A route to more reliable, forward facing solutions with active Human · AI Collaboration

  • Reduced risk. By including humans as collaborators in the system, close to the point of decisioning, they can quickly rectify any errors made by the AI, preventing issues from propagating through and impacting the wider system.
  • Improved performance over time through continuous metalearning. The AI system learns from human corrections and can ask better questions of human counterparts, increasing performance gains.
  • Bias mitigation through Human-AI collaboration. No single human is responsible for training the AI system. Instead, a diverse group of individuals with varying experiences and beliefs each have an impact, helping to reduce the impact of individual biases and blind spots.
  • Increased flexibility. Active learning lets you improve models over time, so you don’t need to throw all your data at the model at once. This reduces training time, emissions and lets you iterate on the solution as requirements change rather than requiring complete retraining.

Author

Mind Foundry

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Mind Foundry is an Oxford U. Company. Operating at the intersection of innovation, research, and usability we empower teams with AI built for the real world.

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Mind Foundry

Mind Foundry

Mind Foundry is an Oxford U. Company. Operating at the intersection of innovation, research, and usability we empower teams with AI built for the real world.

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