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How AI determines addiction risk by behavior
AI does not make diagnoses or "read minds." It analyzes digital traces of behavior and assesses the likelihood of losing control. Below is a practical scheme: from a raw event to the decision "to offer a pause/lower the limit/escalate to a person."
1) What behavioral cues indicate risk
Sessions and time
Length of ↑ sessions, "marathons" without pauses> 45 minutes.
Night games (after 23:00) more often 3 times/week.
Skipping two consecutive reality checks.
Bets and money
"Ladder" bets (sequential upsizes after pluses).
Chasing pattern: loss → deposit ≤30 min → increase in bet size.
Unscheduled microdeposits (many small replenishments in a short period).
Frequent requests for raising limits, lifting limits.
Pace and discipline
Increase clicks/min and reduce decision time.
Ignore stop loss, "transfer" rules during the session.
Reactions to prompts
Closing nujas without reading, refusal of pauses, low share of accepted recommendations.
2) How events turn into features (feature engineering)
Sliding windows: 15 min/2 hours/24 hours/7 days (deposit amounts, duration, ignore pauses).
Trends/slopes: week to week by average rate, time, frequency of sessions.
Sequences: "L L L → Deposit → Bet↑"; «Win → Bet↑×n».
Variability: BRV - rate size spread; instability of the click rate.
Reactions: RCP - share of reality checks without ignoring; ERT - seconds from pulse to pause.
Payment anomalies: new wallets/cards, split amounts.
3) Model stack: from rules to sequences
1. Rules/thresholds (baseline): simple if-else (transparent, but rough).
2. Scoring of tabular features: logistic regression/gradient boosting (+ probability calibration).
3. Sequential models: LSTM/Transformer for the order of events in a session.
4. Anomaly detection: Isolation Forest/autoencoders for atypical patterns.
5. Hybrid: "instant stop" rule + ML scoring + person-in-cycle.
4) Risk states and thresholds
Green: normal behavior, limits are met.
Yellow: moderate risk probability (eg, P≥0. 3): 60-120sec pause, limit reminder, "lower limit" option.
Red: high probability (P≥0. 6) or the "hard" rules worked (dogon, ignore 2 checks, night marathon): auto-logout, cool-off 24-72 hours, route to support.
Thresholds are adjusted by A/B tests for accuracy/harm from errors and regulator requirements.
5) Examples of features → interpretation (non-clinical)
Deposit≤30 min after batch L + Bet↑ ≥X% → high risk of dogon.
RCP <40% + sessions after 23:00 → fatigue/attention tunnel.
BRV↑ + consecutive upsizes on wins → euphoria/loss of frames.
ERT> 60 sec at nujah → low self-regulation "in the moment."
6) What happens in real time (pipeline)
1. Events (rate, deposit, check) →
2. Online features (15 min window) + offline aggregates (day/week) →
3. Score (probability of risk + top cause/SHAP) →
4. Policy measures (ladder of interventions) →
5. UX hint to player + logging results →
6. Monitoring/drift (model quality, complaints, uplift).
7) Intervention ladder (example)
Softly: "It looks like you're playing longer than usual. Pause 2 minutes?" "Lower the limit for today" "Learn about cool-off 72 hours."
Average: autologist-out by timer; "deferred increase" of any limits (entry after 24-168 hours).
Hard: temporary blocking of deposits; recommending self-exclusion; escalation to a care service.
Throttling: no more than N clues in M hours, priority "red."
8) Metrics proving benefit
Precision/Recall by risk levels (do not spam).
Uplift: reduced frequency of dogon/night marathons in the "nuj group" vs control.
Behavior: ↑ of acceptance of pauses; ↓ of unscheduled deposits; ↓ of limit cancellations.
Player-centric: NPS prompts; proportion of obsession complaints.
Compliance: SLA for high-risk cases, traceability of solutions.
9) Ethics and privacy: red lines
Data minimization: only behavioral and transactional signals, without sensitive categories (health, religion, politics, etc.).
Transparency: "why do you see this warning" + hint level option.
Explainability: for each flag - top signs and text in human language.
Fairness: Regular fairness audits (no language/channel/device bias).
Appeal: path to a human operator, revision of controversial cases.
Storage: limited time, removal on request.
10) How to build signs from scratch: a short product checklist
- Windows 15 min/2 h/7 days for rates, time, pauses, deposits.
- Indices: Chase, BRV, RCP, ERT, proportion of night sessions, "marathons"> 45 min.
- Directory of "hard" rules (instant stop triggers).
- Throttling protocol and UX hint templates.
- Quality panel: precision/recall, uplift, complaints, data/model drift.
- Privacy/fairness audit + decision log.
11) Frequent bugs - and quick fixes
Bet on a "black box." → Add explainable signs and SHAP; keep guard rules.
Spam nujami. → Enter frequency limits and context (do not disturb during pause).
Focus on clicks, not benefits. → Measure uplift on behavioral KPIs.
No follow-up. → After 24 hours soft "set limit/cool-off? ».
Collecting unnecessary data. → Revise the diagram: leave only what you need for security.
12) What the player should do if "AI flagged risk"
Perceive the hint as help, not reproach.
Choose one step: pause 2 minutes/lower the limit/cool-off 72 hours.
Note in the diary "6 lines" emotions 0-10 before/after and one correction of the rule.
If the flag feels erroneous, exercise the right to appeal.
AI determines the risk of addiction by behavior and its dynamics: combinations of protracted sessions, dogons, ignoring pauses, night activity, "ladders" of bets and reactions to prompts. Strong design is a hybrid of rules and models, transparent thresholds, soft-default measures, benefit metrics, and strict data ethics. In this format, AI really helps the player stop on time, and the platform helps maintain a safe, stable game format.