# Ai Selection Tech Data Algorithms > >- - **Type:** Skill - **Install:** `agentstack add skill-openmatter-network-agent-io-skills-ai-selection-tech-data-algorithms` - **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-selection-tech-data-algorithms ## Install ```sh agentstack add skill-openmatter-network-agent-io-skills-ai-selection-tech-data-algorithms ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # AI selection: technologies, data, algorithms The starting lens for the whole "scientific, legal, and ethical concerns" framework. Before asking "is this AI tool any good?", **decompose it into three independent parts** and evaluate each separately (the "modular approach," Lievens & Sackett, 2017). Conflating them is the root error behind most overclaiming. ## The three parts ### 1. Technologies — independent of the constructs measured Examples: online games, video interviews, social media, gamification, VR. Technologies are **independent from the constructs being measured** and **should not be confused with them** (Arthur & Villado, 2008; Campbell & Fiske, 1959). Most formats can measure a wide range of constructs, and most constructs can be assessed by many technologies. **Consequence (state this in any evaluation):** *a new technology cannot be said to be universally valid.* "Our video-interview platform is validated" is a category error — validity is a property of the **inferences about the constructs measured** in a specific use, not of the technology. Demand evidence about **which job-relevant constructs** are measured, which then informs validity and fairness. ### 2. Data — a continuum from intentional to incidental Data vary along a continuum (Oswald, 2020): - **Intentional** — traditional, controllable responses to a prompt (a test item, an interview answer). - **Incidental** — less intentional/controllable, requiring little or no applicant effort (and sometimes little applicant control): social-media posts, facial movements, voice characteristics in a video, mouse clicks, response times. Less obtrusive technologies tend to collect **more incidental data, in massive amounts** (game data = every click/decision/scenario; video = continuous voice and facial features; "big data" pulled from resumes, emails, social media). The more incidental the data, the more the concerns about job relevance, control, consent, and fairness intensify (see `ai-candidate-data-control`). ### 3. Algorithms — AI ⊃ ML ⊃ deep learning - **AI** — broad term for computer procedures that mimic human decisions/processes/outcomes closely enough to appear intelligent. - **Machine learning (ML)** — subset: the mathematical/statistical procedures underlying these tools. - **Deep learning** — subset of ML: neural-network-based. - **Supervised** learning predicts a **criterion** (the criterion "supervises" how predictors are used — e.g., predicting supervisory performance ratings); **unsupervised** learning groups people/cases into **clusters** with **no criterion** (e.g., applicants like/unlike high performers). The "learning" happens when algorithms are first exposed to a **training set**; the model is judged on an independent **test set** (a hold-out sample, k-fold folds, or newly collected data). ## A key claim to carry forward There are hundreds of ML algorithms, and **different algorithms often make highly similar predictions with similar overall accuracy** (Domingos, 2012). So in personnel selection, the effectiveness of ML prediction/clustering is **more likely driven by the availability of high-quality data than by which ML algorithm is chosen.** Whether the advantages of a large number of predictors offset the disadvantages of "messy" data must be determined case by case. **Accurate, well-justified predictions depend on good measurement processes and good data — not on algorithm sophistication.** Don't let a vendor's algorithm story distract from data-quality and construct questions. ## How to use this skill 1. **Name the three parts** of the tool under review (which technology, what data on the intentional–incidental continuum, which algorithm type — supervised/unsupervised). 2. **Reject "the technology is valid"** framing; redirect to *what constructs are measured and is that measurement job-relevant, reliable, valid, and fair?* 3. **Locate the data on the continuum** — the more incidental, the more you escalate the control/consent/fairness concerns. 4. **Treat algorithm choice as secondary to data quality**; probe the data-generation and construct-relevance story. 5. Proceed to the legal frame (`ai-selection-legal-landscape`) and the 11 concerns. ## Pitfalls - Accepting "this technology is validated" as if validity were a property of the medium. - Being dazzled by the algorithm while ignoring data quality and construct relevance. - Missing that incidental data (face, voice, social media) drives the hardest legal/ethical issues. - Assuming supervised = good and unsupervised = bad (each has distinct evaluation needs). ## Checklist - [ ] Technology, data type, and algorithm type named and evaluated separately - [ ] "Universally valid technology" claim rejected; construct/inference framing substituted - [ ] Data located on the intentional–incidental continuum; control/consent flags raised if incidental - [ ] Supervised vs. unsupervised identified; training/test-set basis confirmed - [ ] Data-quality story probed as primary; algorithm choice treated as secondary ## See also `ai-selection-legal-landscape` · all 11 concern skills · `ai-validity-evidence` · `ai-input-data-and-design-audit` (the audit counterpart) · `validation-planning` *Source: Tippins, Oswald & McPhail (2021), "New Forms of Assessment."* ## 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-selection-tech-data-algorithms - 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. 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