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Paul Marrow
MA DPhil CITP MBCS, Data Science & AI Expert

Project Management for Responsible AI

Project management is as useful in software development for AI applications as it is in other project domains.

Artificial Intelligence (AI) is attracting a lot of attention. AI products like other software products are often delivered using project management. This is because the product delivery roadmap is often very complex.

Many choices are available for software project management. Agile project management seems particularly useful for AI projects because it emphasises the importance of people involved in the project (on both sides: product development and management; and customers and other external stakeholders such as regulators). Given the rapid change in AI technology, the emphasis in agile project management in dealing with change and using it to support the delivery of value also seems to make agile project management relevant. Agile project values do not restrict you to a particular type of project management since agile values have informed different types of project management.

Can agile project management be used to generate responsible AI (AI where a person in the organisation developing it takes responsibility for it)?

Responsible AI has enhanced requirements because of the different ways in which people can interact with AI, and because of the different ways in which value can be obtained from it. Here these are compared with the focus of agile project management, and it is suggested that this is a useful means of managing projects designed to produce responsible AI. Agile project management is particularly useful because it does not refer to a particular type of project management, but has inspired a choice of techniques, including Scrum and Kanban. A project manager of responsible AI can choose one appropriate for their organisation and their project.

Introduction

Artificial Intelligence (AI) has not achieved the original objective of the field since it commenced at a workshop in 1956[1] (of reproducing human intelligence within the decade). But it has become a large area of research and application.

Project Management

Project management is as useful in software development for AI applications as it is in other project domains. This is because a software project needed to deliver AI is likely to be too complex for one person to deliver. Irrespective of project management standards followed, APM (Association for Project Management 2006); PRINCE2 (Axelos 2023); PMP (Project Management Institute 2023), IPMA (International Project Management Association 2023); or software project methodology used (Beck 2001) (Shore 2021) (Rubin 2013) (Brechner 2015); a project manager has many alternatives for allocating tasks needed to deliver an AI application.

Responsible AI

What is responsible AI? If the AI under consideration is a product being brought to market, then responsible AI (see also (Diaz-Rodriguez 2023) for an alternative discussion) must be that which management can be responsible for. If you are responsible for such a product in a large enterprise, then there may be layers of management between you and the project manager and the development team. If you are the founder of a start-up producing an AI product you are likely to interact with the development team every day. These are not the only alternatives; different types of management may delegate different aspects of responsibility.

Agile Software Development

The Agile Manifesto (Beck 2001) was a declaration made by a group of software engineers given problems with earlier software project management techniques. A summary of Agile values (originally from (Beck 2001); reproduced in (Shore 2021)) is given in Figure 1 below.

Shore et. al (Shore 2021) mention that software projects based on agile principles do not imply a particular type of software project management. Rather, the Agile Manifesto has contributed to several directions in software project management that are often viewed on their own. For example, Scrum (Rubin 2013) and Kanban (Brechner 2015) incorporate agile principles.

Figure 1: Summary of the Agile Manifesto

The values of the Agile Manifesto although produced some time before the current emphasis on responsible AI are still relevant today:

  • Individuals and interactions emphasize the importance of interactions between the software engineers, project manager(s), product testers, product owner and senior managers during all parts of the project towards product roll out.
  • Working software shows the importance of continual testing and interaction between development team members and the project manager before the AI product roll out to ensure that the emerging software is fully understood, and no unexpected faults arise.
  • Customer collaboration reminds any organisation developing an AI application that the development is carried out for a customer, even if that is an internal customer. Clearly in many commercial contexts the customer will be external, business or consumer.
  • Responding to change is particularly relevant while AI innovation proceeds rapidly and the most successful start-ups will seek to deploy products quickly. That doesn’t mean that there won’t be unanticipated changes, but that there should be a means of responding to them.

Project Management for Responsible AI

Is there a particular management technique that is most appropriate for producing AI products? Which technique is chosen may depend upon trends and preferences within your organisation and go beyond anything discussed here.

However, the values of agile software development introduced above (Beck 2001) (Shore 2021) that have inspired other types of project management (Scrum (Rubin 2013) or Kanban (Brechner 2015)) link closely with requirements for responsible AI (Benjamins 2019) (Diaz-Rodriguez 2023) (UNESCO 2022). Because agile values do not refer to a particular type of project management, the term “agile” may not be mentioned in an operational project, but the values at least ought to be considered.

