Technology often advances faster than regulations, leaving organizations to instill ethical practices haphazardly. How can they implement AI with ethics-centricity?
According to IDC, AI adoption in the Asia Pacific & Japan region is expected to skyrocket by 2028. Organizations from tech giants to governments are already working on new AI frameworks to drive their ethical and Responsible AI development in the region.
As more organizations in Asia embrace AI, there is a need to reassess current AI and data strategies to maintain stakeholder trust and mitigate the potential risks of AI misuse.
How should we cultivate trustworthy, adaptable, and flexible AI approaches to put AI to work more effectively and create new value?
Getting the AI foundation right
Data drives AI and serves as the foundation upon which algorithms are trained and developed. Without the right guardrails, biases can creep into algorithms during the training process. How?
- Machines can unintentionally replicate biases present in the data fed into their AI models. Since the output of AI depends heavily on the quality of its input, it is crucial to maintain comprehensive datasets and ensure aggregation of data from diverse sources into a unified, reliable repository. This approach is pivotal for achieving data accuracy.
- When AI systems operate with limited transparency, there is also the risk that the data that users share with businesses could be used for training and appear in front of another user. This data can include personal information, as well as sensitive information such as medical records. The collection and processing of such data can raise concerns around data privacy and security, undermining organizational integrity.
- Since AI is progressing too quickly for policymakers to keep pace, the onus falls on creators and users to help ensure its ethical use based on the principles of openness, flexibility, and responsibility.
So, organizations leveraging AI have a responsibility to incorporate Responsible AI to address risks.
AI cannot command unchecked freedom
One area of AI that holds a lot of potential is specialized AI, which includes models trained for a specific business task or process. As explained by Forbes, Specialized AI can sift through workforce insights, such as from interaction data (user clicks and keystrokes), to automatically discover how employees complete certain tasks and provide insights for refining workflows.
Today, new context grounding capabilities are helping organizations improve the accuracy of generative AI models by furnishing them with a foundation of business context through retrieval-augmented generation. Such a system can extract information from company-specific datasets such as internal policies to create more insightful responses.
Embracing an open approach allows organizations to harness the strengths of various AI models and capabilities. The true power of AI lies in integration: linking individual AI components with other systems in the larger mosaic of digital transformation. However, while organizations design technology ecosystems with AI in mind, it is still crucial to ensure that AI operates within appropriate boundaries and does not have unchecked freedom:
⟩ Ultimately, AI should work for workers, not against them.
⟩ The system should be able to adapt to users’ needs, and not vice versa.
⟩ When an organization has no choice but to choose an off-the-shelf model that does not quite meet their use case, accidents can happen.
⟩ Accuracy is everything, particularly when AI is tasked to make decisions that must adhere to compliance standards or directly influence users.
Therefore, organizations can leverage the strengths of various models and ensure that these align with their organizations’ unique requirements, while prioritizing users, trust and integrity.
New age of AI risks, rewards
Coupled with automation, AI can dramatically speed up processes, improve decisions, and free people from a range of repetitive tasks. However, as with any transformative technology, we must know how to manage AI well to maximize its benefits.
Human oversight and intervention will still play a critical role in mitigating AI risks and liabilities. We must focus on refining the synergy between humans and software workers, and optimizing workflow integration to realize the potential of human-in-the-loop systems.
The democratization of technology provides a pathway to drive the continuous development needed to make the human-machine dynamic work. Another factor is the training and education of people on the ethical implications of AI to empowers them to take control of integration and decision-making processes. This will facilitate industries’ transition towards a more purpose-driven approach to innovation.
By providing everyone with accessible tools to understand and utilize AI appropriately, we are empowering humans to realize AI’s full potential, and vice versa.
