Effective Use of Machine Learning in OHS Decision Making
Expert-defined terms from the Advanced AI OHS Professional Certification (Part II) (Canada) course at LearnUNI. Free to read, free to share, paired with a professional course.
Adaptive Learning #
Adaptive Learning
Concept #
Dynamic adjustment of model parameters in response to new data. Related terms: online learning, incremental training. Explanation: In OHS, adaptive learning enables safety models to refine risk predictions as workplace conditions evolve, improving relevance without full retraining cycles.
Algorithmic Bias #
Algorithmic Bias
Concept #
Systematic error favoring certain outcomes due to data or design. Related terms: fairness, discrimination. Explanation: Bias can skew injury forecasts, leading to inequitable resource allocation; mitigation requires diverse datasets and bias‑aware validation.
Anomaly Detection #
Anomaly Detection
Concept #
Identifying data points that deviate markedly from normal patterns. Related terms: Outlier analysis, novelty detection. Explanation: Detects sudden spikes in near‑miss reports, signaling emerging hazards before they cause incidents.
Artificial Neural Network (ANN) #
Artificial Neural Network (ANN)
Concept #
Computational model inspired by biological neurons. Related terms: Deep learning, multilayer perceptron. Explanation: ANNs model complex interactions among exposure, behavior, and environment to predict occupational injury likelihoods.
Attribute Selection #
Attribute Selection
Concept #
Process of choosing relevant features for model input. Related terms: feature engineering, dimensionality reduction. Explanation: Selecting ergonomics, shift length, and equipment age improves model accuracy while reducing overfitting risk.
Baseline Model #
Baseline Model
Concept #
Simple reference model against which advanced models are compared. Related terms: benchmark, naive predictor. Explanation: A logistic regression using historical injury counts serves as a baseline to assess gains from more complex ML approaches.
Batch Learning #
Batch Learning
Concept #
Training a model on a complete dataset at once. Related terms: Offline training, full‑dataset learning. Explanation: Useful for periodic OHS risk assessments where data accumulation justifies comprehensive re‑training.
Bias‑Variance Tradeoff #
Bias‑Variance Tradeoff
Concept #
Balancing model simplicity and flexibility to minimize total error. Related terms: overfitting, underfitting. Explanation: In safety prediction, high variance may cause erratic hazard alerts, while high bias can miss subtle risk patterns.
Calibration #
Calibration
Concept #
Aligning predicted probabilities with observed frequencies. Related terms: Reliability diagram, probability scaling. Explanation: Well‑calibrated OHS models ensure that a 70% risk estimate truly reflects a 70% occurrence rate, supporting credible decision‑making.
Classification #
Classification
Concept #
Assigning observations to discrete categories. Related terms: categorical prediction, label assignment. Explanation: Classifies work tasks as “low”, “moderate”, or “high” injury risk, enabling targeted preventive measures.
Clustering #
Clustering
Concept #
Grouping similar data points without predefined labels. Related terms: Unsupervised learning, k‑means. Explanation: Segments workers by exposure profiles, revealing hidden sub‑populations that may benefit from tailored safety programs.
Confusion Matrix #
Confusion Matrix
Concept #
Table summarizing true vs. Predicted classifications. Related terms: accuracy, precision, recall. Explanation: Provides OHS analysts with insight into false positive alerts (unnecessary interventions) and false negatives (missed hazards).
Cross‑Validation #
Cross‑Validation
Concept #
Technique for estimating model performance on unseen data. Related terms: K‑fold, holdout validation. Explanation: Repeatedly splits OHS datasets to ensure risk predictions generalize across different sites or time periods.
Data Augmentation #
Data Augmentation
Concept #
Generating synthetic data to expand training sets. Related terms: oversampling, synthetic minority oversampling technique (SMOTE). Explanation: Balances rare severe injury cases with generated samples, improving model sensitivity to critical events.
Data Governance #
Data Governance
Concept #
Policies and procedures for managing data quality, security, and compliance. Related terms: data stewardship, privacy. Explanation: Ensures OHS datasets respect worker confidentiality while remaining reliable for machine‑learning analysis.
