In a few short months, generative AI has become the new talk of the town, and rightly so. Beyond the hype, generative AI is a groundbreaking innovation that unlocks novel capabilities as it evolves rapidly into the enterprise world. However, with every new technology, questions and debates will inevitably ensue around ensuring its ethical usage.
The generative AI systems function from a dataset to create new, original content similar to the input it receives. The technology can be leveraged for diverse tasks like writing software code, developing content, facilitating drug development, and targeted marketing. Still, it can also be misused for scams, fraud, spreading disinformation, forging identities, and more.
According to Gartner, by 2025, generative AI will account for 10 percent of all data produced, up from less than one percent today. With such a rapid evolutionary phase, generative AI will unlock new capabilities we previously did not have. But how safe is generative AI, and can industries rely on it?
The bias and privacy concerns of generative AI are not limited to some professions. From corrupting bank systems to raising questions about the role of human reporters and editors in the news process, AI systems can be easily disrupted and misused. For instance, the ability to automate the news creation process and dissemination also means we are disregarding journalistic ethics and ideals. The AI systems can generate realistic and convincing images, movies, and sounds that are enough to propagate fake news, spread hate or make deep fake films.
Additionally, bias in generative AI can lead to a series of discriminatory content, which can perpetuate social and cultural stereotypes, exacerbate societal disparities, and reinforce systemic prejudices. Hence, it becomes essential to track and address the potential for bias in generative AI and take steps to reduce it. Regular reviewing and testing of the AI systems for tracking bias and privacy issues are necessary to ensure that data is diverse and representative.
Here verifying the system's safety at all levels becomes a bigger challenge because we must understand what the system is trying to do. Apart from this, maintaining transparency is critical in generative AI since it is difficult to identify the ownership of the content AI obtains, and a lack of transparency can add to biases and other ethical concerns. This implies that the data extracted should include examples from a wide range of demographics, including gender, age, race, socioeconomic status, etc.
Another ethical challenge surrounding the prospects of generative AI is the issue of accountability since the tool is often used to generate publicly shared content. However, if that content is harmful or offensive to a person or community, then it becomes difficult to hold the creators of the generative AI system accountable. This becomes concerning when the generative AI system has been structured to function autonomously without human intervention.
While we know generative AI tools can drastically reduce the capital and time needed for content creation and also boost productivity, it is essential to have fairness and accountability at various levels.
Establishing clear guidelines and regulations for the systematic use of generative AI is critical. This includes relevant policies around the type of content that can be generated using AI and how that content is shared. Additionally, it may become essential to include a human oversight component to ensure that any generated content is appropriate and ethical.
Also Read: Are banks ready to take on generative AI?
Additionally, when it comes to ownership, it is critical to establish clear principles and guidelines around who owns the content developed by the AI systems. The solution may involve setting new copyright laws or licensing agreements, especially for generative AI-generated content. Additionally, there can be a case when it becomes necessary to accommodate a mechanism for attributing credit to the platforms from where the information was sourced.
It's safe to say generative AI has the potential to revolutionise content creation and take things to the next level. However, right now, technology is too young to make any notable impact or change a major trend in business. Still, factors like responsibility, transparency, auditability, incorruptibility, and predictability should be considered while using a generative AI tool. Organisations today must implement innovation strategies and stay updated with the latest developments in the diverse field to ensure full transparency.
The writer is founder and CEO of coto.
The thoughts and opinions shared here are of the author.
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