Surprisingly Useful AI Article Enhancements
‘Trust’ is an important and recurring keyword in all discussions surrounding Ai. Since Ai is constantly learning and modifying from the information that is fed to it, it is important for stakeholders to be able to trust the source material that it relies on. This is where data transparency comes in. The adoption of Ai largely depends on these two factors – trust and data transparency. In organisations of all sizes, generative Ai is dependent on transparency in data governance, communication and technology policies.
The complication around Ai governance, however, is a consequence of its rapid development. While it is becoming interwoven in all our lives, we are yet to truly comprehend its full potential, as well as its flaws – including the inherent risks of result accuracy, data leaks, unverified data input and underlying biases. Grasping this knowledge can seem like a mammoth challenge but is pertinent to the responsible and ethical adoption of Ai. It is crucial, therefore, for organisations to understand the risks around the use of Ai, to build data transparency and safeguard trust through Ai governance.
Mitigating the risks associated with Ai
The most obvious risk of Ai is that it is trained on the data provided to it. This means that if the source data is corrupted or inaccurate, the resulting data will also be inaccurate. Therefore, it is important for organisations to ensure that source data is transparent, so any inaccuracies can be identified and corrected. Using incorrect or copyrighted data without proper permissions can also go beyond inaccuracies, leading to IP infringement and potential lawsuits.
Another key factor to ensuring data security is accountability. Organisations need to know whom to hold accountable to ensure the accuracy of data and entrust them with the responsibility of maintaining data transparency. Clearly defined roles and responsibilities, communication processes, technology policies and governance can go a long way in ensuring Ai safety and building trust.
Understanding Ai governance
The technological shifts around Ai have posed challenges for its use, but they also come with opportunities. If we can understand and mitigate risks through governance, we can unlock Ai’s full potential. The organisations that understand this can easily surpass competition simply by building good governance around its use.
One of the main factors here would be setting policies and ground rules around the training and operationalisation of Ai. IT professionals and business leaders will have to take responsibility for maintaining these rules and regulations. Once the accountability and policies are established, the next step would be to understand what source data will be used to train Ai models. Where will the data be acquired from? Who will check the data? How can accuracy be maintained? Who will have access to this data and be responsible for ensuring it remains incorrupt?
It is apparent that the need for a human to be guiding Ai is important, especially in, but not limited to, its training. Centralised, user-friendly Ai models make it easier to achieve that, where humans can be the backbone of maintaining Ai accuracy to harness its full capabilities.
Building Trust in Ai
Users can trust Ai systems when they understand how the systems are trained, what source data is used, the origins of the source and how the outcomes are created by Ai. This usually develops from a culture of using a data-first approach within the organisation to make all decisions across the board. When data is the basis of every decision and strategy in an organisation, all employees will have no choice but to develop a data-first approach and mindset.
It is the responsibility of business leaders to understand how algorithms are developed, what models are being used; how Ai systems work, when, how, and why Ai is being used to make decisions that affect individuals; and how customers can most effectively implement Ai solutions. Organisations must continually try to develop their own knowledge on the use of Ai and gain insights into its working, to confidently make data-driven decisions. When users understand how Ai works and trust the data that it is being trained on, they can trust the accuracy and integrity of the output generated by Ai and the decisions made by it. Transparency, therefore, plays a massive role in building and retaining trust in Ai generated decisions.
Embracing ethical Ai
Ai is rapidly transforming the world and is quickly becoming an integral part of our day-to-day lives. It is only natural for this progression to come with its own flaws, but with adequate investment in technology, attention to detail in terms of its governance and a system of checks and transparency around its development, we can ensure that AI reaches its best potential.
This rapid change in the use of Ai brings many new opportunities, but also cultivates a sense of fear within the workforce. Organisations must focus on training their employees, getting their buy-in and building a data-focused mindset, which gives employees control over Ai and not the other way round. An intelligence-augmented workforce, built on a solid foundation of data literacy and training, can ensure that businesses can get the most from Ai systems, and build reliable, trustworthy and ethical Ai-driven decision intelligence that propels their business to new heights.
Philip Madgwick is Regional Vice President at Alteryx Asia & ANZ.
Last Updated on November 21, 2024 by Philip Madgwick
