AI help to prevent ludomania

AI in ludomania prevention is an early warning radar, not a stick or crystal ball. Its task is to notice the patterns of loss of control and offer a safe next step in time: pause, limit reduction, cool-off or support contact. Key principles: minimum data, maximum explainability, soft default actions.

1) Exactly what AI can do (and why it's needed)

Early risk detection. Reveals combinations of signals: ignoring reality checks, night marathons, "dogon" after the minus, an increase in the rate/pace of action.

Personalized nujas. Context hints - "pause 2 minutes," "today is exhausted X of Y," "turn on the cool-off 72 hours? ».

Auto-Protect "by default." Deferred increase in limits (enters after 24-168 hours), auto-logout by timer, soft blocking of deposits at "red" risk.

Solution support. Offers one concrete step, reducing impulsivity without shame or pressure.

2) Which risk signals are analyzed (without "sensitive" data)

Behavior: duration of sessions, skipping pauses, rate of clicks, reaction to reality checks.

Bets/money: "ladder" bets, microdeposits outside the schedule, frequent requests for raising limits.

Time of day and rhythm: night sessions after 23:00, streams without breaks> 45 min.

Event chains: loss → quick deposit ≤30 min → rate increase ("dogon" template).

Reactions to clues: accepted/closed/ignored (dynamics of trust and self-control).

💡 Not used: health, religion, politics, geolocation "to the house," microphone/camera. Minimization principle: only what is necessary for safety.

3) How AI translates signals into 'risk states'

1. Rules and thresholds as a base layer (transparent, deterministic).

2. Tabular characteristic models (logistic regression/gradient boosting) with probability calibration.

3. Sequential models (LSTM/Transformer) for orders of events in a session.

4. Anomaly detectors (IsolationForest/autoencoder) for "atypical" behavior.

5. Three states: green (normal), yellow (voltage/euphoria), red (high risk of impulse).

Interpretability is mandatory: top features/reasons are visible for each flag.

4) Ladder of interventions: softly → stiffer

Yellow: pause 60-120 seconds (STOP, breathing 4-4-6), limit counter "X of Y," a proposal to reduce the daily limit.

Repeat yellow/locally red: auto-logout by timer, "delay on increase" for limits, short poll "emotions 0-10."

Red/relapse: temporary blocking of deposits, 24-72 hour cool-off button, self-exclusion recommendation, channel to the care service (person-in-cycle).

Throttling: no more than N clues in M hours, so as not to annoy or "vestibulate" the player.

5) Tools for the player himself: how to benefit today

Turn on the reality check every 20 minutes + auto-logout.

Set a monthly limit (≤1 -2% of revenue) with a "deferred increase."

Stop rules: rate ≤1% BR, stop loss 2-3% BR, stop wine 5-10% BR.

At the tooltip about the risk - select one step: "Pause 2 minutes" or "Cool-off 72 hours."

Keep a diary of "6 lines" (plan, facts, result, emotions, violations, one correction).

Keep buddy close: 10-min report once a week (metrics below).

6) Metrics that show prevention works

NED (No Extra Deposits): weeks without unscheduled deposits (target - growing series).

SRL (Stop-loss Respect Level):% of sessions with observed stop loss (≥80%).

RCP (Reality-Check Prompt):% of pauses without ignoring (≥90%).

BRV (Bet Range Variability): rate spread (lower is better).

ERT (Emotion Reaction Time): seconds from the pulse to the application of the technique/click on "pause" (<30 seconds).

Uplift After Nudges: How the Probability of Dogons/Night Marathons Changes vs Control.

7) Ethics and player eligibility: Red lines

Transparency: say what data is analyzed and why; Select the level of prompts.

Consent: separate consent for chat/text analysis and for "soft interventions."

Minimization and storage: collect only what you need, store limited, delete on request.

Fairness: regular fairness audits (no bias by language/device/country).

Explainability and appeal: show "why there was a flag," give a path to a human operator.

The focus is on security, not retention - no "gamification" of nudges.

8) Operator Launch Plan (6-8 weeks)

Weeks 1-2. Goals and KPI, data map, list of features (20-40 features + 3-5 anomalies), basic thresholds.

Weeks 3-4. MVP model (logit/boosting), 2-3 UX hint templates, A/B tests for uplift (not clicks).

Weeks 5-6. Streaming scoring, throttling nudges, man-in-a-cycle, decision log.

Weeks 7-8. Fairness audit, privacy review, documentation for the regulator, feature extension, "delay on increase" by default.

9) Frequent mistakes - and how to avoid them

Black box without explanation. Solution: SHAP/feature top + text "why do you see this window."

Spam tips. Solution: throttling and prioritizing the Reds.

No follow-up after nudge. Solution: soft check-in after 24 hours ("set limit/cool-off? »).

Fuzzy goal. Solution: Capture security KPIs (NED, SRL, RCP), not "engagement."

Collecting unnecessary data. Solution: privacy-by-design and regular stripping.

10) Mini checklists

For the player (today, 10 minutes)

Included reality check + auto-logout.
Set the monthly limit (≤1 -2% of revenue) to "delay on increase."
Recorded stop rules: rate ≤1% BR; SL 2–3% BR; SW 5–10% BR.
Selected reaction to nudge: "Pause 2 min" as option # 1.
Began the diary "6 lines"; appointed a buddy report for Sunday.

For product/operator

Defined green/yellow/red zones and measures for each.
In sales - online scoring + throttling.
Hints - with explainability and safe default selection.
Safety KPI on the dashboard: NED, SRL, RCP, ERT, uplift.
Conducted a privacy/fairness audit; there is an appeals procedure.

11) FAQ (short)

Is AI "guessing" my emotions?

No, it isn't. He sees behavioral proxies (time, pace, response to pauses) and considers the likelihood of risk.

Will it limit my freedom?

Interventions are stepped and soft by default. Tough measures - only with high risk/repetitions and with explanations.

What if the flag is wrong?

There is a path to a human operator and the right to appeal. The model is additionally trained taking into account such cases.

AI helps to see the risk earlier and choose a safe step: pause, hold the limit, turn on the cool-off, contact people. It is effective when it remains modest and explainable: minimal data, transparent reasons, respectful UX, and metrics that measure security rather than engagement. Such AI prevention makes the game what it should be again: controlled leisure without devastating consequences.