Data Imbalance #
Data Imbalance
Concept #
Unequal representation of classes within a dataset. Related terms: class skew, minority class. Explanation: Injury events are often far fewer than non‑injury records; imbalance requires resampling or cost‑sensitive methods to avoid biased predictions.
Data Integration #
Data Integration
Concept #
Combining disparate data sources into a unified dataset. Related terms: ETL, data fusion. Explanation: Merges incident reports, sensor readings, and HR records to enrich predictive features for OHS decision support.
Decision Threshold #
Decision Threshold
Concept #
Probability cut‑off that determines class assignment. Related terms: operating point, ROC curve. Explanation: Adjusting the threshold influences the trade‑off between alert frequency and missed hazards in safety monitoring systems.
Deep Learning #
Deep Learning
Concept #
Subset of machine learning using multi‑layer neural networks. Related terms: Convolutional neural network (CNN), representation learning. Explanation: Processes raw video from wearable cameras to automatically detect unsafe postures without manual coding.
Dimensionality Reduction #
Dimensionality Reduction
Concept #
Reducing the number of input variables while preserving information. Related terms: Principal component analysis (PCA), feature compression. Explanation: Simplifies high‑frequency sensor data into core movement patterns for faster OHS risk scoring.
Ensemble Methods #
Ensemble Methods
Concept #
Combining multiple models to improve predictive performance. Related terms: Random forest, boosting. Explanation: Merges decision trees trained on different hazard factors, yielding a more robust injury probability estimate.
Evaluation Metric #
Evaluation Metric
Concept #
Quantitative measure of model performance. Related terms: F1‑score, area under the curve (AUC). Explanation: In OHS, the AUC indicates how well a model discriminates between safe and unsafe conditions across all thresholds.
Explainable AI (XAI) #
Explainable AI (XAI)
Concept #
Techniques that make model decisions understandable to humans. Related terms: model interpretability, SHAP values. Explanation: Provides safety managers with clear reasons—such as high vibration exposure—behind a predicted injury risk, supporting transparent actions.
Feature Engineering #
Feature Engineering
Concept #
Creating informative variables from raw data. Related terms: derived features, transformation. Explanation: Converts timestamped shift logs into fatigue indices, enhancing the model’s ability to predict fatigue‑related incidents.
Feature Importance #
Feature Importance
Concept #
Ranking of input variables based on their impact on predictions. Related terms: variable contribution, permutation importance. Explanation: Highlights that ergonomic score and PPE compliance are top drivers of predicted injury rates, guiding resource allocation.
Gaussian Process #
Gaussian Process
Concept #
Probabilistic model providing predictions with uncertainty estimates. Related terms: kernel methods, Bayesian regression. Explanation: Offers OHS analysts confidence intervals around risk forecasts, useful for risk‑based decision thresholds.
Generalization #
Generalization
Concept #
Model’s ability to perform well on unseen data. Related terms: out‑of‑sample performance, transferability. Explanation: A well‑generalized safety model maintains accuracy when applied to new facilities or evolving work practices.
Gradient Descent #
Gradient Descent
Concept #
Optimization algorithm that iteratively reduces error. Related terms: learning rate, stochastic gradient descent. Explanation: Trains OHS predictive models by minimizing the difference between predicted and actual injury counts across epochs.
Hyperparameter Tuning #
Hyperparameter Tuning
Concept #
Adjusting non‑learnable settings to improve model performance. Related terms: Grid search, random search. Explanation: Finds the optimal number of trees in a random forest that balances OHS prediction accuracy with computational cost.
Imbalanced Classification #
Imbalanced Classification
Concept #
Classification tasks where one class dominates. Related terms: cost‑sensitive learning, resampling. Explanation: In injury prediction, the “no‑injury” class overwhelms the “injury” class, requiring specialized strategies to avoid trivial models.
Inference #
Inference
Concept #
Applying a trained model to new data to generate predictions. Related terms: prediction, scoring. Explanation: Real‑time inference on sensor streams can trigger immediate alerts when unsafe conditions are detected.
