Artificial Intelligence: Practical Strategies for Practitioners

This article identifies practical strategies for thoughtfully engaging with AI, whether you are looking for ways to simply become more informed or to connect and learn with other practitioners deploying AI for various purposes. 

Educate yourself on AI fundamentals. 

Before being able to engage more deeply with AI, it is essential to have a foundational understanding of key concepts including artificial intelligence (general and narrow), rules-based or symbolic AI, and machine learning (supervised, unsupervised, reinforcement and deep).  

• The Organisation for Economic Co-operation and Development has a downloadable working paper, “Artificial Intelligence and its Use in the Public Sector,” that provides a straightforward primer on these and other foundational concepts.  

• Google Cloud Tech has developed a series of publicly accessible videos explaining machine learning. 
  
   

Attend trainings to deepen your foundational understanding of AI. 

While learning independently about AI fundamentals can be a powerful first step, it could also be helpful to deepen your understanding and ask clarifying questions in a more interactive environment.  

• The Glenn College now offers a one-day course, Emerging Roles for AI in Public Service Organizations, through our Management Advancement for the Public Service (MAPS) program. Interested? Contact Megan Hasting, MAPS program manager. 

• If you prefer to learn online at your own pace, AI for Everyone and Generative AI for Everyone are non-technical courses that can help you deepen your foundational understanding of AI. 

Gain a critical perspective on AI.  

In addition to building a foundational understanding of AI, it is important to think about this powerful new technology through a critical lens. The following books offer accessible and insightful critical perspectives on AI:  

• “Weapons of Math Destruction” by Cathy O’Neil  

• “The Age of AI: And Our Human Future” by Henry Kissinger, Eric Schmidt and Daniel Huttenlocher 

• “Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence” by Kate Crawford 

Identify AI use cases relevant to your organization.  

After building your foundational and critical understanding of AI broadly, it is important to think about how it might be usefully deployed in your organization.  

• Management magazines like the Sloan Management Review and Harvard Business Review regularly publish pieces on AI use in different organizational contexts.  

• The Organisation for Economic Co-operation and Development working paper “Artificial Intelligence and its Use in the Public Sector” mentioned earlier describes several public sector AI use cases, as does “A Policy Maker’s Guide to Artificial Intelligence for State and Local Governments.” 

Stay informed about changes in AI governance. 

Currently, policymakers and regulatory agencies in the United States are beginning to take more concrete steps toward AI regulation, such as the White House Office of Science and Technology Policy’s “Blueprint for an AI Bill of Rights.” However, AI governance in the United States is predominantly approached as an organizational issue, meaning that tech companies and chief information and technology officers have great discretion. As AI governance in the United States continues to evolve, it is important to stay informed.  

• Set up a news alert on your preferred search engine. 

• Sign up for electronic newsletters like the Center for AI and Digital Policy Update. 

Understand the risks of using AI. 

After identifying how AI can be used in your organization, it is important to assess associated risks. Keep in mind that risks can emerge at different stages of the adoption cycle and vary depending on your development or procurement strategy and the extent to which AI supports or replaces human decision-making.  

• Both the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence and the EU AI Act underscore the importance of adopting a risk approach.  

• The National Institute of Standards and Technology Artificial Intelligence Risk Management Framework, the International Standards Organization Guidelines 23894 and 31000, and the Organisation for Economic Co-operation and Development’s Algorithmic Impact Assessment tool, among other tools, can be used to identify and manage risks associated with AI, automated decision-making, and data protection. associated with AI, automated decision-making, and data protection.  

Assess your risk tolerance. 

Just because you could does not mean your organization should use AI. After understanding risks associated with AI, your organization must decide which are worth taking and under what conditions. Each organization must consider how its values, legal framework, internal processes and capabilities influence AI adoption decisions. Further, risk and regulation management frameworks, such as the recently approved EU AI act, and the UK Information Commissioner’s Office (ICO) guidance on AI and Data protection, emphasize the need to engage stakeholders when AI systems will be deployed for public service provision, even when systems are developed by private entities.

• The Singapore Personal Data Protection Commission’s AI Model Governance Framework  

• European Center for Not-for-Profit Law’s Framework for Meaningful Engagement. 

Develop responsible AI management structures and processes.  

Before deploying AI, your organization needs to develop processes and structures to detect and respond to issues early and effectively. Clear processes and structures allow your organization to reduce the potential harms of AI and promote transparency and accountability in developing and using these systems. Management approaches include but are not limited to requiring approval of new AI use by a unit or committee, creating checklists to ensure precautions are taken, providing training to those developing and managing AI systems, and creating incentives to catch-solve-escalate issues, etc.  

• The Program on Data and Governance at The Ohio State University recently published a report, “Responsible AI Management,” due to be released soon, and a book, “Business Data Ethics: Emerging Models for Governing AI and Advanced Analytics.” 

• Additional resources can be found in the Ethical Toolkit created by the Markkula Center for Applied Ethics at Santa Clara University.  

Foster a responsible AI culture.  

Generative tools like ChatGPT make it easy to use AI in innovative and flexible ways, but risks include privacy, intellectual property infringement, inaccuracy and error, among others. Traditional top-down control approaches are less effective when the organization cannot gatekeep access to these tools. Not everything can be written into policies and procedures or can be observed by a manager 100% of the time. In these gray areas, your organization can benefit from having a strong responsible AI culture.  

Ideally, everyone in your organization pays attention and plays a role in catching potential issues and critically thinking about the impact of their AI use on their organization and the community.