Turing Test & AI Evaluation Methods

Objectives: Turing Test & AI Evaluation Methods

Turing Test & AI Evaluation Methods β€” English / Kiswahili

Turing Test & AI Evaluation Methods

Overview β€” 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 β€” 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 β€” 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 mwingineβ€”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 β€” 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 β€” 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 β€” 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: ΞΊ = (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 β€” 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
ΞΊ (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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