How AI tracks players' emotional patterns

AI does not "read minds." It derives emotional states from behavioral traits that indirectly reflect stress, arousal, or impulsivity. The goal is to see early markers of loss of control and offer gentle intervention in time. Below is how it works from data to action and where the boundaries of ethics pass.

1) What emotions matter to risk at all

Stress/anxiety: more often launch "dogon" and night "finishing."

Euphoria: provokes an increase in the rate and ignoring stop rules ("on the wave").

Annoyance/anger: Accelerates the rate of clicks and increases the variability of bets.

Fatigue/boredom: lengthen sessions, reduce sensitivity to "reality checks."

AI does not assign clinical labels; it recognizes behavioral patterns associated with these conditions.

2) From what emotional signals are "collected" (without PII)

Behavioral series

Session duration and structure, pauses, night activity.

Pace of action: clicks/min, speed of decision-making, response to "reality check."

Rate dynamics: micro-increases, ladder, range expansion.

Dogon-loop events: loss → quick deposit → rate increase ≤30 min.

Financial markers

The frequency of microdeposits "off schedule," lifting limits, trying to raise them.

Patterns of distribution of winnings (fixed/reinvested).

Content and language (optional, with consent)

Tone of calls in support/chat (NLP at the level of intentions: "anxiety," "anger," "self-control").

Reaction labels to prompts: "accepted/opened/closed."

Context signals

Time of day, frequency of nights with ≤6,5 hours of sleep (by proxy activity), "marathons" without breaks.

💡 Minimization principle: we take only what is necessary to protect the player and lends itself to explanation.

3) How "emotional states" are born from signals

1. Physical engineering: sliding windows 15 min/2 hours/7 days; trends (rate slope, rate growth), ratio "response to check/ignore."

2. Basic rules: "if you ignore two checks in a row + a ≥X% rate increase → high stress." Transparent but rough.

3. Risk models:

Tabular (logistic regression/gradient boosting) with probability calibration.
Sequential (LSTM/Transformer) for the order of events in a session.
Anomaly detectors (IsolationForest/autoencoder) for "atypical behavior."
4. Foliation into "states": green (normal), yellow (voltage/euphoria), red (high risk of impulse).

4) Examples of "digital emotions" (proxy → probable interpretation)

Frequent quick clicks + rate increase after minus series → annoyance/dogon.

Ignore 2-3 reality checks + night time → fatigue/attention tunnel.

Series of wins + consecutive upsizes → euphoria/increased risk of losing rules.

Microdeposits for 10-15 minutes within an hour → anxiety/inability to "get out."

Important: always keep probability and context; single sign ≠ output.

5) What the platform does when it sees a high risk (ladder measures)

Low-friction

Pause 60-120 seconds with breathing equipment and the "continue later" button.

Stop loss/time limit reminder with "today's X of Y" counter.

Proposal to include cool-off 24-72 hours or reduce the limit.

Intermediate level

Autologist-out at the end of the timer with a "cold" countdown window.

"Deferred increase" of limits: enters in 24-168 hours.

Stringent (red/repeat)

Temporary blocking of deposits, recommendation of self-exclusion.

Escalation to a care service (person-in-a-loop, empathic script).

6) How to check that AI really helps (not interferes)

Precision/Recall by risk levels: do not overheat with nujahs.

Uplift: How the probability of dogon/night marathons changes after prompting vs control.

Behavioral KPIs: ↑ acceptance of pauses, ↓ unscheduled deposits, ↓ limit cancellations.

Player-centric KPI: NPS clues, proportion of obsession complaints.

Justice: is there no bias by language, country, device.

Drift monitoring: stability of distribution of features and quality of models.

7) Ethical and legal principles (red lines)

Transparency: we tell the player what data is analyzed and why; give the level options of hints.

Minimization: no "sensitive" data (health, religion, politics).

Explainability: for each flag - a human explanation ("what caused the warning").

Consent: explicit consent to text/chat analysis and to "soft interventions."

No whip: Focus on safety, not "holding" in the game.

Right to appeal: A person-in-a-loop can remove the wrong flag.

8) Feature design: what usually "shoots"

RCP: proportion of reality checks without ignoring (low → attention tunnel).

ERT: seconds from pulse to pause/breath (short ERT → better self-control).

BRV: rate spread (→ euphoria/loss of frames is growing).

Chase-index: deposit ≤30 min after cons + beta growth ≥X%.

Sleep proxy: proportion of sessions after 23:00 and streams without pauses> 45 min.

9) UX correct clue: tone of care, one step

💡 "Looks like the pace has picked up and pauses are skipped. Let's take a 2-minute breather?
Pause now Lower daily limit Learn about cool-off 72 h"
Keys: respect, specificity, safe choice "by default."

10) How to connect AI and personal player awareness

Reality check every 20 minutes → mandatory 60-second pause with breathing 4-4-6.

If the prompt is "stress/euphoria," use STOP or urge surfing 90 sec.

In the diary "6 lines": mark the emotion 0-10 "before/after" and one adjustment of the rule.

11) Frequent implementation errors - and quick fixes

Black box without explanation. → Add SHAP/feature-top, texts "why do you see this window."

Too many nujas. → Trottling: no more than N in M hours, prioritization of "red."

No follow-up. → After 24 hours soft "How are you? Set up reminders/cool-offs? ».

Ignore privacy. → Reconsider data collection, delete unnecessary data, reduce retention periods.

12) Operator/Product Checklist

Defined "green/yellow/red" states and measures for each level.
20-40 explainable features + 3-5 anomalies; windows 15 min/2 h/7 days.
Online scoring with tip throttling.
Man-in-a-loop and empathic communication script.
A/B nuja tests on uplift, not just clicks.
Privacy/fairness audit; journal of decisions and right of appeal.
Options for the player: level of prompts, cool-off, self-exclusion.

AI can notice emotional patterns early - not in words, but in behavior. Its strength is not in "guessing," but in supporting a safe choice: to offer a pause in time, to remind about the limit, to close the "short vicious circle" of dogons. With transparent rules, minimal data and respectful UX, technology becomes a real protection for the player - and leaves excitement where it belongs: within the limits of responsible leisure.