the abstract it is US-only, state-level, not limited to decision-support systems, and does not code authority over thresholds, input variables, retraining or updates, nor compare vendor-built with in-house systems. Other new work codes vendor FactSheets (Kuehnert et al., 2026) or model clauses and guidance (Abdulbari, Frontiers 2026), not executed contracts. The opportunity should be narrowed to decision authority in contracts for decision-support systems, cross-nationally. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert.

Where the search could not reach. Screened by web search on the date shown; the CHI 2026 paper by Hudig, Kallina and Singh was checked only through Crossref metadata and the first author's summary page (the full text was not read), which describe an interview study, not contract coding. Google Scholar, SSRN, paywalled law reviews and non-English procurement scholarship were not systematically searched. Absence of a hit is not proof that the work does not exist. Literature check of 2026-10-13: Only the abstract of Cusenza was read; the full paper may code more dimensions than the abstract lists, which would narrow the gap further and should be checked before the reword is finalised. The ACM paper 'Algorithm Auditing Policies Rest on Flawed Assumptions About Public Sector Systems' (doi:10.1145/3805689.3812280) and the arXiv taxonomy of human-AI interaction requirements in public procurement (arXiv:2606.21247, whose abstract does not describe a corpus) were not read in full. The Dutch query found government audit and guidance documents (Algoritmekader, Amsterdam Rekenkamer) but no academic contract corpus; German-language and EU TED-based searches were not run. 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

• Mulligan & Bamberger, "Procurement as Policy - Administrative Process for Machine Learning," Berkeley Technology Law Journal 34, 773 (2019)
  Argues that procured machine-learning systems embed policy choices (targets, training data, thresholds) made largely by vendors outside administrative process. Normative and case-based; no coded dataset of contracts (only part of the article was read).
• Johnson, Silva, Leon, Eslami, Schwanke, Dotan & Heidari, "Legacy Procurement Practices Shape How U.S. Cities Govern AI - Understanding Government Employees' Practices, Challenges, and Needs," Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025), 772–789; arXiv:2411.04994
  Interviews with 19 city employees in 7 US cities on how procurement shapes who holds decision power over AI; does not analyse contract texts.
• Brauneis & Goodman, "Algorithmic Transparency for the Smart City," Yale Journal of Law & Technology 20, 103 (2018)
  42 open-records requests in 23 states about six predictive programs; finds transparency almost never provided and over-broad trade-secret claims. Studies what agencies disclose, not how contracts allocate decision authority.
• Hudig, Kallina & Singh, "'It's Just a Wild, Wild West': Harnessing Public Procurement as an AI Governance Mechanism," Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI 2026), 1–22, doi:10.1145/3772318.3791968 Judged from an abstract, summary or search listing only Some details not confirmed
  Semi-structured interviews with public-sector buyers, suppliers, consultants and procurement specialists in the EU and UK on how procurement can govern AI, covering pilots that stay below procurement thresholds, post-deployment model changes and audits. Read via the first author's summary page; the ACM page could not be opened. The summary describes no systematic coding of contract texts.
• Cusenza, "From Policy to Practice: Closing the Gap in AI Public Procurement," SSRN Working Paper No. 6812838 (posted 22 May 2026), forthcoming Drexel Law Review 19.2 Judged from an abstract, summary or search listing only
  Abstract read: systematic review of publicly available US state-level contracts for acquiring AI systems, examining data access, intellectual property, stakeholder engagement, testing and cybersecurity; finds contracts often fail to address algorithmic-governance risks and proposes standard AI contract models. Closest to the design. The abstract does not mention a coding scheme, reliability, the number of contracts, authority over thresholds, input variables or model updates, decision-support systems specifically, a vendor versus in-house comparison, or non-US contracts.
• Kuehnert, Johnson, Dotan & Heidari, "Disclosure or Marketing? Analyzing the Efficacy of Vendor Self-reports for Vetting Public-sector AI," arXiv:2604.01332 (1 April 2026) Judged from an abstract, summary or search listing only
  Abstract read: interviews plus systematic analysis of completed GovAI Coalition FactSheets (vendor self-disclosures); finds them of limited value for evaluation. Analyses pre-contract disclosures, not contract terms on decision authority.
• Abdulbari, "The contractual vacuum: AI procurement and the erosion of good administration," Frontiers in Artificial Intelligence 9 (2026), doi:10.3389/frai.2026.1916534
  Opened: qualitative content analysis of EU model contractual clauses, UK guidelines, OECD and US federal guidance (2020 to June 2026), described by the author as 'a conceptual benchmark mapping exercise rather than an empirical statistical coding project'; proposes a normative accountability framework. Not a corpus of executed contracts.
• Coglianese & Lampmann, "Contracting for Algorithmic Accountability," SSRN Working Paper No. 3853208 (2021) Judged from an abstract, summary or search listing only Some details not confirmed
  Seen in search results only (not opened; year from SSRN id range, not confirmed). Title indicates a legal argument for using contract terms to secure accountability, in the line of Mulligan & Bamberger; not known to code a contract sample.

