This dataset about Responsible Artificial Intelligence (AI) is the perfect tool for companies and organizations looking to ensure ethical and responsible implementation of AI technologies.
The comprehensive dataset includes quantitative and qualitative drivers, as well as risks associated with using AI to ensure sound decision making and reduce human bias in a range of applications.
Each category provides detailed analysis into relevant issues and possible solutions related to ensuring responsible AI operations.
The Governance & Policies category contains information on how organizations can evaluate ethical risks, create clear definitions and guidelines, and monitor compliance.
Additionally, there are resources for identifying legal and regulatory frameworks that must be adopted when using AI technology.
Lastly, this section includes suggested policies and best practices for working with AI in different contexts.
Additionally, it provides key strategies for investing in the educational training of employees.
The information also contains suggestions on how to use AI to benefit diverse communities and increase diversity within AI teams.
The Human-in-Loops section focuses on how to utilize human intelligence within AI processes.
It outlines various techniques for gathering and capturing data in order to drive more effective decisions.
There are also tools to help identify potential areas where humans can provide feedback, guidance, or oversight when utilizing AI. The Documentation category provides guidelines for gathering, storing, and documenting data related to AI.
This section includes resources for data mapping as well as considerations for security and privacy.
Additionally, there are tools for creating audit trails and assessing data system integrity.
The Data Sets & Inference category centers on how data sets can be used to facilitate decision making and how accurate inferences can be made from those data sets.
This includes methods for collecting and cleaning data sets to support rigorous inference analysis. The Systematic Analysis & Monitoring section provides resources for examining how AI systems work in order to detect changes in biases, outliers, or errors.
This section outlines approaches to automated testing, creating monitoring systems, and managing AI models.
Overall, this dataset provides companies and organizations access to a comprehensive collection of resources on responsible AI implementation.
It offers perspective on how to reduce bias, ensure accuracy, and provide oversight to AI processes.
CONTENTS:
109 Responsible AI Functions and their Responsibilities
417 Essential Inquiries Regarding Responsible AI
1598 Responsible AI Recommendations
..and all their relationships covering Responsible AI and its connections to:
AI Standards
Ensuring Access
Responsible AI
Digital Ethics
Autonomous Drones
Fraud Detection
Responsible Use
Data governance
Policy
Data Regulation
Why AI Matters In Self Service Analytics
Autonomous Systems
Deep Learning
Decision Support
Source Code
Financial forecasting
Content creation
Ml And AI Cloud
Scaling AI For Businesses Service
Data Accountability
Customer Service AI Management
Responsible AI deployment
Data Strategy
Privacy Impact Assessment
Sandbox Platforms
Data compression
Product Safety
AI Causal Tracing Risk
Artificial Intelligence
AI Security
Identifying Use Cases That Benefit From Artificial Intelligence
Responsible Algorithms
AI Avoidance
Security Measures
Data Sharing
Regulatory Compliance
Data Security
Virtual Assistants
Content Strategy
Protection Policy
AI In Business Rules
Risk Systems
Artificial Intelligence Systems Integration Principle
Artificial Intelligence Rules
Data Analysis
Risk Mitigation
Data Innovation
Cybersecurity defense
Expert Systems
Surveillance Authorities
Frequently Asked
Disruption Technology
Ensuring Safety
Artificial Intelligence Ethics Data
Common Focuses For AI Solutions
Artificial Intelligence In Postmodern Review
Augmented Reality
AI and responsible governance
AI Ethics
Cool Vendors In Personal Devices Exploiting AI And Ux Design
Control Management
Data Ethics
Automated Trading
AI
Applied Artificial Intelligence
To For Your First AI Project
Sentiment Analysis
Data Mining
Cloud Computing
AI and decision-making
Conversational AI
Fundamentals Of Artificial Intelligence
Human Oversight
Media Platforms
Privacy Protection
Information Requirements
Privacy Policy
Risk Assessment
Adaptive AI Thresholds Audit
AI and responsible innovation
Automated Decision
Evaluate The Data Fit For AI Project
Safety Regulations
Risk Practices
Research Activities
Cyber Threats
Data Ownership
Artificial Intelligence Projects
Data Science
Transparency guidelines
Time Series Analysis
AI Governance Principles
There Will Be Another AI Winter Principle
Smart Cities
Artificial Intelligence In Postmodern Rules
Artificial Intelligence Ethics
Data Classification
