How AI tracks players' emotional states

Introduction: why it is needed and where the boundaries

AI does not "guess emotions," but infers probable states by a number of indirect signs: text, voice characteristics, click rate, betting patterns, time of day, etc. The goal is early recognition of distress (frustration, loss of control, fatigue) and careful self-control tips. Borders - law, privacy, informed consent and the principle of "minimum data."

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1) What exactly AI sees: signal map (no correspondence and cameras by default)

A. Behavioral signals (interface telemetry)

sharp jumps in bets/deposits after losing (chasing);

• frequent clicks, "rage-clicks," canceled conclusions;
• increasing the speed of action, night binges (00: 00-05: 00);
• ignoring Reality Check, trying to raise limits;

frequent transitions between highly volatile games.

B. Text signals (NLP, user consent only)

tone of support chats: markers of irritation, despair, impulsivity;

vocabulary about "loss return," "last deposit," "debts."

C. Audio paralinguistics (with separate consent)

changes in timbre, tempo and pauses; voice trembling, "breakdown" of phrases;

here it is not the content of speech that is analyzed, but "how" it is said.

D. Visual signals (generally not applicable)

analysis of facial expressions - extremely controversial, gives a high risk of errors and intrusion; use only in research, with hard opt-in and local processing. Behavioral and textual traits are preferred for production.

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2) State taxonomy for product solutions

Instead of dozens of "emotions," use the operating scale:

• Calm/Normal - behavior is stable;
• Excitement/Euphoria - fast pace, increased bets after wins;
• Frustration - an increase in errors/clicks, re-deposits after a loss;
• Fatigue - long sessions, reduced response to prompts;
• Distress - linguistic markers of despair/hopelessness, critical patterns.

Each level has an intervention ladder (see § 6).

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3) Models and features: how it builds

Fici (examples):

• rolling units by deposits/rates/winnings;
• inter-click-time, burstiness, share of "night" events;
• cancellation of conclusions and time to re-deposit;
• NLP chat embeddings (tonality, toxicity, "passive requests for help");
• audio embeddings (pitch, jitter, speaking rate).

Model stack:

• tabular models (gradient boosting) for behavioral features;
• lightweight NLP classifier on chat embeddings;
• fusion/ensemble to combine modalities;
• anomaly detectors (Isolation Forest) as a "radar" and manual check trigger.
• Explainability: SHAP/feature importance on the case card.

Labels:

• not "emotion," but an operational event of harm: self-exclusion of 30 days, strong escalation to support, confirmed crisis. This reduces subjectivity.

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4) Ethics, legal requirements and privacy

Opt-in and informed consent. By default - only behavioral signals, without text/audio.

Data minimization. Aggregates instead of raw logs; pseudonymization.

Local/on-device processing for sensitive modalities.

DPIA/audits: regular assessment of data processing risks.

Prohibition of discrimination: do not use gender, ethnicity, health, etc.; monitor fairness across cohorts.

Right to explanation and refusal. The user sees which signals have been triggered and can disable advanced analysis.

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5) Accuracy and limitations: being honest about the risks

Emotions are dynamic and contextual: the same pattern in different people means different.

Computer "face emotion recognition" - unreliable in production; priority - behavioral and textual data.

Models give probability rather than diagnosis. Solutions - only as a basis for soft tips and assistance, and not for sanctions for the sake of sanctions.

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6) Action Framework: How to act on levels

Principles: transparency, respect for choice, logging of consents and reasons.

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7) Product and process integration

Real-time inference in the event stream; "cold start" is closed by the rules.

CS/RG panel: session history, explanation of triggers, checklist of actions.

CRM orchestration: stop lists of promos for L3-L5, replacing reactivations with educational content.

Event sourcing: immutable logs of interventions and limit changes for audits.

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8) MLOps and quality

Online metrics: PR-AUC, calibration (Brier), latency, drift feature.

Business KPIs:

• an increase in the share of players who set limits;
• reduced lead cancellations;
• an increase in the proportion of early calls for help;
• reducing "night binges."
• Processes: canary releases, auto-retraining at drift/once every 4-8 weeks, A/B test of interventions with guardrails.

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9) Localization and cultural context

Tonality and linguistic markers vary by country and language. Local dictionaries and offset checking are needed. For audio - calibration for accents and timbres. For behavioral metrics - taking into account local habits (work shifts, time zones, sports seasons).

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10) Implementation Roadmap (8-10 weeks)

Weeks 1-2: data inventory, DPIA, choice of modalities (default is behavior).

Weeks 3-4: prototype feature and basic model (GBM + rules), offline assessment, explanation design.

Weeks 5-6: real-time integration, CS panel, CRM rules, text module (opt-in).

Weeks 7-8: pilot on 10-20% of traffic, A/B interventions, setting thresholds.

Weeks 9-10: rollout, drift monitoring and fairness, public report on RG metrics.

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11) Launch checklists

Law and privacy:

• Opt-in/opt-out, transparency policy
• DPIA, minimization, local processing of sensitive data
• RBAC and access logs

Data/Models:

• Behavioral features and time windows
• Explainability in the case card
• Fairness monitoring by cohort

Operations/Product:

• CS/RG panel + action playbooks
• CRM Promotional Limiters for L3-L5
• Event sourcing solutions

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12) Frequent errors

Hyperinvasiveness: trying to "read emotions across the face" without having to → legal/ethical risks.

Black-box without explanation: it is impossible to protect decisions in front of the regulator and the player.

The same thresholds for all countries/languages: distortions and false positives.

Detection without action: there is speed, there are no playbooks → loss of benefit and trust.

Collecting "superfluous" data: the risk of leaks and fines - keep only what you need for RG.

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AI helps not to "stigmatize," but to maintain: it notices patterns indicating fatigue, frustration or distress, and in time offers soft self-control tools - limits, pauses, help. Success is possible only with ethics, transparency and privacy, with an emphasis on behavioral signals and understandable actions. Then technology really reduces harm and builds player trust in the operator responsible.