References

Sources behind the motivating claims:

• CNAF/CAF, DMDE 2026 transparency and source-code documentation (SRC-LD05-CNAF-DMDE-2026, original)
• DWP; Home Office; Ada Lovelace; Robodebt synthesis (SRC-R04-HQ-CONTESTABILITY-PROVENANCE-SYNTHESIS, no checked link to the original yet)

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld05_05_v2,
  author = {{SocioLens project}},
  title = {In contracts for public decision systems, who controls thresholds, variables and updates?},
  year = {2026},
  date = {2026-10-13},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld05-05/v2/}},
  note = {Research opportunity RO-LD05-05, version v2}
}

@misc{ro_ld05_05_nearest_1,
  title = {Mulligan \& Bamberger, "Procurement as Policy - Administrative Process for Machine Learning," Berkeley Technology Law Journal 34, 773 (2019)},
  url = {https://btlj.org/data/articles2019/34_3/03_Mulligan_Web.pdf},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld05_05_nearest_2,
  title = {Johnson, Silva, Leon, Eslami, Schwanke, Dotan \& Heidari, "Legacy Procurement Practices Shape How U.S. Cities Govern AI - Understanding Government Employees' Practices, Challenges, and Needs," Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025), 772–789; arXiv:2411.04994},
  url = {https://arxiv.org/abs/2411.04994},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld05_05_nearest_3,
  title = {Brauneis \& Goodman, "Algorithmic Transparency for the Smart City," Yale Journal of Law \& Technology 20, 103 (2018)},
  url = {https://yjolt.org/node/956},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld05_05_nearest_4,
  title = {Hudig, Kallina \& Singh, "'It's Just a Wild, Wild West': Harnessing Public Procurement as an AI Governance Mechanism," Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI 2026), 1–22, doi:10.1145/3772318.3791968},
  url = {https://doi.org/10.1145/3772318.3791968},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only; Some details not confirmed}
}

@misc{ro_ld05_05_nearest_5,
  title = {Cusenza, "From Policy to Practice: Closing the Gap in AI Public Procurement," SSRN Working Paper No. 6812838 (posted 22 May 2026), forthcoming Drexel Law Review 19.2},
  url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6812838},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld05_05_nearest_6,
  title = {Kuehnert, Johnson, Dotan \& Heidari, "Disclosure or Marketing? Analyzing the Efficacy of Vendor Self-reports for Vetting Public-sector AI," arXiv:2604.01332 (1 April 2026)},
  url = {https://arxiv.org/abs/2604.01332},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld05_05_nearest_7,
  title = {Abdulbari, "The contractual vacuum: AI procurement and the erosion of good administration," Frontiers in Artificial Intelligence 9 (2026), doi:10.3389/frai.2026.1916534},
  url = {https://doi.org/10.3389/frai.2026.1916534},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld05_05_nearest_8,
  title = {Coglianese \& Lampmann, "Contracting for Algorithmic Accountability," SSRN Working Paper No. 3853208 (2021)},
  url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3853208},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only; Some details not confirmed}
}

@misc{src_ld05_cnaf_dmde_2026,
  title = {CNAF/CAF, DMDE 2026 transparency and source-code documentation},
  url = {https://caf.fr/allocataires/actualites/actualites-nationales/controles-caf-un-nouvel-algorithme-plus-transparent},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_r04_hq_contestability_provenance_synthesis,
  title = {DWP; Home Office; Ada Lovelace; Robodebt synthesis},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}