# Ai Applicant Reactions And Communications > >- - **Type:** Skill - **Install:** `agentstack add skill-openmatter-network-agent-io-skills-ai-applicant-reactions-and-communications` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [OpenMatter-Network](https://agentstack.voostack.com/s/openmatter-network) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [OpenMatter-Network](https://github.com/OpenMatter-Network) - **Source:** https://github.com/OpenMatter-Network/agent-io-skills/tree/main/ai-selection-legal-ethical/skills/ai-applicant-reactions-and-communications ## Install ```sh agentstack add skill-openmatter-network-agent-io-skills-ai-applicant-reactions-and-communications ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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."* ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [OpenMatter-Network](https://github.com/OpenMatter-Network) - **Source:** [OpenMatter-Network/agent-io-skills](https://github.com/OpenMatter-Network/agent-io-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-openmatter-network-agent-io-skills-ai-applicant-reactions-and-communications - Seller: https://agentstack.voostack.com/s/openmatter-network - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.