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5 Insights on Using Algorithms in Government Service Delivery

By Guest Writer on March 1, 2023

Algorithms in government bias

It’s easy to be seduced by the power of algorithms to deliver public services. Do you want to target beneficiaries of government programs and services precisely and accurately? Well, there’s an algorithm for that. This is also true for real- time monitoring of resources, personalization of government interactions, fraud and corruption prevention, anticipation (if not outright prediction) of events and behavior, and more.

In such instances, algorithms seem like a magic formula that can crack some of government’s most persistent problems.

However, experience shows that algorithms can be divisive and destructive, be it in the hands of governments, government-affiliated partners, or forces hostile to public-sector actors. Algorithms have been used to sow distrust in public information and government machinery such as elections, and they have been held responsible for perpetuating discrimination in the delivery of services and unfavorably profiling segments of the population. Some have blamed algorithms for a variety of injustices, such as people being denied admission to college or being denied bail by judges who rely on automated systems.

With algorithms, even good intentions can result in unforeseen socially and politically disruptive outcomes. Complicating matters for governments, particularly those in developing countries that are eager to introduce or expand the use of algorithms in the public sector, is the fact that most of the experience and lessons learned so far reflect the reality in developed countries, where there is greater technical, human, institutional, and infrastructural capacity. What’s more, advanced economies have different priorities and policy objectives than developing countries and have a different level of algorithmic maturity.

Algorithms in Government: A Magic Formula or a Divisive Force? presents preliminary observations drawn from a high-level review of two cases, one in Izmir, Turkey, and one in Belgrade, Serbia, as well as an analysis of secondary material. The focus is on data governance-related design and implementation issues specific to developing country governments that are considering algorithmic decision-making services.

5 Key Insights on Algorithms in Government Service Delivery

  1. Algorithmic decision-making is becoming prevalent in the public sector worldwide, and governments in developing countries are increasingly beginning to deploy algorithms to deliver citizen and business services as part of their digital transformation agenda.
  2. Many algorithmic decision-making initiatives in developing countries are still at an early stage, as the case studies in this issue brief suggest. The examples featured in this brief are local and carefully designed, with data governance challenges such as privacy and data security in mind.
  3. Developing countries face several distinct data governance challenges related to the design and implementation of algorithmic decision-making services.
    • Institutions in developing countries have an extreme legitimacy, accountability, and transparency problem.
    • Poor local data means that people in developing countries are inadequately represented in training data.
    • People in developing countries have less experience in interacting with machines and algorithms, and there’s a scarcity of data in local languages to close the cultural gap.
    • Developing countries have had limited involvement in developing standards for fairness, transparency, and accountability in algorithmic decision- making.
    • Developing countries are dependent on international data infrastructure to develop and manage their algorithms.
    • Developing countries deploying algorithmic decision-making are dependent on big tech companies but have little leverage over them.
  4. Opportunities to address these specific data governance challenges are emerging, including:
    • Create regional or other data alliances to tackle relevant data governance challenges.
    • Focus on cases that don’t depend on personal data to deliver relevant services to citizens and businesses.
    • Keep the emphasis on people, both as designers and supervisors of algorithms and as consumers of algorithmic services.
  5. Many additional data governance challenges posed by algorithmic decision-making can be addressed as part of a country’s overall digital transformation agenda. These are not the focus of this issue brief but include themes such as the overall legal/regulatory/enabling environment, infrastructure development, financing, capacity/ skills development, and institutional support.

Written by Prasanna Lal Das and originally published as Algorithms in Government: A Magic Formula or a Divisive Force?

Filed Under: Government
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