| name | ai-ml-methodology-evaluation |
| description | Use when evaluating whether the machine-learning methodology behind a selection tool is appropriate and interpretable — Concern 4 of Tippins, Oswald & McPhail (2021). Covers ML interpretability and the "black box," explainable AI (XAI), evaluation metrics (MSE, confusion matrix, ROC/AUC), the high variable-to-case ratio in big data, the difficulty of comparing ML results to traditional methods, and the I-O psychology education gap. Triggers: "is the ML methodology appropriate", "black box hiring model", "explainable AI selection", "ROC AUC confusion matrix", "how to evaluate a machine learning model", "compare ML to regression validity", "I-O psychologists machine learning training". |
| version | 1.0.0 |
| author | OpenMatter-Network |
| license | MIT |
| category | research |
| tags | ["Community","io-psychology","ai-assessment","legal-ethical"] |
| permissions | [] |
AI methodology evaluation (Concern 4)
Technologically enhanced selection may apply ML to thousands of data points, weighting and
combining them into predictions that are (a) complex (interactive, nonlinear) and (b) intended to
hold up in new datasets (cross-validation). Evaluating whether that methodology is appropriate
requires understanding methods that are unfamiliar to many I-O psychologists.
The interpretability problem
- ML results can be obscure, counterintuitive, or impossible to interpret: random forests base
predictions on hundreds of "trees"; neural nets tune arbitrary, layered configurations of
"neurons." Understanding what goes on inside the black box is problematic.
- Explainable AI (XAI) is advancing (Ribeiro et al., 2016; Kaleghi, 2019), but the authors are
unaware of breakthroughs in complex selection prediction that yield new insight for theory or
practice. Continued XAI development will be extremely important for achieving transparency in
personnel selection.
- When AI methods are used in talent assessment, the underlying predictive structures are generally
inaccessible or proprietary, making it hard to interpret results for ourselves, stakeholders,
the legal community, and beyond.
Evaluation metrics (know them, or get help)
AI models generate metrics unfamiliar to many I-O psychologists:
- Mean squared error (MSE) — minimized in many estimation methods.
- Confusion matrix — actual vs. predicted classifications.
- ROC curve and area under the curve (AUC) — correct/incorrect classifications under different
cut scores.
Many I-O psychologists lack the background to interpret and evaluate these and need additional
methodological education.
The variable-to-case ratio
In big-data applications the ratio of variables to cases is often high (e.g., 30 variables per
case), the reverse of traditional analyses (≈1 variable per 30 cases). Traditional statistics are
literally impossible when variables exceed cases (e.g., the variance–covariance matrix won't
invert) — which is why ML is necessary to operate on big data (unless variables are reduced via
composites, factor/scale scores, etc.). Even with cross-validation, variable-driven interpretation
(as in regression coefficients) differs and often remains in a black box.
You can't yet compare AI to traditional methods cleanly
- For traditional measures, there's a large meta-analytic literature giving typical correlation
ranges by instrument/construct (Schmidt & Hunter, 1998), plus effect-size benchmarks (Cohen, 1988;
Bosco et al., 2015). I-O psychologists would be highly skeptical of a .75 correlation between a
structured interview and overall performance.
- But the available literature on ML application and efficacy in selection is insufficient to
provide a basis for comparison. Because many I-O psychologists lack a fundamental understanding of
how different ML algorithms work — their assumptions, boundary conditions, and metrics — it is
challenging to compare ML results to one another and to traditional multiple regression.
The I-O education gap (a recurring theme)
ML methods may be unfamiliar or "completely foreign" to many I-O psychologists, yet their strong
training in psychological measurement and psychometrics positions them to extend into ML and
participate in critical conversations: whether big-data analysis is necessary, whether it provided
meaningful prediction and a substantial improvement, and whether/when predictions generalize. More
ML education is "clearly needed" (Aiken & Hanges, 2015; Oswald & Putka, 2016, 2017), including changes
to I-O graduate curricula.
Questions to ask (from the article)
- What are the appropriate criteria for evaluating technologically enhanced models?
- What is the best approach to choosing and using an ML algorithm?
- What defines acceptable results when ML is used? What is an acceptable level of prediction?
- How can we determine if predictive results are based on idiosyncrasies of the sample the model
was built on — how generalizable are they to other samples?
- What continuing education and changes to I-O graduate education are needed to prepare
psychologists to develop, research, and evaluate these tools?
Pitfalls
- Accepting a model's reported accuracy without understanding its metric (e.g., AUC) or cross-validation.
- Comparing an ML model's performance to traditional validities without a common, defensible baseline.
- Treating a proprietary black box as evaluable from outputs alone.
- Mistaking in-sample fit for generalizable prediction (sample idiosyncrasy).
- Deferring entirely to data scientists instead of building enough ML literacy to ask hard questions.
Checklist
See also
ai-validity-evidence · ai-reliability · ai-model-development-audit ·
ai-model-outputs-audit ·
criterion-related-validation (data analysis, cross-validation) ·
ai-extending-professional-standards
Source: Tippins, Oswald & McPhail (2021), Concern: "Appropriate Methodology."
