1. AI Problem Definition

1.1 Identify the problem you are trying to solve using AI (e.g., user segmentation, improving customer service)
• Identify the need that will be addressed
• Find out what information comes in and what output is expected
• Determine whether AI is called for
• Consider upsides and downsides of AI in the situation
• Define measurable success
• Benchmark against domain or organization-specific risks to which the project may be susceptible

1.2 Classify the problem (e.g., regression, unsupervised learning)
• Examine available data (labeled or unlabeled?) and the problem
• Determine problem type (e.g., classification, regression, unsupervised, reinforcement)

1.3 Identify the areas of expertise needed to solve the problem
• Identify business expertise required
• Identify the need for domain (subject-matter) expertise on the problem
• Identify AI expertise needed
• Identify implementation expertise needed elligence

1.4 Build a security plan
• Consider internal access levels or permissions
• Consider infrastructure security
• Assess the risk of using a certain model or potential attack surfaces (e.g., adversarial attacks on real-time learning model)

1.5 Ensure that AI is used appropriately
• Identify potential ways that the AI can mispredict or harm specific user groups
• Set guidelines for data gathering and use
• Set guidelines for algorithm selection from user perspective
• Consider how the subject of the data can interpret the results
• Consider out-of-context use of AI results

1.6 Choose transparency and validation activities
• Communicate intended purpose of data collection
• Decide who should see the results
• Review legal requirements specific to the industry with the problem being solved

2. Data Collection, Processing, and Engineering

2.1 Choose the way to collect data
• Determine type/characteristics of data needed
• Decide if there is an existing dataset or if you need to generate your own
• When generating your own dataset, decide whether collection can be automated or requires user input

2.2 Assess data quality
• Determine whether the dataset meets needs of task
• Look for missing or corrupt data elements

2.3 Ensure that data are representative
• Examine collection techniques for potential sources of bias
• Make sure the amount of data is enough to build an unbiased model

2.4 Identify resource requirements (e.g., computing, time complexity)
• Assess whether the problem is solvable with available computing resources
• Consider the budget of the project and the resources that are available

2.5 Convert data into suitable formats (e.g., numerical, image, time series)
• Convert data to binary (e.g., images become pixels)
• Convert computer data into features suitable for AI (e.g., sentences become tokens)

2.6 Select features for the AI model
• Determine which data features to include
• Build initial feature vectors for test/train dataset
• Consult with subject-matter experts to confirm feature selection

2.7 Engage in feature engineering
• Review features and determine what standard transformations are needed
• Create processed datasets

2.8 Identify training and test datasets
• Separate available data into training and test datasets
• Ensure test dataset is represented

2.9 Document data decisions
• List assumptions, predicates, and constraints upon which design choices have been reasoned
• Make this information available to regulators and end users who demand deep transparency 

3. AI Algorithms and Models

3.1 Consider applicability of specific algorithms
• Evaluate AI algorithm families
• Decide which algorithms are suitable, e.g., neural network, classification (like decision tree, k means)

3.2 Train a model using the selected algorithm
• Train model for an algorithm with best-guess starting parameters.
• Tune the model by changing parameters
• Gather performance metrics for the model
• Iterate as needed

3.3 Select specific model after experimentation, avoiding overengineering
• Consider cost, speed, and other factors in evaluating models
• Determine whether selected model meets explainability requirements

3.4 Tell data stories
• Where feasible, create visualizations of the results
• Look for trends
• Verify that the visualization is useful for making a decision

3.5 Evaluate model performance (e.g., accuracy, precision)
• Check for overfitting, underfitting
• Generate metrics or KPIs
• Introduce new test data to cross-validate robustness, testing how model handles unforeseen data

3.6 Look for potential sources of bias in the algorithm
• Verify that inputs resemble training data
• Confirm that training data do not contain irrelevant correlations we do not want classifier to rely on
• Check for imbalances in data
• Guard against creating self-fulfilling prophecies based upon historical biases
• Check the explainability of the algorithm (e.g., feature importance in decision trees)

3.7 Evaluate model sensitivity
• Test for sensitivity of model
• Test for specificity of model

3.8 Confirm adherence to regulatory requirements, if any
• Evaluate outputs according to thresholds defined in requirements
• Document results

3.9 Obtain stakeholder approval
• Collect results and benchmark risks
• Hold sessions to evaluate solution

4. Application Integration and Deployment

4.1 Train customers on how to use product and what to expect from it
• Inform users of model limitations
• Inform users of intended model usage
• Share documentation
• Manage customer expectations

4.2 Plan to address potential challenges of models in production
• Understand the types of challenges you are likely to encounter
• Understand the indicators of challenges
• Understand how each type of challenge could be mitigated

4.3 Design a production pipeline, including application integration
• Create a pipeline (training, prediction) that can meet the product needs (may be different from the experiment)
• Find the solution that works with the existing data stores and connects to the application
• Build the connection between the AI and the application
• Build mechanism to gather user feedback
• Test accuracy of AI through application
• Test robustness of AI
• Test speed of AI
• Test application to fit size of use case (e.g., in AI for mobile applications)

4.4 Support the AI solution
• Document the functions within the AI solution to allow for maintenance (updates, fixing bugs, handling edge cases)
• Train a support team
• Implement a feedback mechanism
• Implement drift detector
• Implement ways to gather new data

5. Maintaining and Monitoring AI in Production

5.1 Engage in oversight •
Log application and model performance to facilitate security, debug, accountability, and audit
• Use robust monitoring systems
• Act upon alerts
• Observe the system over time in a variety of contexts to check for drift or degraded modes of operation
• Detect any way system fails to support new information

5.2 Assess business impact (key performance indicators)
• Track impact metrics to determine whether solution has solved the problem
• Compare previous metrics with new metrics when changes are made
• Act on unexpected metrics by finding problem and fixing it

5.3 Measure impacts on individuals and communities
• Analyze impact on specific subgroups
• Identify and mitigate issues
• Identify opportunities for optimization

5.4 Handle feedback from users
• Measure user satisfaction
• Assess whether users are confused (e.g., do they understand what the AI is supposed to do for them?)
• Incorporate feedback into future versions

5.5 Consider improvement or decommission on a regular basis
• Combine impact observations (e.g., business, community, technology trends) to assess AI value
• Decide whether to retrain AI, continue to use AI as is, or to decommission A