Putting People First

The Agile Manifesto refers to (see Figure 1): individuals and interactions and customer collaboration (my emphasis). Moving away from a rigid project process all the people involved in delivery of the AI product need to interact throughout, from project manager to the development team, product testers, product owner, senior managers and sales and marketing experts along with present and potential future customers to ensure that the project leads to success.

But responsible AI needs more. Any person involved needs to be able to ensure that the AI product (European Commission High-Level Expert Group on AI 2019) (Benjamins 2019):

  • Has a respect for human autonomy.
  • Does not cause harm[2].
  • Acts fairly and does not discriminate.
  • Produces transparent and explicable results.
  • Uses data in a private and secure manner.

All these points refer to people associated with AI, in this case a project delivering an AI product. Different bodies of stakeholders may be involved in each case, but the sophistication of the technology does not avoid the requirement for people to understand what is happening. Telefonica (Benjamins 2019) have made the good point that these criteria should not just be achieved with respect to current stakeholders, but that they should be transferrable to any third party that becomes involved.

Delivering Value

The Agile Manifesto refers to (Figure 1): working software and responding to change (my emphasis). This means continual testing of the AI product during its development, as well as interaction between the individuals involved in the development and management of that development to learn about the outcome of that testing. Testing doesn’t stop when the product is completed. There will need to be further testing by the software developers of the complete product to check its robustness and to test for unexpected behaviour. There will need to be testing with either potential customers of this product or actual customers of previous products to learn about the behaviour of customers with this new product. The information from these activities should inform sales and marketing activities.

Because this is a discussion of an AI product this feeds directly back towards all the issues that need to be considered under the heading of Putting People First above. But it also informs how the project manager delivering an AI product can deal with the substantial requirements for responsible AI and still be successful.

A successful project must include iterative development, adaptive planning, and flexibility so that change management is conventional rather than exceptional. In this way the project manager can deliver:

  • Business value. Return on investment into the project.
  • Innovation value. A product that advances insights compared to the competition.
  • Social and environmental value. A product that reduces the energy and resource demand for its operation compared to the competition.
  • Insight value. A product that delivers technical insights into the organisation for future development.
  • Process value. Improvement in project management processes that can be more closely linked to the requirements of future projects.

See (Shore 2021) for more detail.

Conclusion

Many techniques exist for software project management that can inform development of AI projects. Here the emphasis of agile techniques on putting people first and delivering value are suggested to be particularly appropriate for responsible AI because of the flexibility and adaptability they entail. It is for these reasons that current project managers of responsible AI can learn from agile project management.


[1] Where the term was first used.

[2] A shorter version of the original text.

References

Association for Project Management. 2006. APM Body of Knowledge. Princes Risborough, UK: Association for Project Management.

Axelos. 2023. PRINCE2 Certification. Accessed July 21, 2023. https://www.axelos.com/certifications/propath/prince2-project-management.

Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R.C., Mellor, M., Schwaber, K., Sutherland, J. and Thomas, D. 2001. Manifesto for Agile Software. Accessed July 20, 2023. https://agilemanifesto.org/.

Benjamins, R., Barbado, A. and Sierra, D. 2019. “Responsible AI by Design in Practice.” Proceedings of the Human-Centered AI: Trustworthiness of AI Models & Data (HAI) track at AAAI Fall Symposium. Washington, DC.

Brechner, E. 2015. Agile Project Management with Kanban. Redmond, WA: Microsoft Press.

Diaz-Rodriguez, J., Del Ser, J., Coeckelbergh, M., Lopez de Prado, M., Herrera-Viedma, E. and Herrera, F. 2023. “Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics and key requirements to responsible AI systems and regulation.” Information Fusion 99. https://doi.org/10.1016/j.inffus.2023.101896.

European Commission High-Level Expert Group on AI. 2019. Ethical guidelines for trustworthy AI. Brussels: European Commission.

International Project Management Association. 2023. International Project Management Association. Accessed July 20, 2023. https://ipma.world/.

Project Management Institute. 2023. Project Management Institute. Accessed July 20, 2023. https://www.pmi.org/.

Rubin, K.S. 2013. Essential Scrum: A Practical Guide to the Most Popular Agile Process. Boston, MA: Addison-Wesley Professional.

Shore, J., Larsen, D., Kitgaard, G., and Warden, S. 2021. The Art of Agile Development, 2nd edition. Sebastopol, CA: O’Reilly Media, Inc.

UNESCO. 2022. Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO.

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