Getting Started

Start evaluating your AI systems in just a few minutes

1. Create Your Account

Visit platform.raidot.ai to create your free account. You can sign up using your email address or through Google/LinkedIn authentication.

2. Choose Your Plan

Select the plan that best fits your needs:

3. Your First Risk Evaluation

Follow these steps to complete your first AI risk assessment:

1. Select Your Sector: Choose from healthcare, finance, automotive, education, or other available sectors
2. System Classification: Define your AI system type (e.g., diagnostic tool, recommendation engine, autonomous system)
3. Complete Assessment: Answer 30-50 sector-specific questions about your AI system
4. Review Report: Receive detailed risk analysis with actionable recommendations
5. Download Certificate: Generate compliance documentation (Premium plans)

4. Explore Additional Features

• Training Modules: Access AI ethics and risk management courses
• Fairness Analysis: Test your AI systems for bias and discrimination (Premium)
• Expert Consultancy: Book one-on-one sessions with AI specialists (Premium)
• Compliance Tracking: Monitor your progress towards regulatory compliance

Platform Guide

Comprehensive guide to using the RaiDOT platform

Dashboard Overview

Your dashboard provides a centralized view of all your AI risk assessments, training progress, and compliance status.

Key Dashboard Elements

• Risk Summary: Overview of all completed evaluations and risk levels
• Compliance Status: Track your progress towards regulatory requirements
• Learning Progress: Monitor your training course completion and certifications
• Recent Activity: View your latest assessments and consultancy sessions

Risk Evaluation Process

The risk evaluation process is designed to be thorough yet efficient:

Step 1: Sector Selection

Choose the most relevant sector for your AI system:

• Healthcare: Medical devices, diagnostic tools, treatment recommendations
• Finance: Credit scoring, fraud detection, algorithmic trading
• Automotive: Autonomous vehicles, driver assistance systems
• Education: Student assessment, adaptive learning, admissions
• Employment: Recruitment, performance evaluation, workforce analytics
• And more... Additional sectors available based on your needs

Step 2: System Classification

Define key characteristics of your AI system:

• AI system type (e.g., machine learning model, rule-based system, hybrid)
• Decision automation level (fully automated, human-in-the-loop, advisory)
• Data types processed (personal data, sensitive data, public data)
• User interaction model (direct user interaction, background processing)

Step 3: Assessment Questionnaire

Answer detailed questions across multiple risk dimensions:

• Safety & Security: System reliability, cybersecurity measures
• Fairness & Bias: Discriminatory outcomes, protected characteristics
• Transparency: Explainability, user understanding, documentation
• Privacy: Data protection, user consent, data minimization
• Accountability: Human oversight, responsibility, governance

Step 4: Report Generation

Receive a comprehensive report including:

• Overall risk score and classification
• Detailed analysis by risk dimension
• Specific recommendations for improvement
• Regulatory compliance mapping
• Action plan with prioritized next steps

Report Management

Manage and track your evaluation reports:

• View Reports: Access detailed analysis and recommendations
• Download PDFs: Export reports for sharing and documentation
• Track Changes: Compare assessments over time
• Share Results: Collaborate with team members (Premium plans)

Training & Certification

Access professional development resources:

Available Courses

• AI Ethics Fundamentals: Core principles and frameworks
• Risk Assessment Methodologies: Practical evaluation techniques
• Bias Detection & Mitigation: Fairness in AI systems
• Regulatory Compliance: EU AI Act, GDPR, sector-specific regulations

Certification Programs

• AI Risk Assessment Specialist: Comprehensive risk evaluation certification
• AI Ethics Practitioner: Ethics and responsible AI development
• Bias Detection Expert: Fairness analysis and mitigation specialist

Risk Evaluation

Comprehensive AI risk assessment methodology and tools

Overview

RaiDOT's risk evaluation framework assesses AI systems across multiple dimensions to provide a comprehensive understanding of potential risks and their implications.

