Safeguarding AI: Accountability

AI Ethics

Joe Franklin

Associate Data Literacy and Essentials Manager, DataCamp

Define accountability

• Accountability:
  • Assigning responsibility for AI outcomes
• Critical in AI's development, deployment, and use
• AI isn't a responsibility-evading "magic wand"

Accountability is vital

• People trust AI systems more when there is accountability
• Accountability ensures ethical use and mitigates potential harm
• Accountability means not absolving humans from responsibility

             

The paradox of accountability

• Increasing AI accountability can improve trust
• Yet, excessive trust in AI can lead to misguided decisions
• Example:
  • Georgia Tech study where participants followed misguided robot guidance

The Tesla story

• Misunderstanding of the auto-pilot capabilities among consumers
• Criticism for Tesla's insufficient safeguards
• Both Tesla and consumers share responsibility

Achieving accountability

• AI producers:
  • Achieving accountability involves transparency and solving the 'Black Box' problem
  • Attributing responsibility is key

• AI consumers:
  • 'Trust but verify'
• Producers and consumers both play a role in creating ethical AI
• Challenges are opportunities for innovation

¹ Icons made by Eucalyp & Sumitsaengtong from www.flaticon.com

No one-size-fits-all

• Accountability in AI is a continuous journey
• With each AI advancement, the accountability conversation evolves
• No one-size-fits-all approach; varies across industries

¹ Icon made by Freepik from www.flaticon.com

Let's practice!

AI Ethics