With generative AI going mainstream, enterprises have the responsibility to ensure that they are using this technology ethically and mitigating potential harm
Artificial Intelligence (AI) and the rapid leaps it has made in recent times on the generative AI front, has emerged as a powerful tool for creating and supercharging various aspects of businesses, from content generation to product design to general decision making. However, the adoption of generative AI requires that we temper our excitement with a careful and sensibly guided approach to avoid potential pitfalls, unintentional consequences and to maximize its benefits, not just to the business but also the society.
With the benefit of having a ring side view of the developments at SAS, it is safe to say that it is critical that emerging tech adoption needs careful balancing to avoid the risks of spreading misinformation, perpetuating historical injustices and biases.
1. Understanding the Purpose, Potential of AI, and the value of transparency:
A business using generative AI technology in an enterprise setting is different from consumers using it for private, individual use. And there is a minefield of legal, financial, and ethical implications if the output generated is inaccurate, biased, or offensive. Some organizations have formed ethics advisory councils to mitigate these threats.
Head – Marketing & Communications
SAS India
Either way, it is imperative to define clear business goals and align generative AI initiatives accordingly. A use case-based approach will help define and narrow down specific areas where generative AI can add value and focus efforts on those areas to achieve tangible results. Along the way, is important to openly communicate the intended use, potential risks and decisions made which may include:
Should the organization indicate to users/consumers when it is AI created content?
What are the legal, reputational, financial considerations when using AI generated content and are there sufficient guardrails?
Can solution results be reliably reproduced?
2.Data Quality and Ethics:
Data is the fuel for Generative AI. Ensuring data sets accurately reflect real world populations being analysed is topic that is gaining traction and rightfully so to avoid propagating bias. Another important angle is organizations need to be able to train AI models on their own data or data collected with clear customer consent while ensuring the quality, reliability, and ethical use of data is critical. Data acquisition and use should not occur at the expense of consumer privacy and a strong data lineage is important to ensuring models are accurate and dependable.
As responsible player in the space of AI, SAS has developed frameworks for how organizations can start, mature, and expand these practices, which provide clear principles to guide the development of trustworthy AI and comply with privacy regulations.
3. Human-In-The-Loop Approach:
Generative AI can automate various tasks, while it is still in its infancy, it is essential to adopt a human-in-the-loop approach where human expertise complements AI capabilities. Human oversight is crucial to validate and refine the outputs generated by generative AI models. Continuous feedback loops and human intervention can help improve the accuracy, relevance, and ethical aspects of the generated content or output. Broadly speaking, generative AI should be leveraged to augment human capabilities and empower communities, not replace, or displace them. AI needs to promote human well-being and equity.
4. Risk Management and Interpretability:
Generative AI models may produce outputs that are often difficult to interpret or explain. Organizations should invest in methods to interpret and validate the outputs, particularly in regulated industries or sensitive domains. It is vital to understand the potential risks associated with generative AI, such as unintended biases or misleading results, and develop mitigation strategies accordingly.
Look at trustworthy AI platforms such as Viya that have “explainability” features to help mitigate this challenge by providing additional reasons and context for why an AI model produced what it did. Transparent and interpretable generative AI models fosters trust and confidence in their applications.
5. Incremental Implementation and Continuous Learning:
Adopting generative AI enterprises should follow an incremental approach, starting with smaller projects or use cases. This allows organizations to experiment, learn from initial projects, assess the impact of the technology, and iterate based on feedback. Continuous learning and improvement are essential to refine the generative AI models over time, demonstrate value and gain confidence in the system.
In summary, with generative AI going mainstream, enterprises have the responsibility to ensure that they are using this technology ethically and mitigating potential harm. Considering where we are on this journey, organisations will benefit from a pragmatic, human-first approach and committing to a strict ethical framework with sufficient guardrails.
