Data ethics and transparency

1) Why do you need it

Data ethics is a set of principles and practices to ensure that the collection, storage and use of data respects the individual, minimizes harm and increases trust. In iGaming, this is especially important due to the sensitivity of PII/financial data, the risks of addictive behavior, strict regulation and a high pace of experiments (personalization, bonuses, anti-fraud, RG scoring).

• Protecting players and brand reputation.
• Prevention of manipulation and discrimination in ML/marketing.
• Increase conversion through transparency and trust.
• Reducing regulatory and legal risks.

2) Basic principles

1. Benefit: Use data for real player value (honest recommendations, safe play).
2. Non-malefiction: do not exploit vulnerabilities (for example, aggressive offers "over" signs of risk).
3. Justice: non-discrimination by gender, age, ethnicity, disability, etc.; equal access to responsible tools and support.
4. Autonomy: informed consent, understandable explanations, easy refusals.
5. Accountability: designated owners, audit, decision log.

3) Pillars of transparency

Understandable explanations: in simple language, without legal "fluff."

Validity of decisions: why did you show the offer/limit/segment?
Verifiability: consent logs, versioning of campaigns and models.
Consistency: same wording in product, email and policy.
Accessibility: adaptation for different languages ​ ​ and accessibility (a11y).

4) Consents, minimization and processing objectives

Goal linkage: Collect only what you need for a specific goal (KYC, payments, RG, analytics).
Granularity of consents: separately for personalization, marketing, A/B tests, third-party.
Free failure: no degradation of basic functionality.
Lifecycle: retention periods, automatic expiration of consents, DSAR procedures.
Pseudonymization and anonymization: default in analytics and research.

5) Ethical marketing and personalization

Forbidden practices: dark patterns (hidden failures, masking chances), pressure on vulnerable states (late night, "series of defeats"), false deficit.
Fair offers: indicate the conditions for wagering, RTP/volatility, limits.
RG restrictions: personalization should not bypass self-exclusion/limits; for "high-risk" - soft scripts and pauses.
Transparent recommendations: explain why "you may be suitable" (genre, provider, RTP-range), avoid "anchoring" on aggressive slots.

6) Fairness and non-discrimination in ML

6. 1 Displacement sources

Class imbalance: rare events (chargeback, self-exclusion) retrain the model.
Proxy variables: geo/device/time can indirectly encode prohibited attributes.
Label drift: the rules of moderation or anti-fraud have changed - the labels are outdated.

6. 2 Metrics and procedures

Fairness metrics (example): TPR/FPR equality between groups, disparate impact, calibration.
A/B ethics: preliminary risk assessment + strata by vulnerable groups; early stop rules.
Human control: high-risk solutions (freezing, limits) - only with human-in-the-loop.

6. 3 Technical practices

Data statements: dataset origin, group coverage, known limitations.
Bias control in the pipeline: automatic tests on proxy attributes, regular fairness reports.
Explainability module: local explanations for support (SHAP/feature attributes), allowed features in the case book.

7) Transparency for players

Odds and RTP: clear RTP ranges by product, links to RNG/provider rules.
Limits and RG mechanics: explanation of the trigger algorithm (at a high level), understandable consequences.
Account history: bets, sessions, deposits/conclusions, bonuses - in convenient exports.
Communication channels: easy access to support, ombudsman/regulator (where applicable).

8) Transparency to regulators and partners

Audit trails: changes in anti-fraud models/campaigns/rules, data and code versions.
Vendor clauses: requirements for providers (anti-fraud, KYC, risk attribution, log storage).
Reporting: reports on RG-indicators, complaints, reaction time, false positives/negatives.

9) Roles and responsibilities

Ethics Board/Council: CDO/DPO/Legal/CRM/RG/ML - approves policies, disassembles complex cases.
DPO/Privacy Lead: consents, DPIA, incidents and notifications.
Data & ML Owners/Stewards: quality, dataset documentation, fairness reports.
Marketing & CRM Leads: a "black list" of tactics, a review of creatives, the frequency of impressions.
RG Lead: vulnerability criteria, intervention scenarios, operator training.
Security: encryption, access, logging, secrets.

