Just because we can’t (yet) trust generative AI doesn’t mean we should fear it

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Although the release of ChatGPT sparked much discussion about the game-changing impact of generative AI on technology, some of the shortcomings of the technology received equal attention. Indeed, there have been heated debates about the potentially dangerous impact of generative AI on society, its conceivable negative applications, and the significant ethical concerns surrounding its development.

But from the perspective of IT and software development – ​​where many predict that generative AI will have the most telling impact in the future – one question, in particular, keeps coming up: how much can companies really trust this technology to handle their critical and creative tasks?

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The answer, at least for now, is not much. The technology is too riddled with inaccuracies, has serious reliability issues, and lacks real-world context for businesses to rely on it completely. There are also very justified concerns about its security vulnerabilities, namely how malicious actors use the technology to produce and distribute misleading deepfake content.


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All of these concerns certainly have companies wondering if they can actually ensure responsible use of generative AI. But neither should they instill fear in them. Of course, companies must always balance caution against the endless possibilities of technology. But business decision makers – and tech professionals in particular – should already be accustomed to acting responsibly when they receive new innovations that promise to disrupt their entire industry.

Let’s break down why.

Learn from past innovations

Generative AI is not the first technology to be met with fear and skepticism. Even cloud computing, which has been nothing short of a saving grace since the start of the remote working revolution, has raised alarm bells among business leaders over concerns about security, privacy and reliability of data. Many organizations have actually been reluctant to adopt cloud solutions for fear of unauthorized access, data breaches, and possible service disruptions.

Over time, however, as cloud providers improved security measures, implemented robust data protection protocols, and demonstrated high reliability, organizations gradually adopted it.

Another example is open source software (OSS). Initially, it was feared that it lacked quality, security, and support compared to proprietary alternatives. Skepticism persisted due to fear of unregulated code changes and a perceived lack of accountability. But the open source movement has gained momentum, leading to the development of highly reliable and widely adopted projects such as Linux, Apache, and MySQL. Today, open source software is ubiquitous in IT fields, offering cost-effective solutions, rapid innovation, and community support.

In other words, after an initial moment of caution, companies embraced and adopted these technologies.

Meeting the Unique Challenges of Generative AI

This is not to downplay people’s concerns about generative AI. There is, after all, a long list of unique – and justified – concerns surrounding the technology. For example, there are fairness and bias issues that need to be addressed before companies can truly trust it. Generative AI models learn from existing data, which means they can inadvertently perpetuate biases and unfair practices present in the training dataset. These biases, in turn, can lead to discriminatory or biased results.

In fact, when our recent survey of 400 CIOs and CTOs about their adoption and views on generative AI asked these leaders about their ethical concerns, “ensuring fairness and avoiding bias” was the most important ethical consideration. which they cited.

Inaccuracies or subtle “hallucinations” are another threat. These are not colossal errors, but they are errors nonetheless. For example, when I recently asked ChatGPT to tell me more about my business, it incorrectly named three specific businesses as past customers.

These are certainly concerns that need to be addressed. But if you dig deeper, you’ll also find some that are perhaps exaggerated, like those that speculate that these AI-powered innovations will replace human talent. All you need to do is do a quick Google search to see the headlines on the 10 most risky jobs or why AI worker anxiety is justified. Usually, its impact on software development is a particularly hot topic.

But if you ask IT pros, that’s really not a problem. Job loss actually ranked last among ethical considerations for CIOs and CTOs in the aforementioned survey. Additionally, an overwhelming 88% of respondents said they believe generative AI cannot replace software developers, and half said they believe it will increase in makes the strategic importance of IT managers.

Cracking the code for the future of generative AI

Companies need to recognize the need to approach generative AI with caution, just as they have had to do with other emerging technologies. But they can do it while celebrating the transformative potential it has to offer to drive progress in the IT industry and beyond. The reality is that technology is already reshaping the IT and software development spaces, and businesses will never be able to stop it.

And they shouldn’t want to shut it down, given its promise to empower their top tech talent and improve software quality. These are abilities they should not fear. At the same time, these are abilities they can’t fully appreciate until they address generative AI issues. Only by doing this will they maximize the power of generative AI to support IT and software development, improve efficiency, and create more advanced software solutions.

Natalie Kaminski is co-founder and CEO of an IT development company jet rockets.


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