The Responsible AI Blog
Get the latest news and insights about Responsible AI and its transformative impact on today’s business landscape.
AI Risk Management
July 23, 2024
Fairness and Bias in Machine Learning: Definition and Mitigation Strategies
Reducing bias and ensuring fairness in machine learning can lead to equitable outcomes where technology benefits everyone.
July 16, 2024
AI Risk Management: Ensuring Data Privacy & Security
Learn AI risk management strategies for data privacy & security, including AI risks, policies, proactive measures, security practices & regulatory compliance.
July 5, 2024
What Might Your AI Risk Management Radar Miss?
Explore hidden AI risks and proactive strategies for comprehensive AI risk management. Stay ahead with insights on safeguarding your AI systems.
July 2, 2024
Adversarial Attacks in ML: Detection & Defense Strategies
Learn how adversarial machine learning exploits vulnerabilities. Explore seven cutting-edge defensive strategies for mitigating AML-driven threats.
June 18, 2024
Managing AI Generated Content: Legal & Ethical Complexities
Learn what AI generated content is & why AI generated content can harm your organization! Find out if you can copyright ai generated content. Read more.
May 28, 2024
AI Risk Management: The Importance of Transparency and Accountability
Curious to learn more about AI risk management? Stay with us and discover AI and machine learning challenges & AI models and data risk management.
April 30, 2024
The AI Revolution in Insurance: Risks & Solutions
AI is transforming insurance, streamlining claims, personalizing policies, detecting fraud, and enhancing customer experiences.
April 23, 2024
Beware the Pitfalls: Why Skimping on AI-Specific Risk Management Spells Trouble
Did you know that neglecting AI-specific risk management could spell trouble for your organization? Learn why in our latest blog post.
March 29, 2024
What You Should Know About ISO 42001
Did you know? Early attempts at AI risk management standardizatio are now being made, with ISO 42001 being the first global AI risk management standard. Read on to learn more.
March 26, 2024
4 Types of AI Cyberattacks Identified by NIST
NIST identifies four major types of cyberattacks and offers mitigation strategies to protect, detect, respond and recover.
March 7, 2024
NIST's Cybersecurity Framework 2.0 Was Officially Released. Here's What You Should Know
Learn more about NIST's Cybersecurity Framework 2.0 and how it can help your organization to efficiently manage risk.
February 20, 2024
Understand Your AI: Exploring the Opportunities and Risks of AI in Insurance
Read our latest blog post to find out more about how AI is shaping the future of insurance.
January 29, 2024
What you need to know about NIST's AI Risk Management Framework
NIST released a framework to guide and manage the use of AI products, services, and systems.
November 10, 2023
The future of healthcare: Navigating the applications and challenges of AI
Explore the future of healthcare as we delve into the applications and challenges of integrating AI. Understand potential benefits, risks, and the roadmap for a responsible AI approach.
March 2, 2023
Adversarial Attacks vs Counterfactual Explanations
Adversarial examples are closely related to counterfactual explanations, yet their goal is fundamentally different. Find out more in our latest blog post.
February 22, 2023
Types of Adversarial Attacks and How To Overcome Them
Machine Learning powered algorithms are susceptible to a variety of adversarial attacks that aim to degrade their performance. Here’s what you need to know.
January 26, 2023
Understanding Adversarial Attacks in Machine Learning
Adversarial attacks and adversarial learning have become key focus points for data scientists and machine learning engineers worldwide. Find out why.
August 31, 2022
Group Fairness vs. Individual Fairness in Machine Learning
In fair machine learning research, group and individual fairness measures are placed at distinct levels. Learn more about the nuances of algorithmic fairness.
July 29, 2022
The Nuances of Fairness in Machine Learning
As the problem of AI bias is becoming a global concern, companies should prioritize the implementation of a fair and equitable ML strategy. Find out more.