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.
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:
Why AI Matters In Self Service Analytics
Ml And AI Cloud
Scaling AI For Businesses Service
Customer Service AI Management
Responsible AI deployment
Privacy Impact Assessment
AI Causal Tracing Risk
Identifying Use Cases That Benefit From Artificial Intelligence
AI In Business Rules
Artificial Intelligence Systems Integration Principle
Artificial Intelligence Rules
Artificial Intelligence Ethics Data
Common Focuses For AI Solutions
Artificial Intelligence In Postmodern Review
AI and responsible governance
Cool Vendors In Personal Devices Exploiting AI And Ux Design
Applied Artificial Intelligence
To For Your First AI Project
AI and decision-making
Fundamentals Of Artificial Intelligence
Adaptive AI Thresholds Audit
AI and responsible innovation
Evaluate The Data Fit For AI Project
Artificial Intelligence Projects
Time Series Analysis
AI Governance Principles
There Will Be Another AI Winter Principle
Artificial Intelligence In Postmodern Rules
Artificial Intelligence Ethics