Instance‑Based Learning #
Instance‑Based Learning
Concept #
Learning algorithms that compare new cases to stored examples. Related terms: K‑nearest neighbors (k‑NN), memory‑based. Explanation: Matches a current work scenario to past incidents to suggest similar preventive actions.
Interpretability #
Interpretability
Concept #
Degree to which a human can understand the cause of a model’s output. Related terms: transparency, XAI. Explanation: Essential for OHS stakeholders to trust automated risk scores and act on them responsibly.
Kernel Trick #
Kernel Trick
Concept #
Technique that enables linear algorithms to learn non‑linear patterns. Related terms: Support vector machine (SVM), RBF kernel. Explanation: Allows OHS models to capture complex relationships between temperature, humidity, and slip‑related injuries.
K #
Means Clustering
Concept #
Partitioning data into K clusters based on distance to centroids. Related terms: Unsupervised learning, centroid. Explanation: Groups job tasks by similarity in exposure metrics, revealing hidden high‑risk clusters for targeted audits.
Label Encoding #
Label Encoding
Concept #
Converting categorical variables into numeric form. Related terms: One‑hot encoding, ordinal encoding. Explanation: Transforms equipment type labels into numbers for inclusion in regression models predicting equipment‑related incidents.
Learning Curve #
Learning Curve
Concept #
Plot showing model performance versus training data size. Related terms: sample efficiency, convergence. Explanation: Indicates whether additional OHS data will meaningfully improve risk prediction or if model capacity is saturated.
Logistic Regression #
Logistic Regression
Concept #
Statistical model for binary outcome prediction. Related terms: logit, probability modeling. Explanation: Provides a baseline OHS model estimating the odds of injury based on factors such as hours worked and safety training completion.
Loss Function #
Loss Function
Concept #
Metric that quantifies error during model training. Related terms: cost function, optimization target. Explanation: Mean squared error penalizes large deviations between predicted and actual injury counts, guiding the learning process.
Machine Learning Pipeline #
Machine Learning Pipeline
Concept #
Structured sequence of steps from data ingestion to model deployment. Related terms: workflow, orchestration. Explanation: In OHS, the pipeline may include data cleaning, feature extraction, model training, validation, and automated risk reporting.
Model Drift #
Model Drift
Concept #
Degradation of model performance over time due to changing data patterns. Related terms: concept drift, retraining. Explanation: As new safety protocols are introduced, a previously accurate OHS model may misclassify hazards unless monitored and updated.
Model Registry #
Model Registry
Concept #
Centralized catalog of trained models and their metadata. Related terms: Versioning, artifact store. Explanation: Allows OHS teams to track which risk model was used for a specific audit, supporting reproducibility and compliance.
Multiclass Classification #
Multiclass Classification
Concept #
Predicting one of three or more categories. Related terms: softmax, one‑vs‑rest. Explanation: Classifies incidents into “minor”, “major”, and “critical” severity levels, enabling differentiated response plans.
Neural Architecture Search #
Neural Architecture Search
Concept #
Automated method for designing optimal neural network structures. Related terms: AutoML, hyperparameter optimization. Explanation: Finds the most effective deep‑learning layout for processing high‑frequency vibration data in equipment‑safety monitoring.
Normalization #
Normalization
Concept #
Scaling numeric features to a common range. Related terms: standardization, min‑max scaling. Explanation: Ensures that exposure intensity and shift length contribute proportionally to OHS model training.
One‑Hot Encoding #
One‑Hot Encoding
Concept #
Representing categorical variables as binary vectors. Related terms: Dummy variables, indicator matrix. Explanation: Encodes shift type (day, night, swing) for inclusion in a logistic regression predicting fatigue‑related injuries.
Outlier Removal #
Outlier Removal
Concept #
Excluding extreme values that may distort model training. Related terms: noise filtering, data cleaning. Explanation: Removes erroneous sensor spikes that could falsely indicate hazardous conditions in safety dashboards.
Overfitting #
Overfitting
Concept #
Model captures noise rather than underlying pattern, performing poorly on new data. Related terms: high variance, regularization. Explanation: An overfit OHS model may flag routine tasks as high risk, leading to alert fatigue among supervisors.
