The Ethics Of AI: How To Avoid Harmful Bias And Discrimination -- ADTmag

The Ethics Of AI: How To Avoid Harmful Bias And Discrimination

Build trust in the age of AI.

How aware are you of hidden bias in your machine learning models and neural networks? A biased model can harm your customers, your brand and your business if it results in unintended, unexplainable actions against individuals or groups. Every organization that develops or uses AI, or hosts or processes data, must do so in ways that allow them to rationalize the decisions or recommendations in a way that is easily consumable.

Read the Forrester report “The Ethics of AI: How To Avoid Harmful Bias And Discrimination” to learn:

• Three ways that data, models and algorithms can go wrong

• Real-world examples of model-driven bias

• Forrester’s framework for ensuring that models are “FAIR”

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