| name | ai-candidate-data-control |
| description | Use when an AI/ML selection tool uses data the candidate does not control or did not knowingly provide — scraped social-media/Internet data, or incidental data like facial micro-expressions, voice, and appearance — Concern 8 of Tippins, Oswald & McPhail (2021). Covers the loss of applicant control, job-irrelevance and "is it fair," reputation-scrubbing services and adverse impact, the absence of a clear legal/ethical rule, informed consent (Illinois AIVI Act), and the range of policy approaches. Triggers: "scraped social media hiring", "data outside applicant control", "facial appearance in hiring", "is it fair to use this data", "informed consent for AI hiring data", "online reputation scrubbing". |
| version | 1.0.0 |
| author | OpenMatter-Network |
| license | MIT |
| category | research |
| tags | ["Community","io-psychology","ai-assessment","legal-ethical"] |
| permissions | [] |
AI candidate data control (Concern 8)
Traditionally, applicants control to a large degree what they present to an employer — effort on
ability tests, answers on personality/SJT measures, demeanor in interviews, resume and application
content. Using information outside those sources is not new ("word of mouth," references,
background and credit checks). What's new with AI is the scale and the loss of applicant control.
What changed
- Scraped data outside the applicant's control. Employers can search the Internet and large
databases for evidence of "inappropriate behavior" (poor judgment), but such data often contain
irrelevant information — demographics (Zhang et al., 2020), political affiliation (Roth et al.,
2020). Applicants have increasingly less control over the type and relevance of personal data
organizations extract from social media. They may try impression management via profiles
(Schroeder & Cavanaugh, 2018), but in some cases they did not post the information themselves, it
was substantially altered, or it was posted without intent to be shared. Online information is
often suspect, dated, or lacking context.
- Reputation-scrubbing services and adverse impact. Companies that help manage online reputations
are not free; to the extent their availability/affordability varies by race/ethnicity or other
demographics, these "scrubbing" services may contribute to adverse impact that is difficult to
detect.
- Incidental data that candidates cannot alter. Images, video, audio, facial micro-expressions, and
voice purport to convey job-relevant emotions — but physical appearance beyond grooming is outside
most people's control (skin color, voice timbre, basic speech patterns, the features of one's face).
This raises special problems for people who look or speak differently due to cultural
differences (minorities, immigrants), physical differences (disabilities, diseases, injuries),
gender, and age.
The crux: "Is it fair?"
Irrelevant variables may well predict performance; the essential question is "Is it fair?" There is
no law or guideline requiring an employer to use only data presented by the candidate (except in
the realm of privacy statutes), and no specific ethical standard requiring it either. Yet there
is a moral dilemma. The article lays out a spectrum of approaches:
- One extreme — press ahead. Use this kind of data, on the rationale that applicants have never
controlled everything an employer sees and uses.
- Other extreme — use no data beyond the candidate's control. This would entail careful review even
of traditional forms like biodata.
- A moderate approach — informed consent. Being legislated in laws like the Illinois AI Video
Interview Act (
ai-selection-legal-landscape): require informed consent before an employer
bases a selection decision on data beyond the applicant's control.
Questions to ask (from the article)
- Is it fair to use data that are outside the control of an applicant?
- Should employers seek out data on the Internet at all?
- Would there be legal issues associated with not seeking information about some behaviors (e.g.,
poor judgment, behavioral deviancy, CWBs)?
- How long should applicants' past failures or mistakes affect their future job prospects, and what
mistakes should be considered (criminal history, online behavior, early-life behavior)?
Pitfalls
- Using scraped data without confirming relevance, recency, accuracy, or that the candidate authored it.
- Ignoring that paid reputation-scrubbing can create undetectable adverse impact.
- Scoring appearance/voice features that candidates cannot change (disability/cultural/age fairness).
- Assuming "no law against it" settles the fairness/ethics question (it doesn't).
- Treating implied consent as covering data the candidate doesn't know is being collected (see
ai-selection-ethics).
Checklist
See also
ai-selection-ethics (informed consent) · ai-applicant-reactions-and-communications ·
ai-selection-legal-landscape (Illinois AIVI Act, privacy)
· ai-reliability (appearance/disability) · candidate-accommodations
(disability, linguistic/cultural) · ai-claims-and-stakeholder-audit
Source: Tippins, Oswald & McPhail (2021), Concern: "Control Over the Data Presented to an Employer."