10) Ethics/Transparency Metrics and KPIs

Coverage:% of key datasets with data statement and owner.
Explainability rate: the proportion of high-impact solutions with available explanations.
Fairness score: TPR/FPR equality between groups within tolerances.
Consent health: proportion of valid/relevant consents; average DSAR processing time.
RG outcomes: response time to triggers, proportion of correct interventions, reduction of harmful patterns.
Complaint MTTR: Average time to close complaints.
Marketing ethics: Proportion of campaigns that pass a pre-launch ethics check.

11) Templates (ready to use)

11. 1 Data Statement

• Coverage and representativeness: (countries/languages/devices/channels)
• Sensitive attributes: (collected ?/masking)

11. 2 Model Card (sketch)

Task and business context: (e.g. RG scoring)

Data and features: (no PII or masked)

Quality metrics: AUC/PR, calibration.
Fairness metrics: groups, criteria, results.
Explainability: available attributions/limits of use of explanations.
Risks/mitigations: manual check, thresholds, revision frequency.
Versions: model/data/code/environment, release date.

11. 3 Ethical Marketing Policy (excerpt)

Prohibited: dark patterns, hidden conditions, high-risk targeting without RG restrictions, "resuscitation" after self-exclusion.
Mandatory: clear bonus conditions, visible RTP range, "refuse" button in 1 click, frequency limits of impressions.
Process: pre-launch check, creative audit, post-campaign report with complaints and RG metrics.

11. 4 DPIA/DEIA - checklist

• Statement of purpose and expected benefit
• Data and Consent Map
• Analysis of vulnerable groups and risks
• Mitigation plans (limits, pauses, human-in-the-loop)
• Fairness Metrics and Drift Monitoring
• Communication plan (what we explain to the player)
• Legal Evaluation and Recording of Ethics Board Decisions

12) Processes and control points

Pre-design ethical review: before data collection/re-use.
Pre-launch review: before launching the campaign/model - verification of consents, fairness, RG restrictions.
Runtime monitoring: alerts to drift, a surge in complaints, an abnormal frequency of impressions.
Post-mortem ethics: for incidents (for example, an aggressive offer for self-exclude-similar profiles) - with a public internal report.

13) Playbook Incident (Brief)

1. Detect: signal from monitoring, complaint, regulatory request.
2. Stabilize: stop rule/campaign, model/segment freeze.
3. Assess impact: who is affected, how long, what data/decisions.
4. Compensation and communication: players, partners, if necessary, the regulator.
5. Correct: corrections of features/thresholds/creatives, personnel training.
6. Learn lessons: update policy, tests, pre-launch checklist.

14) Implementation Roadmap

0-30 days (MVP)

Approve the Data Code of Ethics and the minimum consent policy.
Assign Ethics Board, dataset owners and high impact models.
Implement data statements for the top 10 sets, model cards for 3 key models.
Add a fairness check and release lock in violation of thresholds to the CI.

30-90 days

Standardize consent and rejection texts, restart banners/settings.
Connect runtime-fairness monitoring + RG alerts/complaints.
Audit creatives and frequency limits; introduce a "black list" of tactics.

3-6 months

Cover data statements ≥70% of active sets and model cards for all high-risk models.
Regular ethical reports: fairness, DSAR deadlines, complaints, RG outcomes.
Team training (marketing, CRM, support, DS/ML, product).

15) Anti-patterns

"First we launch, then we think about ethics."

Reliance on "hidden" proxy attributes in targeting.
Lack of human-in-the-loop with high-risk solutions.
Opaque bonus terms and "bounce" of consents.
Ignoring RG complaints and signals in post-analysis.

16) Connection with neighboring practices

Data Governance, Data Origin and Path, Data Quality, DSAR/Privacy, Legal Hold, Model Monitoring, Drift and Data Update - Ethics relies on them and sets the "framework of the game."

Result

Data ethics and transparency are a daily discipline, not a one-off policy. Clear principles, verifiable processes and understandable explanations make analytics and ML reliable, marketing honest, and brand trusted. In iGaming, the one who knows how to personalize responsibly wins.