Ensuring Data Accuracy for AI as a Service

Organizations must take proactive steps to ensure their data accuracy when using Artificial Intelligence as a Service (AIaaS). Learn five areas where AI can have the greatest impact on effective data management.

Ensuring Data Accuracy for AI as a Service

Organizations must take proactive steps to guarantee that their data is accurate when using AI as a service. Technology alone cannot replace good data management processes, such as actively tackling data quality, understanding roles and responsibilities, creating data supply chains, and establishing common definitions of key terms. AI is a valuable resource that can significantly improve both productivity and the value that companies derive from their data. Here are five areas where AI can have the most significant impact on effective data management in an organization.

Data is the fuel that drives AI, so it is essential to make sure that data sets are accurate, complete, and reliable. Poor data quality can lead to inaccurate AI models, which can have serious consequences for your business. To help ensure data integrity, set up a data management framework that includes data quality controls, data lineage tracking, and data access controls. Invest in data cleaning and normalization tools to make sure that your data is consistent and error-free. Leaders must guide their organizations towards defining and establishing the metrics that best align AI with the company's values and objectives.

CEOs must make clear precisely what the company's objectives and values are in different contexts, ask teams to articulate values in the context of AI, and encourage a collaborative process when selecting metrics. Following employee concerns about AI projects for the defense industry, Google developed a broad set of principles to define responsible AI and bias, and then backed it up with tools and training for employees. A technical training module on equity has helped more than 21,000 employees learn about the ways in which biases can appear in training data and has helped them to master techniques for identifying and mitigating them. As mentioned, almost all AI applications require large amounts of computing resources, especially when training with large data sets. Organizations need a clear and practical framework for how to use generative AI and align their generative AI objectives with the “to-dos” of their companies, including how generative AI will affect sales, marketing, commerce, services and IT jobs. Data is locked in silos, inaccessible, poorly structured and not organized in such a way as to be used as the fuel that makes AI work.

Data classification and extraction is a broad area, and it has grown even more as more media have been digitized and social networks have increasingly focused on images and video. A mature ethical AI practice puts its principles or values into practice through the responsible development and deployment of products. With existing and emerging regulations such as GDPR or CCPA, all leaders have had to reexamine how their organizations use customer data and interpret new regulatory issues such as the right to be evaluated by a person, the right to be forgotten or automatic profiling. Ensure that your AI models are validated and revalidated to ensure that they work as intended and do not discriminate unlawfully. Numerous cases of bias, discrimination, and violations of AI privacy have already appeared in the news. As Harvard Business Review perfectly points out, you're not ready for AI unless your data is all grouped together.

In sectors where building trust is one of the top priorities such as finance or healthcare it is important that people participate in decision-making with the help of data-based information that can be provided by an AI model to build trust and maintain transparency.