generative AI, GenAI

The artificial intelligence revolution has shaken virtually every industry in the world, but like every paradigm shift, it has been met with hesitance and fear over the technology’s potential consequences. Advocates are beginning to call for increased regulation and governance of AI technology as this will create a landscape where innovators can better thrive.

Challenges in Artificial Intelligence Adoption

Although artificial intelligence has enormous potential as a tool for productivity, it does have some shortcomings. Critics have pointed out several challenges of AI use that could prove to be hindrances for adoption, including:

  • Bias and Misinformation: At this time, artificial intelligence — particularly large language models — is entirely dependent on the data it is fed during the training process. Because of this, if the data used for training is biased or inaccurate, the model can inadvertently perpetuate these biases or misinformation. For example, if a model in the legal space is fed sentencing data unfairly skewed against people of color, the output of that model will show the same racial bias.
  • Lack of Algorithmic Transparency: Another prominent issue with many AI models is a lack of algorithmic transparency. Because of a fear of security concerns or IP theft, many AI developers have kept the underlying technology for their models secretive. And for open-source models, it is still difficult for the average user to understand the AI’s underpinnings. Thus, many users do not understand how the models come to their decisions, creating a situation where they essentially put blind faith into something they do not entirely comprehend.
  • Data Privacy and Security: Many critics have pointed out the data security concerns that arise from AI models reusing user data for training purposes. Users should pay close attention to the terms of service to understand how the model uses their data and inputs, and never input confidential or sensitive data — such as personal information, medical information, or legal information — as this information could end up inadvertently revealed to other users.
  • Plagiarism and IP Infringement: Similarly, because AI models learn from the information put into them by users, there have been instances of accidental plagiarism and IP infringement. Since AI models do not create original thoughts but synthesize them based on pre-existing information, it is possible — if not likely — that these models are replicating another person’s thoughts.

How Regulation and Governance Can Provide a Safer Landscape for AI Growth

Legal standards and regulatory frameworks can be created by governments to help address these concerns, holding developers and users accountable for issues like data privacy, algorithmic accountability and liability and providing a legal basis for responsible AI development and deployment. Some jurisdictions — like the European Union — have already begun to develop and implement regulations that ensure the safety of users and the public while also encouraging the responsible growth and development of the technology.

However, even beyond the minimum requirements established by law, an ethical framework for accountability, fairness and transparency in implementation should be developed. By establishing internal compliance standards even more stringent than the regulations that govern the technology, organizations can inspire greater trust from their stakeholders in integrating AI.

Another step that must be taken to ensure the ethical adoption of AI is an increase in international collaboration. Greater global cooperation among nations and organizations will allow AI regulations, standards, and policies to be much more harmonious. In addition to facilitating the flow of ideas between innovators on an international scale, this collaboration will also foster consistency and reduce the potential for conflicts in applying AI technologies across borders.

Many in the AI space underestimate the community’s power in identifying AI’s shortcomings. After all, users have the most encounters with the output of these models, so they are an invaluable resource in flagging any potential biases, inaccuracies, or plagiarism in responses, allowing developers to find solutions to these challenges more quickly. 

Finally, when put in place, these standards should not be considered rigid, as artificial intelligence is still a developing technology. Continuous assessment and adaptation will be necessary not only to address new challenges that may arise with the technology’s development but also to encourage the growth of new capabilities to fulfill emerging societal needs.

As with any regulation of innovation, some will argue that the creation of laws and compliance standards for artificial intelligence will limit the development of the technology. However, innovators in the field know that increased regulatory clarity will create a framework where users are safer and developers can responsibly innovate and push the industry forward.