Parameter #
Parameter
Concept #
Learnable coefficient within a model. Related terms: Weight, bias term. Explanation: In a linear OHS risk model, each predictor (e.G., Exposure level) has an associated weight indicating its influence on injury probability.
Precision #
Precision
Concept #
Proportion of positive predictions that are correct. Related terms: positive predictive value, accuracy. Explanation: High precision in safety alerts means most warnings correspond to genuine hazards, preserving trust in the system.
Probabilistic Forecasting #
Probabilistic Forecasting
Concept #
Generating predictions expressed as probability distributions. Related terms: uncertainty quantification, predictive intervals. Explanation: Provides OHS managers with a range (e.G., 10‑15% Chance of a slip) rather than a single deterministic value, supporting risk‑based decision making.
Principal Component Analysis (PCA) #
Principal Component Analysis (PCA)
Concept #
Technique that transforms correlated variables into uncorrelated components. Related terms: eigenvectors, dimensionality reduction. Explanation: Condenses dozens of sensor metrics into a few principal components that capture most variance in workplace motion patterns.
Probabilistic Graphical Model #
Probabilistic Graphical Model
Concept #
Representation of variables and their conditional dependencies using graphs. Related terms: Bayesian network, Markov model. Explanation: Models causal pathways from equipment failure to injury, enabling inference about the most likely root causes.
Recall #
Recall
Concept #
Proportion of actual positives correctly identified. Related terms: sensitivity, true positive rate. Explanation: In OHS, high recall ensures most true hazards trigger alerts, reducing missed‑incident risk.
Regularization #
Regularization
Concept #
Adding a penalty to the loss function to discourage complex models. Related terms: L1, L2, ridge. Explanation: Prevents OHS models from over‑reacting to noisy data, yielding smoother risk scores.
Reinforcement Learning #
Reinforcement Learning
Concept #
Learning optimal actions through trial‑and‑error interaction with an environment. Related terms: policy, reward function. Explanation: Can simulate safety interventions, rewarding actions that reduce predicted injury probability, to suggest optimal preventive strategies.
Resampling #
Resampling
Concept #
Adjusting the training dataset by oversampling minority class or undersampling majority class. Related terms: SMOTE, bootstrap. Explanation: Balances scarce severe injury records with duplicated or synthetic examples, improving OHS model sensitivity to critical events.
Risk Matrix #
Risk Matrix
Concept #
Visual tool that plots likelihood against severity. Related terms: heat map, risk assessment. Explanation: Machine‑learning outputs can populate a dynamic risk matrix, automatically updating positions as new data arrives.
ROC Curve #
ROC Curve
Concept #
Plot of true positive rate versus false positive rate at various thresholds. Related terms: AUC, diagnostic ability. Explanation: Evaluates OHS model’s discriminative power; a curve closer to the top‑left corner indicates better hazard detection.
Sampling Bias #
Sampling Bias
Concept #
Systematic error introduced when the sample is not representative of the population. Related terms: selection bias, coverage error. Explanation: If only high‑visibility incidents are recorded, the model may underestimate hidden low‑visibility risks.
Scalable Architecture #
Scalable Architecture
Concept #
System design that can handle growth in data volume and processing demands. Related terms: distributed computing, cloud deployment. Explanation: Enables OHS predictive services to expand from a single plant to a national network without performance loss.
Segmentation #
Segmentation
Concept #
Dividing a dataset into distinct groups for targeted analysis. Related terms: Clustering, stratification. Explanation: Segments workers by age and experience to tailor safety messaging based on model‑identified risk differentials.
Shapley Additive Explanations (SHAP) #
Shapley Additive Explanations (SHAP)
Concept #
Method that attributes contribution of each feature to a specific prediction. Related terms: feature attribution, explainability. Explanation: Shows that high noise exposure contributed 0.25 To a worker’s predicted injury risk, supporting transparent communication.
Signal‑to‑Noise Ratio (SNR) #
Signal‑to‑Noise Ratio (SNR)
Concept #
Measure of useful information relative to background variability. Related terms: data quality, filtering. Explanation: High SNR in vibration sensor data improves the reliability of ML‑derived fatigue risk scores.
