The Generative AI Moral Compass for a Responsible Future

In just a few days, ChatGPT has garnered worldwide attention and achieved widespread adoption, much like Google Search did over time. However, it is the underlying technology of generative AI that is captivating the global tech community.

In a recent survey of over 2500 leaders, nearly 70% acknowledged that they were exploring new innovations with generative AI. The priorities for developing new use cases in generative AI vary across different businesses, ranging from enhancing the customer experience to ensuring business continuity.

As an increasing number of consumers use generative AI tools in the content creation process, including school essays, musical symphonies, and animated movie characters, a growing debate has emerged regarding the ethical boundaries that businesses, governments, and consumers should establish to promote responsible AI advancement.

While apocalyptic consequences often depicted as the product of AI in sci-fi movies are too far-fetched, there are some emerging concerns that need to be addressed for risk-free AI adoption in the mainstream. In order to prioritize the ethical development of AI, three key areas warrant our attention: eliminating bias, ensuring privacy, and enforcing accountability.

In this context, let’s consider the primary use case of generative AI widely recognized today, i.e., creating content.

Eliminating Bias: Striving for Fairness in AI

AI systems achieve their level of autonomous decision-making through continuous cycles of training with a vast volume of data. Over time, an AI system tends to inherit hidden biases within the data structure if there is no check.

Let us explain with an example. If a generative AI tool that instantly generates stories or poems for literary consumption is trained on excerpts and compositions created by authors with either a left-liberal or right-conservative opinion, the output generated by the AI tool will eventually be aligned with the author’s perceptions.

This will result in dangerous consequences when the work thus produced is used in real-world scenarios, for example, as stories or poems published in books for children.

To eliminate this threat, organizations must strive to train their generative AI tools with more accurate datasets devoid of bias. Achieving this goal requires delving deeper into the training data and physical data models to ensure a balanced representation of diverse thoughts and inferences relevant to a given scenario.

This approach ensures that the generative AI tool considers all aspects of a problem before generating a solution without being predisposed to any specific behavioral bias.

Ensuring Privacy: Safeguarding User Information

The quality of the data used to train generative AI has a significant impact on the content it produces. This may also include confidential data, ranging from information on personal assets and credentials to insights on behavioral or preferential choices.

If the system eventually creates different types of content assets that easily demonstrate traits found within the personal choices of customers, it can be a massive privacy problem. Consumers will become even less willing to share private information as a result of their concern that AI tools may target them in unexpected ways. Ultimately privacy-governing bodies will tighten their strings and put a dent in the growth and scalability of such AI tools.

To avoid this, businesses must ensure that they maintain transparency in the data collected to train their numerous AI models. While developing data models to help predict outcomes from information, businesses need to apply a privacy-by-design principle from the start. This will help keep a check on the acquisition, storage, and use of sensitive personal information by target audiences.

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Another aspect of privacy concerns the use of AI in software development. Recently, the emergence of AI tools like the GitHub Pilot has raised concerns with regards to the security and ethics of the tool. Experts expressed concerns about the use of the tool to create malicious code or code that promotes bias. As such, monitoring the access as well as the output of the tool is going to be a significant way to mitigate these risks.

Enforce Accountability: Safeguarding Against Errors

While making content for different requests, generative AI systems leverage the power of autonomous decision-making to arrive at the right choices for words or any other content asset. However, with such a high degree of machine autonomy, there are possibilities of significant blunders being vetted as authentic content inputs owing to inaccuracies or problems in training relevant data. Using the content thus generated autonomously with erroneous insights can turn disastrous depending on where it is eventually used in real-time.

To counter this threat, every organization must look at how it manages data and insist on organization-wide governance and accountability for data accessed, managed, and leveraged by AI systems.

Care should be taken to ensure that only the most contextually relevant data is used for innovative use cases and developing data models, and that the responses of AI tools are automatically monitored for their accountability. This will ensure a more responsible use of generative AI. In the event of a risk, a proper escalation and remedial matrix will always be available for quick remedies.

The Way Forward: Embracing a Responsible AI Experience

It is estimated that, by 2030, generative AI will become proficient enough to generate 90% of the content in a blockbuster movie, including visuals. When we are riding on a wave of disruption powered by technologies like generative AI, it is crucial to know not just what can be done as well as what should be avoided. This will ensure a safer and enduring AI experience.

What is essential here in this regard is that enterprises looking to leverage generative AI for their needs must not forget to prioritize the above three core concerns. This is where an expert technology partner like Xoriant can help navigate the complexity.

Embarking on a responsible AI journey?