facct-related-work

FAccT Related Work

Use this to audit novelty and disciplinary reach. FAccT reviewers come from different fields,

and each expects to see the nearest work in their lane engaged. A fairness-metrics reviewer

wants the ML fairness literature; a legal reviewer wants the relevant law and governance work; an

STS/critical reviewer wants the theory you are (often implicitly) drawing on. The fastest way to

lose a mixed panel is a bibliography that is deep in one field and blank in the others. Reopen the

current CFP for anonymity, dual-submission, and prior-publication rules before advising.

Positioning checks

• Name the FAccT novelty precisely. What is new: a fairness/transparency method, an empirical

harm nobody had measured, an accountability framework, a reframing of a taken-for-granted

construct, a qualitative account of an affected community, or a legal-technical synthesis?

• Cover the disciplinary lanes (see table). A paper that cites only its home field reads as

unaware of the interdisciplinary conversation FAccT exists to host.

• Write delta-first. Each closely related work gets one sentence naming what it did and one

naming what you do differently — across the divide where relevant ("the ML work optimized the

metric; the legal work named the right; we connect them by...").

• Cite borrowed constructs to their real origin. If you use "disparate impact," "contestability,"

"situated knowledge," or "the right to explanation," cite the field that coined it, not a

second-hand ML paper — mixed reviewers notice mis-attribution instantly.

• Preserve mutual anonymity. Cite your own prior work in the third person; never link reviewers

to an identity-revealing preprint, repository, project page, or the arXiv version of this paper.

• Declare overlap with a prior workshop/CRAFT version or concurrent submission; do not re-submit

archival work as new.

FAccT literature lanes

| Lane | Typical venues / bodies | What FAccT reviewers check |

|---|---|---|

| Algorithmic fairness & ML | FAccT, NeurIPS/ICML/ICLR, JMLR | Whether the nearest fairness measure/method is compared or distinguished |

| HCI & human factors | CHI, CSCW | Whether prior work on how people use/contest the system is credited |

| Law, policy & governance | Law reviews, policy journals, regulation | Whether the relevant legal doctrine or regulatory instrument is engaged correctly |

| STS & critical theory | STS venues, critical data/algorithm studies | Whether the theoretical lineage of your critique is named, not just gestured at |

| Documentation & accountability infra | Prior FAccT (datasheets, model cards, audits) | Whether existing documentation/audit frameworks are built on rather than reinvented |

| Domain literature (health, credit, hiring...) | The applied field | Whether you understand the real decision context you study |

A bibliography that reaches across at least the lanes your claim touches signals command of the

interdisciplinary field; one confined to a single lane invites the "unaware of the neighbor

discipline" critique that a mixed panel is unusually well-positioned to make.

Delta-first positioning vignette

Suppose the paper proposes a contestability mechanism for automated benefit decisions. Its

neighbors span lanes: an ML paper on algorithmic recourse (technique, no institutional grounding),

an HCI study of how claimants experience appeals (experience, no mechanism), and legal scholarship

on due-process rights in automated administration (the right, no system). The novelty sentence names

all three contrasts — a mechanism where recourse gave only a technique, grounded in the *appeal

experience HCI documented, realizing the due-process right* the law names — which is exactly the

cross-lane synthesis FAccT rewards.

Concurrent and prior-version judgment calls

[Concurrent arXiv work]   cite neutrally, state the difference, avoid unverifiable priority claims;
                          keep the citation mutually anonymous
[Your workshop/CRAFT version]  usually non-archival and citable, but confirm against the current CFP
                          and phrase so anonymity survives
[Prior short/position version] declare the overlap and state what the full paper adds beyond it
[Archival status unclear]  declare the overlap in the submission form rather than guessing

Eligibility red flags

• Substantial text overlap with a published paper by the same authors (self-plagiarism risk).
• A "new" audit that re-reports a prior dataset's disparities without a new question or population.
• Citations confined to one discipline while the paper claims interdisciplinary contribution — the

clearest signal that the interdisciplinarity is a label, not a method.

Output format

[Eligibility] clear / needs declaration / risky
[Lanes covered] <ML-fairness / HCI / law-policy / STS-critical / documentation / domain>
[Nearest 3 works] <work -> one-line cross-lane delta>
[Construct attribution] <borrowed term -> cited to its real origin? yes/no>
[Archival-overlap risk] <none / declare: what>
[Novelty sentence] <FAccT-ready contribution contrast across the relevant lanes>