Four Principles for Integrating AI & Good Governance

Oxford Commission on AI and Good Governance: “Many governments, public agencies and institutions already employ AI in providing public services, the distribution of resources and the delivery of governance goods. In the public sector, AI-enabled governance may afford new efficiencies that have the potential to transform a wide array of public service tasks.
But short-sighted design and use of AI can create new problems, entrench existing inequalities, and calcify and ultimately undermine government organizations.

Frameworks for the procurement and implementation of AI in public service have widely remained undeveloped. Frequently, existing regulations and national laws are no longer fit for purpose to ensure
good behaviour (of either AI or private suppliers) and are ill-equipped to provide guidance on the democratic use of AI.
As technology evolves rapidly, we need rules to guide the use of AI in ways that safeguard democratic values. Under what conditions can AI be put into service for good governance?

We offer a framework for integrating AI with good governance. We believe that with dedicated attention and evidence-based policy research, it should be possible to overcome the combined technical and organizational challenges of successfully integrating AI with good governance. Doing so requires working towards:

Inclusive Design: issues around discrimination and bias of AI in relation to inadequate data sets, exclusion of minorities and under-represented
groups, and the lack of diversity in design.
Informed Procurement: issues around the acquisition and development in relation to due diligence, design and usability specifications and the assessment of risks and benefits.
Purposeful Implementation: issues around the use of AI in relation to interoperability, training needs for public servants, and integration with decision-making processes.
Persistent Accountability: issues around the accountability and transparency of AI in relation to ‘black box’ algorithms, the interpretability and explainability of systems, monitoring and auditing…(More)”