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Prompts for assessing AI projects

What challenge does the AI investment solve?

How does the AI meet the challenge?

Who is providing leadership?

  • Is leadership's AI strategy documented and articulated well?
  • Does the AI investment strategy align with the organization's overall strategy, culture, and values? Does the organization appreciate experimental processes?
  • Is there a time constraint? Does the schedule meet the Technology Readiness Level (TRL) of the AI investment?
  • Is the AI investment properly resourced? budgeted, trained staff with key positions filled?
  • Responsibility clearly defined and communicated for AI research, performing data science, applied machine intelligence engineering, qualitative assurance, software development, implementing foundational capabilities, user experience, change management, configuration management, security, backup/contingency, domain expertise, and project management
  • Of these identified responsibilities which situations are they outsourced? What strategy is incorporated to convey the AI investment knowledge to the organization?
  • Is the organization positioned or positioning to scale its current state with AI?

Are best practices being followed?

What Laws, Regulations and Policies (LRPs) pertain, e.g. GDPR??

  • Are use cases testable and traceable to requirements, including LRPs?
  • When was the last time compliance requirements and regulations were examined? What adjustments were/must be made?
  • Does the AI investment require testing by external assessors to ensure compliance and/or auditing requirements?

What portion of the AI is developed inhouse and what is/will be procured?

  • If the AI is procured/outsourced, e.g. embedded in sensor product, what items are included in the contract to future proof the solution?
  • Contract items to protect organization reuse data rights?
  • Does acceptance criteria include a proof of capability?
  • How well do a vendor's service/product(s) and/or client references compare with the AI investment objectives?
  • How is/was the effort estimated? If procured AI, what factors were used to approximate the needed integration resources?

How is AI success measured?

  • What are the significant measures that indicate success? Are tradeoff rationale documented, e.g. accuracy vs speed?
  • Are the ways the mission is being measured clear, realistic, and documented? Specifically what are the AI investment's performance measures?
  • What is the Return on Investment (ROI)? Is the AI investment on track with original ROI target?
  • If there is/was an Analysis of Alternatives how were these measures used? What were the findings?
  • What mission metrics will be impacted with the AI investment? What drivers/measures have the most bearing? Of these performance indicators which can be used as leading indicators of the health of the AI investment?
  • What are the specific decisions and activities to impact each driver/measure?
  • What assumptions are being made? Of these assumptions, what constraints are anticipated?
  • Where does the AI investment fit in the portfolio? Are there possible synergies with other aligned efforts in the portfolio? Are there other related AI investments? If so, is this AI investment dependent on the other investment(s)? What investments require this AI investment to be successful? If so, how? Are there mitigation plans in place?
  • How would you be able to tell if the AI investment was working properly?
  • Is/will A/B testing or multivariate testing be performed?

What AI governance is in place?

What is the algorithm administration strategy?

  • What is the deployment vision? What attributes are being used to size the investment, count of users, queries, installations, etc.? What is the Minimum Viable Product (MVP) version of the AI investment that has enough features to satisfy early users and provide feedback for future investment development.? If an incremental rollout, how what is the strategy, portion of the users, markets, locations, capabilities?
  • What tool(s) are used or will be used for model management?
    • How are Hyperparameters managed? What optimizers are used, e.g. automated learning (AutoML)?
    • What components, e.g. optimizer, tuner, training, versioning, model dependencies; e.g. training data, dataset(s), historical lineage, publishing, performance evaluations, and model storing are integrated in the model management tool(s)?
    • What is the reuse strategy? Is there a single POC for the reuse process/tools?
      • Are the AI models published (repo, marketplace) for reuse, if so where?
      • Is the AI model reused from a repository (repo, marketplace)? If so, which one(s)?
    • Is Master Data Management (MDM) in place? What tools are available or being considered?
      • Is data lineage managed?
      • What data cataloging capabilities exists today? Future capabilities?
      • How are versioning|data versions controlled?
      • How are the dataset(s) used for AI training, testing and validation managed?
      • Are logs kept on which data is used for different executions/training so that the information used is traceable?
      • How is the access to the information guaranteed? Are the dataset(s) for AI published (repo, marketplace) for reuse, if so where?
  • What is the development & implementation plan?
    • What foundational capabilities are defined or in place for the AI investment? infrastructure platform, cloud resources?
    • How is the AI investment deployed?
      • What is the plan for model serving? For each use case, is the serving batched or streamed? If applicable, have REST endpoints been defined and exposed?
      • Is the AI investment implementing an AIOps pipeline/toolchain?
      • What tools are used for the AIOps? Please identify those on-premises and online services?
      • Are the AI languages, libraries, scripting, and AIOps applications registered in the organization?
    • Are the processes and decisions architecture driven to allow for end-to-end visibility and allow for dependency management? Is information mapped to the intended use to allow analytics and visualizations framed in context?
      • Does the AI investment depict the AIOps pipeline/toolchain applications in its architecture, e.g tech stack?
      • Does the SecDevOps depict the AI investment in its architecture and how the health metrics are depicted?
      • Is algorithm administration reflected in the AIOps pipeline/toolchain processes/architecture?
    • How is production readiness determined?
      • Does the team use ML Test Score for production readiness?
        • What are the minimum scores for Data, Model, ML Infrastructure, and Monitoring tests?
        • What score qualifies to pass into production? What is the rationale for passing if less than exceptional (score of >5)?
        • What were the lessons learned? Were adjustments made to move to a higher score? What were the adjustments?
      • Who makes the determination when the AI investment is deployed/refreshed?
      • How does the team ready for cybersecurity? use the MITRE ATT&CK™ Framework? ...use the GSA DevSecOps Guide?

How are changes identified and managed?


Nature of risks inherent to AI applications: We believe that the challenge in governing AI is less about dealing with completely new types of risk and more about existing risks either being harder to identify in an effective and timely manner, given the complexity and speed of AI solutions, or manifesting themselves in unfamiliar ways. As such, firms do not require completely new processes for dealing with AI, but they will need to enhance existing ones to take into account AI and fill the necessary gaps. The likely impact on the level of resources required, as well as on roles and responsibilities, will also need to be addressed. AI and risk management: Innovating with confidence | Deloitte

How Should We Evaluate Machine Learning for AI?: Percy Liang
YouTube: https://www.youtube.com/watch?v=7CcSm0PAr-Y

Machine learning has undoubtedly been hugely successful in driving progress in AI, but it implicitly brings with it the train-test evaluation paradigm. This standard evaluation only encourages behavior that is good on average; it does not ensure robustness as demonstrated by adversarial examples, and it breaks down for tasks such as dialogue that are interactive or do not have a correct answer. In this talk, I will describe alternative evaluation paradigms with a focus on natural language understanding tasks, and discuss ramifications for guiding progress in AI in meaningful directions. Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).

Machine Learning, Technical Debt, and You - D. Sculley (Google)
YouTube: https://www.youtube.com/watch?v=V18AsBIHlWs

Machine Learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. In this talk, we'll argue it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems. We explore several ML-specific risk factors to account for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns. We then show how to pay down ML technical debt by following a set of recommended best practices for testing and monitoring needed for real world systems. D. Sculley is a Senior Staff Software Engineer at Google