Turing Test & AI Evaluation Methods

Objectives: Turing Test & AI Evaluation Methods

Turing Test & AI Evaluation Methods — English / Kiswahili

Turing Test & AI Evaluation Methods

Overview — English

This document explains the Turing Test and a broad set of AI evaluation methods: behavioural tests, quantitative metrics, human evaluation, robustness checks, fairness, interpretability, and example calculations. Each concept includes an explanation, a real-world example, formulas and a diagram.

Muhtasari — Kiswahili

Hati hii inaelezea Mtihani wa Turing na mbinu mbalimbali za kutathmini AI: mitihani ya matendo, vipimo vya kimuundo, tathmini za wanadamu, upinzani, haki (fairness), ufasiri (interpretability) na mifano halisi. Kila kipengele kina maelezo, mfano, fomula na mchoro.

Turing Test — Concept (EN) / Mtihani wa Turing (SW)

English explanation

Invented by Alan Turing (1950). A human evaluator converses with an unseen interlocutor: either a human or a machine. If the evaluator cannot reliably tell which is which, the machine is said to have passed the Turing Test.

Real-world example

Chat-based customer service: If customers conversing with a chatbot cannot tell it apart from a human operator after a 10-minute mixed conversation, the chatbot approaches passing a practical Turing Test.

Ufafanuzi kwa Kiswahili

Imetengenezwa na Alan Turing (1950). Mtathmini (mzee wa binadamu) huweza kuzungumza bila kuona na upande mwingine—ni binadamu au mashine. Ikiwa mtathmini hawezi kutofautisha kwa uhakika, mashine inachukuliwa kupita Mtihani wa Turing.

Mfano wa mazingira

Huduma kwa wateja mtandaoni: Ikiwa wateja hawawezi kutofautisha kati ya chatbot na afisa wa binadamu baada ya mazungumzo ya dakika 10, chatbot inakaribia kupita Mtihani wa Turing kwa vitendo.

Diagram — Turing Test interaction (SVG)
Evaluator (Human) Interlocutor A (Human) Interlocutor B (Machine) Blind text channels (no face/voice).

Limitations / Mapungufu: The Turing Test measures imitation, not understanding. It is sensitive to conversational domain, length of interaction, and human evaluators' skill.

AI Evaluation Methods — Key categories

Behavioural Tests (Black-box)

Observe inputs and outputs only. Examples: Turing Test, CAPTCHA robustness, passing question-answer benchmarks.

Analytic Metrics (White-box / Output-based)

Numeric measures such as accuracy, precision, recall, F1-score, ROC AUC, BLEU, ROUGE, perplexity, calibration error.

Confusion matrix — formulae & symbols
Confusion matrix (Binary classification)
TP = True Positives
TN = True Negatives
FP = False Positives
FN = False Negatives

Accuracy = (TP + TN) / (TP + TN + FP + FN)
Precision = TP / (TP + FP)
Recall (Sensitivity) = TP / (TP + FN)
F1 = 2 * (Precision * Recall) / (Precision + Recall)

Symbol meanings:

  • TP Items correctly predicted as positive.
  • FP Items incorrectly predicted as positive (Type I error).
  • FN Items incorrectly predicted as negative (Type II error).
Mfano halisi (Example)

Suppose a medical test for disease on 100 people: TP=30, TN=60, FP=5, FN=5.

Accuracy = (30+60)/100 = 0.90
Precision = 30/(30+5) = 0.857
Recall = 30/(30+5) = 0.857
F1 = 2*(0.857*0.857)/(0.857+0.857) = 0.857
Interactive confusion matrix (JS + canvas)

Language models / Generation metrics

Perplexity

Perplexity measures how well a probability model predicts a sample. Lower is better for language models.

Perplexity(P) = 2^{ - (1/N) * sum_{i=1..N} log_2 P(w_i) }

Symbols: P(w_i) probability of token i; N number of tokens.

BLEU / ROUGE (for translation / summarization)

BLEU compares n-gram overlap between candidate and reference. ROUGE is recall-oriented for summarization.

Calibration & Reliability

Calibration checks whether predicted probabilities reflect true frequencies. Example: among examples predicted with 0.8 probability of positive, about 80% should actually be positive.

ExpectedCalibrationError = sum_k |acc(B_k) - conf(B_k)| * (|B_k|/N)
// where B_k are probability bins

Symbols: acc(B_k) empirical accuracy in bin k; conf(B_k) average predicted confidence in bin k.

Human Evaluation & Adversarial Testing

Automated metrics don't capture user satisfaction, safety, or subtle biases. Human evaluation remains essential. Example procedures:

  • Pairwise preference tests: Present two outputs (A, B) to annotators and ask which is better for a task.
  • Error analysis: sample errors, categorize by type (hallucination, bias, toxicity).
  • Adversarial tests: craft inputs that probe model failure modes, e.g., ambiguous instructions or out-of-distribution data.

Real-world case: For a summarization system, show 100 summaries to human raters and compute average rating (1..5). Use inter-rater agreement (Cohen's kappa) to measure consistency.

Cohen's kappa: κ = (p_o - p_e) / (1 - p_e)
where p_o = observed agreement, p_e = expected agreement by chance.

Robustness, Fairness & Interpretability

Robustness

Check performance under noise, distribution shift, or adversarial perturbations. Example: add typos or synonyms for NLP models, or small pixel noise for vision models and measure degradation.

Fairness

Metrics: demographic parity, equalized odds, predictive parity. Example: ensure false positive rates are similar across demographic groups.

Interpretability

Tools: feature importance, SHAP values, LIME, attention visualization. Interpretability helps debug and explain decisions.

Data Model (Black-box) Output & Eval

Appendix — Symbols & Common Formulas

SymbolMeaningContext / Formula
TP, FP, TN, FNConfusion matrix countsUsed in accuracy, precision, recall
P(w_i)Probability assigned to token w_iPerplexity formula
BLEU_nn-gram precision with brevity penaltyMachine translation evaluation
κ (kappa)Inter-rater agreementCohen's kappa formula
Export & Usage

The HTML includes inline SVG diagrams and JavaScript examples to visualize confusion matrices and simple pipelines. You can save this file and open it in a browser. All diagrams are vector so they scale for print.

Reference Book: N/A

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