As a data scientist, you are tasked with identifying bias in training data in an effective way. Which of the tools below can be used to identify bias in data?TensorFlow Data Validation, TensorFlow Serving.TensorFlow Data Validation, What-if Tool.TensorFlow Datasets, What-if Tool.TensorFlow Data Validation, TensorBoard.

True or False: In a supervised learning model, Bias refers to the error that is introduced from the assumptions of the data analyst.Question 5Select one:TrueFalse

Which of these are good practices for addressing bias in AI? (Select all that apply)1 pointTechnical solution such as “zeroing out” biasSystematic auditing processes to check for biasUsing more inclusive/less biased dataUsing an adversarial attack on the AI system to change its outputs to be less biased

Which of the following is a viable method to reduce bias when humans are deployed to label data for training an AI?Group of answer choicesUse only a single annotator for one set of data to reduce inconsistency.Use a large set of annotators with diverse demographic characteristics.Use another AI to suggest labels and humans adjust them if needed.Use only publicly available data, avoiding private and sensitive information.

What are the causes of AI bias?Bias is introduced intentionally by AI developers for experimental purposesAI bias is a myth and does not actually existBias is solely caused by the AI’s programming languageMost training data for AI comes from Western countries

Which of the following is a way to diagnose bias and variance in a model?Question 5AnswerA.L1 regularizationB.Feature engineeringC.Cross-validationD.Gradient descent