Artificial intelligence collaborations for society

Data science and artificial intelligence (AI) hold great promise for public sector organisations to improve services for citizens. But a great challenge remains: governments do not have sufficient knowledge or resources to integrate AI into public services on their own.

Harnessing the potential of AI for society requires collaboration between universities and the public and private sectors. This collaborative approach is already the norm in applied AI centres of excellence around the world.

But despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In our research in Philosophical Transactions, we show the opportunities and challenges of AI for the public sector and propose a series of strategies to successfully manage cross-sector collaborations.

Artificial intelligence holds great promise for public sector organisations

Management challenges and opportunities of AI

While the challenges of collaboration across private sector organisations has been widely researched, much less attention has been paid to the difficulties of working across the public, private and non-profit sectors.

The first issue that can hinder collaboration success is the different environments surrounding public and private organisations. While public organisations are accountable to their service users and the public at large, private organisations are responsible to their shareholders. This can lead to clashes when aligning the interests of the various stakeholders.

Public-sector procurement of AI-based technologies presents challenges and raises questions of accountability. Who is responsible for a decision taken by an algorithm when it has an adverse impact on someone's life? Or for the potential criminal misuse of AI and data?

Who is responsible for a decision taken by an algorithm when it has an adverse impact on someone's life?

Another central challenge of potential AI collaborations between the public and private sectors is the divergent approaches to managing risk: the political risks of governments are not easily reconciled with the market risks of business organisations.

In particular, managers of collaborative ventures may find it difficult to deliver public value for money while also maximising profits to satisfy shareholders. As future collaborations related to AI take place, there is always the inherent risk that the data used have been gamed or sabotaged to serve the opportunism of a self-interested actor.

Additional challenges related to cross-sector collaborations around AI relate to skills and data. There is a significant skills gap in AI between the public sector and business and universities. Public organisations lack individuals who possess knowledge and skills in AI and require technical assistance and training

Developing the digital skills needed for public sector use of AI is not a quick process. More funding is needed for PhD students in machine learning to overcome this shortfall.

7 factors for success in AI collaborations

Our research findings point to the following seven managerial strategies that can contribute to the success of AI collaborations between the public and private sector:

1. Facilitative leadership

In contrast to the classic idea of hierarchical leaders who impose their views on followers by relying on a position of power, facilitative leadership endorses respect and positive relationships among team members, constructive conflict resolution and candid expression of thoughts and attitudes.

Our analysis concludes that leaders of collaborations should promote broad and active participation, ensure broad influence and control, facilitate productive group dynamics and extend the scope of the process. Facilitative leadership is imperative to collaboration, especially since incentives to participate can be low and resources may often be asymmetrically distributed.

2. Shared objectives

Even if all the parties in a collaboration are highly aligned with the main objective of the alliance, there may be differences between the objectives of each organisation. To ensure success, it is important that objectives be aligned because they act as a guide for decision-making and a reference standard for evaluating success.

3. Gathering and sharing knowledge

Management activities should focus on institutional capacity-building for joint action, such as the creation of common standards for the collection and processing of data. On a technical level, organisations are challenged by the way they manage their collaborative data networks to create data-sharing across jurisdictions. Formulating common standards for data collection and improving data-sharing procedures is crucial to ensure successful collaborations.

4. Communication

A communication strategy can have a direct impact on the management of a collaboration. When the collaboration is visibly producing tangible outcomes, stakeholders are more willing to invest time, energy and resources. This happens by showing the value of joint actions through quick wins.

5. Socialising

When managers make the impact of collaboration efforts transparent for key players to work together, collaboration improves. Transparent results and indicators can facilitate more ideas and reforms across all levels of the collaboration when it may be more difficult to implement a top-down idea in decentralised settings.

6. Expertise

Hiring tech-savvy network managers and shepherding the efforts of field experts within the network can induce trust on the basis of their competencies and improve service quality. The appropriate use of relevant technology can significantly improve performance in data quality, data integration, data analysis and visualisation.

7. Sense-making

In cross-sector collaborations, relationships can be asymmetric: one partner may need more cooperation than the other. In these scenarios of unbalanced reciprocity, it is effective to create strategies for trust-building and persuasion. A collaboration manager must, then, make sense of this situational need and stimulate the network structure by encouraging actors to engage themselves.