EDGE OSDEFINITION
Bias in AI is systematic errors that cause unfair results within an AI system. This can occur due to a lack of a representative dataset, historical bias, data gathering, model creation, and evaluation. This can be found in various processes undertaken during health monitoring, recruitment, and even customer service. To stand strong against the biases of AI, it's necessary to have an accurate dataset and robust testing for human oversight.
Addressing bias within AI
- Ensures there are less biased results between groups.
- Provides high quality and representative results from the model.
- Can help us be aware of potential ethical, legal and reputation issues.
- Allows for the AI system to be checked in a transparent way.
AI bias detection use cases
- Check bias within recruitment algorithms.
- Check for systematic errors within AI recommendations.
- Test facial recognition across different demographics.
- Monitor for bias within AI decision making.
- Ensure there is no bias within lending models.
See it running on your own hardware
Vanik Edge OS runs AI models, agents and apps inside your building, so your data stays there.