AI Ml Methodology Evaluation

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

• Model interpretability/XAI status assessed; black-box limits documented
• Reported metrics (MSE/confusion matrix/ROC-AUC) understood and scrutinized
• Variable-to-case ratio and its analytic implications considered
• Generalizability vs. sample idiosyncrasy probed (cross-validation appropriateness)
• Comparison to traditional/meta-analytic baselines attempted (and its limits acknowledged)
• ML expertise secured (own or collaborator) sufficient to evaluate the methodology

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."