Risk Dimensions

Risk Levels

RaiDOT uses a standardized risk classification system:

• No Risk (0-2): Minimal concerns, standard monitoring sufficient
• Low Risk (2-4): Minor issues identified, regular review recommended
• Limited Risk (4-6): Moderate concerns, improvement plan needed
• High Risk (6-10): Significant issues, immediate action required

Sector-Specific Evaluations

Each sector has tailored evaluation criteria:

Healthcare AI Systems

• Patient safety considerations
• Medical device regulations (FDA, CE marking)
• Clinical validation requirements
• HIPAA and patient privacy
• Diagnostic accuracy and reliability

Financial Services

• Fair lending practices
• Credit scoring transparency
• Anti-discrimination measures
• Algorithmic accountability
• Regulatory compliance (GDPR, local finance laws)

Autonomous Systems

• Safety-critical system requirements
• Human oversight mechanisms
• Failure mode analysis
• Environmental impact assessment
• Liability and insurance considerations

Evaluation Results

Your risk evaluation generates a comprehensive report including:

• Executive summary with overall risk score
• Detailed analysis by risk dimension
• Sector-specific compliance mapping
• Prioritized improvement recommendations
• Implementation roadmap with timelines

Fairness Analysis

Advanced bias detection and mitigation tools for ethical AI

Overview

Fairness analysis helps identify and mitigate bias in AI systems to ensure equitable outcomes across different demographic groups and use cases.

Fairness Metrics

RaiDOT evaluates multiple fairness dimensions:

Analysis Process

1. Data Review: Analyze training data for representation and historical bias
2. Model Testing: Evaluate performance across different demographic groups
3. Bias Detection: Identify discriminatory patterns and disparate impact
4. Mitigation Recommendations: Provide specific technical and procedural solutions

Common Bias Types

• Historical Bias: Past discriminatory patterns reflected in training data
• Representation Bias: Underrepresentation of certain groups in data
• Measurement Bias: Systematic errors in data collection or labeling
• Evaluation Bias: Unfair performance metrics or benchmarks
• Deployment Bias: Different performance in real-world vs. test conditions

Mitigation Strategies

RaiDOT provides actionable recommendations for bias reduction:

• Data Augmentation: Improve representation in training datasets
• Algorithm Adjustment: Modify model architecture or training process
• Post-processing: Adjust outputs to ensure fairness constraints
• Process Changes: Implement human oversight and review mechanisms

Training & Awareness

Professional development in AI ethics and responsible AI practices

Course Catalog

RaiDOT offers comprehensive training programs designed for different roles and experience levels:

Learning Paths

Structured programs tailored to your role:

For AI Developers & Engineers

• Responsible AI Development Practices
• Bias Testing and Mitigation Techniques
• Explainable AI Implementation
• Privacy-Preserving Machine Learning

For Business Leaders & Managers

• AI Governance and Strategy
• Risk Management Frameworks
• Regulatory Landscape Overview
• Building Ethical AI Culture

For Compliance & Legal Teams

• AI Audit Methodologies
• Documentation and Reporting
• Legal and Regulatory Updates
• Risk Assessment Validation

Certification Programs

Earn industry-recognized certifications upon successful completion:

Course Access by Plan

• Free Plan: Access to basic awareness materials and introductory courses
• Bronze Plan: Full course catalog access with progress tracking
• Gold Plan: All courses plus premium content, certification programs, and priority support

Expert Consultancy

One-on-one guidance from AI ethics and compliance specialists

Consultation Areas

Our experts provide guidance across multiple domains:

Booking Process

1. Schedule Session: Select available time slots through your dashboard
2. Provide Context: Share information about your AI system and specific challenges
3. Expert Matching: Get paired with a specialist in your domain
4. Consultation: Participate in video or phone consultation session
5. Follow-up: Receive written summary and actionable recommendations

Our Expert Network

Consultations are conducted by qualified professionals with deep expertise:

• Academic Researchers: PhD holders in AI ethics, machine learning, and related fields
• Industry Practitioners: Senior professionals with 10+ years in AI development and deployment
• Regulatory Specialists: Former regulators and policy experts familiar with AI governance
• Sector Experts: Specialists in healthcare, finance, automotive, and other key industries

Consultation Types

Strategy Sessions (60 minutes)

High-level guidance on AI governance, risk strategy, and organizational approaches.

Technical Reviews (45 minutes)

Detailed analysis of specific AI systems, models, or implementation approaches.

Compliance Check (30 minutes)

Focused review of regulatory requirements and compliance status.

Follow-up Sessions (30 minutes)

Progress review and additional guidance on previously discussed topics.

