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your sources for bad data or hidden bias, such as race, gender, ideological differences, and the like. For example, a bank might remove gender and race from its loan-processing AI model, but U.S. ZIP codes can often serve as a proxy for race. Their inclusion could still lead to biased predictions that discriminate against historically underprivileged neighborhoods.

      6 Rigorously examine the criteria for identifying selection variables.

      7 Test machines for sources of bias and evidence of bias, and remedy any problems discovered.

      Although you might hear the term “artificial intelligence” bandied about as if it were a single thing, the reality is that it is an umbrella term for a vast discipline covering countless models or algorithms of varying complexity and rigor. Even within machine learning, dozens of methods can help you accomplish your goal, each used for a specific type of problem.

      Unsupervised learning

Goal Algorithm
Organize data in clusters or trees, such as evaluating investments according to volatility or return Hierarchical cluster analysis
Recommend a product or service based on the choices of similar customers Recommendation engine
Optimize delivery routes by identifying proximate destinations K-means clustering
Identify risk of heart disease based on heart sounds

      Supervised learning

Goal Algorithm
Detect fraud in financial transactions Random forest
Forecast for supply chain management Regression
Forecast sales Neural network
Underwrite loans Decision tree

      Deep learning

Goal Algorithm
Train smarter chatbots, perform language translation Recurrent neural network
Make medical diagnoses using computer vision

      Reinforcement learning

      This method of ML learns a task through trial and error based on preferring actions that are rewarded and avoiding actions that are not. You use reinforcement learning when you need to find the optimum way of interacting with an environment, such as to automate stock trading or to teach a robot to perform a physical task.

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