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7 Best practices for responsible AI

Today, artificial intelligence is everywhere and the value delivered is effective.

But it can come with new dynamic, ethical and social risks. A failure to manage AI-related risks can have a significant impact on users and citizens.

If you don't move, you risk significant financial, legal and reputational consequences. So, in these times of short-term pressure to produce AI success and delivery, there is still little value placed on risk management, including risk detection and reputation damage prevention.

No matter how well your data scientists do technically, and what responsible techniques they deploy, if you don't take collective action, it could extend beyond your own perimeter.


If you want to avoid this, then you need to check out the 7 Best Practices for Managing Responsible AI.

  1. You have an overview of all AI initiatives in your company.

  2. You have separate governance in place to manage AI risks, which prevents you from taking reactive approaches to responsible AI

  3. You've set your standard for minimum levels of AI knowledge so you can get started on your projects faster.

  4. You've translated ethical principles and academic theories into practical, measurable steps and thresholds that work for you.

  5. You've built in controls and metrics throughout the AI lifecycle, allowing for integrated risk management without inhibiting innovation

  6. You've implemented techniques to ensure fairness, bias, and explainability so you can prove your responsible use of AI in the event of a dispute.

  7. You have developed AI principles and policies and designed criteria for responsible AI, which means you have safeguards in place at your company.

If you start focusing on these first rules, you'll have all the perfect ingredients for your responsible AI journey.


If you don't want to spend too much time building IT solutions and tools and would rather focus on your own risk management content, lucky for you, there is now a solution.

Safe AI Now not only helps you assess your own risks, share insights from different perspectives across different levels of stakeholders, but also build concrete steps towards legal, robust and accountable AI. You'll focus on managing your own risks and mitigations instead of spending time building tools, frameworks, and IT solutions on how to manage AI risks.

And remember, companies that embrace responsible AI get a better return on their AI investment.

Do you want to know more on how to save time and energy in setting up your own action plan for responsible AI ?

Christine Laüt

Founder and CEO of SAFE AI NOW


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