Simulation Modeling #
Simulation Modeling
Concept #
Creating virtual representations of real‑world processes to test scenarios. Related terms: digital twin, Monte Carlo. Explanation: Generates synthetic incident streams to stress‑test OHS predictive models before deployment.
Smart Sensor #
Smart Sensor
Concept #
Device that captures data and performs edge analytics. Related terms: IoT, embedded AI. Explanation: Sends real‑time temperature and humidity readings to an OHS risk model, enabling instantaneous heat‑stress alerts.
Softmax Function #
Softmax Function
Concept #
Normalizes a vector of real numbers into probabilities that sum to one. Related terms: multiclass output, activation. Explanation: Used in OHS neural networks to output probabilities for each incident severity class.
Spatial Analysis #
Spatial Analysis
Concept #
Examination of geographic or layout‑based patterns. Related terms: heat mapping, GIS. Explanation: Identifies zones within a facility where injury predictions consistently exceed thresholds, guiding layout redesign.
Specificity #
Specificity
Concept #
Proportion of true negatives correctly identified. Related terms: true negative rate, precision (negative). Explanation: High specificity in OHS alerts reduces unnecessary investigations of safe areas, conserving resources.
Stochastic Gradient Descent (SGD) #
Stochastic Gradient Descent (SGD)
Concept #
Variant of gradient descent that updates parameters using random mini‑batches. Related terms: online learning, optimization. Explanation: Enables rapid updating of OHS models as new incident data streams in, without full retraining.
Supervised Learning #
Supervised Learning
Concept #
Model training using labeled input–output pairs. Related terms: classification, regression. Explanation: Uses historical injury records (labels) alongside exposure metrics (features) to teach the algorithm to predict future occurrences.
Support Vector Machine (SVM) #
Support Vector Machine (SVM)
Concept #
Algorithm that finds the hyperplane maximizing margin between classes. Related terms: kernel, linear classifier. Explanation: Separates high‑risk from low‑risk work periods based on multivariate sensor inputs, providing clear decision boundaries.
Temporal Drift #
Temporal Drift
Concept #
Shift in data patterns over time due to evolving processes or policies. Related terms: concept drift, seasonality. Explanation: After implementing a new lock‑out procedure, injury patterns change, requiring the OHS model to adapt to the new temporal distribution.
Transfer Learning #
Transfer Learning
Concept #
Reusing a model trained on one task for a related task. Related terms: fine‑tuning, domain adaptation. Explanation: Applies a model trained on manufacturing ergonomics to a construction site, reducing data collection burden while retaining predictive power.
Underfitting #
Underfitting
Concept #
Model is too simple to capture underlying structure, leading to poor performance on both training and test data. Related terms: high bias, low variance. Explanation: A linear OHS model may miss nonlinear interactions between temperature and PPE compliance, resulting in inaccurate risk estimates.
Unsupervised Learning #
Unsupervised Learning
Concept #
Learning patterns from data without explicit labels. Related terms: clustering, dimensionality reduction. Explanation: Discovers hidden groupings of tasks with similar hazard profiles, informing proactive safety inspections.
Validation Set #
Validation Set
Concept #
Subset of data used to tune model hyperparameters. Related terms: holdout, cross‑validation. Explanation: Provides an unbiased estimate of OHS model performance before final testing on unseen data.
Variance #
Variance
Concept #
Measure of model sensitivity to fluctuations in the training data. Related terms: model instability, overfitting. Explanation: High variance OHS models may produce wildly different risk scores when trained on different weeks of incident data.
Weighted Loss #
Weighted Loss
Concept #
Assigning different penalties to errors based on class importance. Related terms: cost‑sensitive learning, class weighting. Explanation: In injury prediction, false negatives may be penalized more heavily than false positives to prioritize safety.
Zero‑Inflated Model #
Zero‑Inflated Model
Concept #
Statistical model handling excess zeros in count data. Related terms: hurdle model, overdispersion. Explanation: Addresses the many days with zero recorded injuries while still modeling the occasional high‑count events in OHS datasets.