Show your work! Why explainable AI is crucial for sustainable adoption

Much has been said about the Middle East’s accelerated shift to artificial intelligence (AI) thanks to a massive migration to the cloud in recent years, accelerated by remote working and the need for agility in organizations amid bottlenecks. of COVID-19. A future has been sketched out if not set in stone by commentators who are rightly certain that there is no turning back.

We always went here. As early as 2017, analyst firms like Deloitte and PwC’s Strategy & were chronicling CCG government digital transformation programs. The region is pinning its economic hopes on AI and related technologies. But trust is important. Remember in math class, when a single number given as an answer to a complex problem was never enough? Images of teachers looking sternly over their glasses come to mind. “You have to show your work,” they said.

Democratization of AI

In the world of artificial intelligence, this conversation is called “the democratization of AI,” and we need to keep it in mind. If AI is to be our future partner in innovation, we must trust it. And to trust it, we have to be frank about its inner workings.

Yet companies from the Levant to North Africa are ready to let advanced algorithms make decisions on their behalf. So-called “black box AI” can lead to bad decisions, with little post-mortem capacity that would allow stakeholders to determine points of failure. To generate trust in such systems, we need to expose the path between data and actionable information or the action itself, as is the case in fully automated architectures.

Transparency, accountability

Businesses in the Middle East are subject to increasing regulatory constraints. They can’t afford large-scale blunders, like Apple suffered with its credit card. An Apple-level business may be able to recover from these mistakes, but a growing startup, like those that make up a significant part of Middle Eastern economies, is unlikely to do so. also rids easily.

The regional ISP sector, hungry for growth opportunities, could face serious problems if regulators cannot challenge decisions made due to black box AI. Denied loans, variable credit limits and even fees have to be penetrable.

Another industry in growth mode, and also subject to scrutiny by Gulf regulators, is healthcare. Healthcare providers in the region have already started incorporating AI into their strategies, but it’s not hard to imagine why transparency will be important in making smart technology a mainstay of healthcare. Human analysis of results is essential for accuracy. Indeed, many machine learning models require expert human feedback to refine accuracy and become viable in production environments.

Peel the onion

Think of the opposite of the black box as “explainable AI” or white box AI. If we can answer questions about how an AI system came to its conclusions, we can conduct a vital debate about the direction certain technologies are taking and how those paths can be redirected to more positive and reliable results.

Also consider the governance angle. Responsible use of AI leads to good business for private enterprise. This leads to a more desirable social impact for governments. The ability to deliver noticeable value, free from errors, biases, or other negative elements is certainly the goal of AI. Given the suspicions that automation faces globally for its potential to supplant the human workforce, it’s hard to imagine an increase in AI adoption that won’t come with it. intensification of regulatory requirements. Under such circumstances, black box systems will wither on the vine.

Thrive sustainably

Exposing the algorithm as part of the results dashboard is a natural next step for AI solutions. Measures such as numerical weighting values ​​applied to data to indicate the relative importance of one observation over another should be displayed in full. Mathematical models are constantly improving and user interfaces are continually being improved to provide end users with a broader view of data processing.

Collaboration between companies will also be vital. Open data platforms that allow refining models based on the experience and information gathering of different contributors will lead to greater democratization and more precise results. Ultimately, we’ll end up with a richer information ecosystem, more informed decision-makers, greater confidence in AI, and more sustainably prosperous societies.

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