Ai Applicant Reactions And Communications Openmatter Network

---
name: ai-applicant-reactions-and-communications-openmatter-network
title: AI applicant reactions & communications (Concerns 9–10)
description: 'Use when considering how candidates react to an AI/ML selection tool and what is communicated to candidates and stakeholders about it — Concerns 9-10 of Tippins, Oswald & McPhail (2021). Covers applicant reactions and their tenuous link to behavior, the faking-vs-training question for video interviews, pitfalls in reaction metrics, and what information can/should be shared with unsuccessful applicants and other stakeholders. Triggers: "candidate reactions to AI hiring", "applicant perceptions video interview", "faking vs training interview", "what to tell rejected candidates", "explain AI hiring decision", "what to share with stakeholders about selection".'
author: OpenMatter-Network
author_url: https://github.com/OpenMatter-Network/agent-io-skills/tree/main/ai-selection-legal-ethical/skills/ai-applicant-reactions-and-communications
license: MIT
version: 0.1.0
execution_mode: open
jurisdiction: us
practice: employment
language: en
tags: [community, io-psychology, ai-assessment, legal-ethical]
---

# AI applicant reactions & communications (Concerns 9–10)

Two linked concerns about **information flow** around an AI selection tool: how applicants experience
and react to it, and what the organization tells candidates and other stakeholders.

## Applicant experience and reactions (Concern 9)

Employers want selection that is **simple, quick, and engaging** to attract qualified candidates, and
technologically enhanced assessments are often **highly engaging** with little applicant effort. But
innovative methods raise reaction concerns.

### Pitfalls in reaction metrics
Many vendors collect applicant-reaction data, but the metrics are weak:
1. They **rarely incorporate the full range of organizational considerations** — vendors ask if the
   experience was *engaging*, but seldom whether applicants felt **job-relevant KSAOs were measured.**
2. **Good comparative data are rarely available** — reactions are used as a **marketing tool**, seldom
   compared to reactions to other tools.
3. It's hard to know the **local range** of reactions (how positive/negative) even with meta-analytic
   work (Hausknecht et al., 2004).
4. Reactions are **affected by how well candidates think they performed** — harder to gauge for novel
   games or tools with **no obviously correct answers.**
5. Whether a **job offer** followed is a huge driver of reactions — so consider measuring reactions
   **before** offers are extended.
6. Simply **asking** about the experience afterward may **alter** perceptions (e.g., prompting
   reflection on fairness/invasiveness of a video interview or scraped data).

### Impact on well-qualified candidates and the faking question
The applicant reaction–behavior link is **tenuous** — the "Achilles heel" of applicant-reactions
research (Sackett & Lievens, 2008). Still, organizations worry about effects on the **quality and
quantity** of applicants they attract. It's **unclear how candidates react** on learning their
selection hinged on an **unknown weighted combination of facial expressions, voice quality, mouse
clicks,** and other data — versus, say, their MBA from a top school. Reactions may matter more now
because applicants **amplify them via social media** (Twitter, Facebook, LinkedIn).
A **largely unresolved** issue: whether **training for a video interview** is possible, and if so,
whether it produces **invalid variance (faking/lying)** or **valid variance** (by ensuring candidates
understand what's expected). Some organizations **sidestep** specifics by simply informing candidates
whether the outcome indicates they met the employer's needs.

### Questions to ask (Concern 9)
- How should applicants **prepare** for the assessment (are practice sessions allowable)?
- What is being measured, is it **relevant** to the job, and **does the applicant know** it's being
  measured?
- **Why** was an applicant not selected — can big data and the ML algorithm provide explanations?
- How can applicants **improve** to become more qualified upon retesting?
- How do applicants evaluate organizations that **minimize personal interaction** (chatbots, avatars)?
- What are applicant reactions to innovative approaches, and how do they affect the employer's ability
  to **attract qualified candidates and its reputation**?

## Communications (Concern 10)

Managers (whose success depends on a competent workforce) care that **job-critical skills are being
measured**; labor organizations and advocacy groups care about **job relevance and fairness**; and
enforcement officials have a **statutory/regulatory interest** in what is measured and how.

### Sharing selection-procedure information
A key question is **what to tell people about how others were selected.** There have always been limits:
- Organizations are **unwilling to share anything that jeopardizes test use** (item content, scoring
  keys) or **increases legal/administrative challenge** (e.g., adverse-impact data).
- The **technical aspects** of evaluating measurement or predictive bias are **beyond the comprehension
  of most applicants and hiring managers** (regression slopes, factor loadings), and **bias in ML
  models is even harder to explain.**
- Most applicants want to know at a **basic level**: what **KSAOs** are measured, **how** they're
  evaluated, and — if unsuccessful — **what they can do to improve** next time.
- Test-prep materials typically describe the process, give tips, and sometimes offer practice
  questions; but it's **unclear what preparation** can be offered when selection rests on **face or
  voice characteristics.**

### Questions to ask (Concern 10)
- What information **can and should** be provided to **unsuccessful** applicants?
- What aspects of a selection procedure should an organization **share with a range of stakeholders**
  (manager, industry, clients, customers, shareholders)?

## Pitfalls

- Treating "engaging" reaction data as evidence of fairness or job-relevance perceptions.
- Using vendor reaction stats (marketing) without comparative or local data.
- Ignoring that AI decisions are hard to explain — leaving rejected candidates with no actionable
  feedback.
- Encouraging "training" for video interviews without resolving whether it introduces faking.
- Over-sharing (compromising test security) or under-sharing (eroding trust/justice).

## Checklist

- [ ] Reaction measurement captures job-relevance perceptions, not just engagement
- [ ] Reaction data benchmarked (comparative/local), not taken as marketing
- [ ] Offer-status and timing effects on reactions accounted for
- [ ] Faking-vs-training question for any video/behavioral interview addressed
- [ ] Basic, honest information (what's measured, how, how to improve) prepared for candidates
- [ ] Security-sensitive details (keys, items, adverse-impact data) protected
- [ ] Stakeholder communication plan defined (managers, regulators, advocacy groups)

## See also

`ai-candidate-data-control` · `ai-selection-ethics` · `ai-selection-legal-landscape` ·
`ai-claims-and-stakeholder-audit` (second-party effects, justice) ·
`administration-documentation` (candidate communications, feedback)

*Source: Tippins, Oswald & McPhail (2021), Concerns: "Applicant Experience and Reactions" and
"Communications."*