Compliance & Regulatory

Stay compliant with evolving AI regulations worldwide

Regulatory Coverage

RaiDOT helps you navigate the complex landscape of AI regulations:

EU AI Act Classification

RaiDOT automatically classifies your AI systems according to EU AI Act categories:

High-Risk AI Systems

• Biometric identification and categorization
• Critical infrastructure management
• Education and vocational training
• Employment and worker management
• Access to essential services
• Law enforcement
• Migration, asylum, and border control
• Administration of justice

Limited Risk AI Systems

• AI systems that interact with natural persons
• Emotion recognition systems
• Biometric categorization systems
• AI systems generating synthetic content

Compliance Documentation

Generate the documentation needed for regulatory compliance:

• Risk Management Documentation: Comprehensive risk assessments and mitigation plans
• Quality Management Systems: Processes for ensuring AI system quality and reliability
• Data Governance Documentation: Data quality, lineage, and protection measures
• Human Oversight Procedures: Documentation of human involvement in AI decisions
• Transparency Reports: Clear explanations of AI system functionality and limitations

Compliance Checklist

Key requirements for AI system compliance:

• □ Risk assessment completed and documented
• □ Bias testing and fairness evaluation performed
• □ Data governance framework established
• □ Quality management system implemented
• □ Human oversight mechanisms defined
• □ Transparency and explainability measures in place
• □ Continuous monitoring procedures established
• □ Incident response plan developed
• □ User communication strategy implemented
• □ Regular review and update schedule defined

Pricing

Simple, transparent pricing for AI risk management

What's Included

Free Plan

• 2 risk evaluations per month
• Basic training materials
• Community support
• Basic PDF reports

Bronze Plan

• 10 risk evaluations per month
• 5 fairness analyses per month
• 1 hour expert consultancy per month
• Full training course catalog
• Certification programs
• Enhanced reporting
• Priority email support

Gold Plan - $49.99/month

• Unlimited risk evaluations
• Unlimited fairness analyses
• 3 hours expert consultancy per month
• All training courses + premium content
• All certification programs
• Complete compliance documentation
• Phone + priority email support
• Advanced analytics and reporting

Getting Started

Ready to begin your AI risk management journey?

• Start Free: Create your account at platform.raidot.ai
• Upgrade Anytime: Switch plans as your needs evolve
• Cancel Anytime: No long-term commitments required

Account Setup

Creating and managing your RaiDOT account

Creating Your Account

Getting started with RaiDOT is simple and secure:

1. Visit Registration: Go to platform.raidot.ai/register
2. Choose Sign-up Method: Use email/password or Google/LinkedIn authentication
3. Verify Email: Check your inbox and click the verification link
4. Complete Profile: Add your professional information and preferences
5. Select Plan: Choose the plan that best fits your needs

Account Security

RaiDOT implements enterprise-grade security measures:

• Secure Authentication: Password encryption and secure session management
• Data Protection: GDPR-compliant data handling and storage
• Privacy Controls: Granular privacy settings and data export options
• Audit Logs: Complete activity tracking for compliance purposes

Profile Management

Manage your account settings and preferences:

• Personal Information: Update contact details and professional background
• Notification Preferences: Control email notifications and alerts
• Privacy Settings: Manage data sharing and privacy options
• Subscription Management: View usage, upgrade plans, and manage billing

Team Management

For organizations with multiple users:

• User Roles: Assign appropriate access levels to team members
• Shared Resources: Access shared evaluations and reports
• Billing Management: Centralized billing and usage tracking
• Admin Controls: Organization-wide settings and policies

Best Practices

Guidelines for effective AI risk management with RaiDOT

Before Starting an Evaluation

• Gather Documentation: Collect technical specifications, data descriptions, and system architecture details
• Identify Stakeholders: Include relevant team members from technical, legal, and business functions
• Define Use Cases: Clearly articulate how the AI system will be used and by whom
• Set Expectations: Understand that thorough evaluations require time and honest assessment

During the Evaluation Process

• Be Comprehensive: Answer all questions thoroughly and honestly
• Provide Context: Use comment fields to explain unique aspects of your system
• Consult Experts: Involve technical and domain experts in answering specialized questions
• Document Assumptions: Note any assumptions made during the evaluation

After Receiving Results

• Review Thoroughly: Carefully examine all findings and recommendations
• Prioritize Actions: Focus on high-impact, high-priority recommendations first
• Create Action Plans: Develop specific, measurable implementation plans
• Monitor Progress: Track implementation progress and measure improvements

Ongoing Risk Management

• Regular Re-evaluation: Conduct periodic assessments as systems evolve
• Stay Informed: Keep up with regulatory changes and industry best practices
• Continuous Learning: Participate in training programs and professional development
• Share Knowledge: Contribute to organizational learning and best practice sharing

Common Mistakes to Avoid

• Rushing the Process: Taking insufficient time for thorough evaluation
• Limited Scope: Evaluating only technical aspects while ignoring social impact
• One-Time Assessment: Treating risk evaluation as a one-time activity rather than ongoing process
• Ignoring Stakeholders: Failing to involve relevant team members and end users