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Artificial intelligence without digital discrimination

  • 7 mths ago

As the civil service continues to tackle coronavirus – launching new services, transforming existing ones, and using data to understand this huge and novel threat – machine learning (ML) technologies could prove invaluable. But first, we humans must learn how to avoid the risks inherent in these machines.

Deployed intelligently, ML could dramatically improve the efficiency of civil service business processes, the quality of decision-making, and the government’s ability to tackle threats such as COVID-19. And in an era of rapid, disruptive change and fast-rising public expectations, it’s more important than ever that public services meet citizens’ needs. Poorly designed ML systems could lead to discriminatory outcomes, weaken case management and regulatory frameworks, infringe on citizens’ data rights, and undermine accountability and transparency. So civil servants are – rightly – proceeding with caution: the keys to progress lie in understanding the technology’s characteristics, then creating the frameworks and selecting the applications that will realize ML’s potential whilst addressing its weaknesses.

In essence, ML systems comprise algorithms that, supplied with large datasets, ‘learn’ how to perform a set task by identifying patterns, connections and common indicators. And there are many applications within the government’s work to address COVID-19. By combing through data on coronavirus test results, population mobility data from smartphones and social media feeds, for example, they could forecast where new outbreaks of the virus are likely to appear. And by examining government records on benefits claims, company liquidations, economic stimulus applications and VAT returns, they could identify the regions and industries suffering the greatest economic impact – informing the design and targeting of business support programs.

Continue reading: https://www.civilserviceworld.com/professions/article/artificial-intelligence-without-digital-discrimination

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