An overview of the different research methods used in artificial intelligence, including experimental design, data collection, and analysis techniques.
Introduction to AI Research Methods
Designed as a quick‑reference guide for students and newcomers to the field.
| Goal | What It Enables |
|---|---|
| Reproducibility | Others can verify and build on your work. |
| Validity | Demonstrate that results truly reflect the phenomenon being studied. |
| Efficiency | Choose the right tools and avoid duplicated effort. |
| Ethical Responsibility | Anticipate bias, privacy, and societal impact. |
Problem Definition → Literature Review → Hypothesis/Formulation → Data Acquisition → Model Development → Evaluation → Analysis & Interpretation → Publication → Reproducibility Checks
Each stage has its own methodological toolbox (see sections below).
| Paradigm | Typical Questions | Common Techniques |
|---|---|---|
| Theoretical / Algorithmic | Can we prove convergence? What is the computational complexity? | Formal proofs, complexity analysis, asymptotic bounds. |
| Empirical / Experimental | Does this architecture outperform baselines on benchmark X? | Controlled experiments, ablation studies, statistical testing. |
| Applied / Engineering | How does the system behave in a real‑world deployment? | Field trials, A/B testing, user studies. |
| Interdisciplinary / Societal | What are the fairness implications of model Y? | Survey methods, qualitative analysis, impact assessment. |
Define Variables
Select a Baseline
Control Confounders
Determine Sample Size
Specify Evaluation Metrics
Plan Reproducibility
| Aspect | Best Practices |
|---|---|
| Acquisition | Open datasets (e.g., ImageNet, GLUE), web scraping with ethical compliance, synthetic data generation. |
| Cleaning & Pre‑processing | Missing‑value handling, outlier detection, normalization, tokenization, data augmentation. |
| Splitting | Train / validation / test (commonly 70/15/15) or cross‑validation for small datasets. |
| Label Quality | Crowd‑sourcing with gold‑standard checks, inter‑annotator agreement (Cohen’s κ). |
| Bias Auditing | Demographic parity checks, subgroup performance analysis, counterfactual testing. |
| Strategy | When to Use | Key Steps |
|---|---|---|
| Baseline‑First | Early‑stage projects | Implement a simple model (e.g., logistic regression) to set a performance floor. |
| Incremental Complexity | Controlled ablations | Add one component at |