The implementation of AI, particularly generative AI, raises concerns about trust. At Transform 2023, Hillary Ashton, CPO at Teradata, discussed the importance of data governance, privacy, and transparency in building trust in AI-driven operations.
Generative AI has brought advanced analytics to the forefront of boardroom discussions, highlighting its untapped potential. However, trust must be carefully considered. Questions arise about protected data and personally identifiable information (PII), especially with large language models and generative AI.
The role of trust in generative AI adoption
For enterprises to be trustworthy, they must prioritize foundational data quality and separate PII data. This may sound basic, but many struggle to implement it effectively. It requires the right technology, people, and processes.
Establishing robust data governance frameworks is crucial. Data should be treated as a product, and clean, non-PII data should be made available to users. Regulatory compliance is essential, and organizations should be transparent about their use of generative AI and its impact on data privacy. Safeguarding intellectual property (IP) and proprietary information is also vital when collaborating with third-party vendors or utilizing large language models (LLMs).
Understanding how advanced analytics are used is key. Organizations must identify their PII and IP and protect them accordingly. For example, prompts written by senior data scientists are considered IP and should not be freely shared with competitors. This prompts the need for prompt protection and IP practices.
Even the structure of data is proprietary. Banks, for instance, wouldn’t want to give their competitors an advantage by sharing their data structure with a vendor using an LLM, regardless of how sanitized it is.
Additionally, organizations should consider regulatory compliance, be transparent with customers about their use of generative AI, and ensure reliability and accuracy of model outcomes through regular evaluation and proactive measures.
So where to get started?
According to Ashton, getting started involves working backward from desired outcomes. There are two main areas to focus on: maintaining leadership advantage with advanced analytics and addressing challenges that competitors have already overcome.
Data governance and respecting the sovereignty of IP and PII data are crucial considerations. Scaling these practices is the next step, followed by managing models in production and addressing underperformance.
Lastly, it’s important to evaluate the return on investment and price performance. While LLMs and generative AI are exciting, the cost may become prohibitive for low-value use cases. It’s essential to avoid using these technologies just for the sake of novelty.
The emergence of Generative AI technologies, enabled by advances in Artificial Intelligence (AI) and Machine Learning (ML), has revolutionized the way organizations across industries go about their day-to-day operations. Unfortunately, there has been commensurate increase in the challenges surrounding data management, governance, transparency, and trust, making it difficult for organizations to confidently leverage the power of Generative AI. This article will explore the existing challenges and provide recommendations for navigating the risks associated with Generative AI.
Generative AI carries with it the complexities of large-scale data management, governance, transparency, and trust. Data governance models govern how organizations handle and secure the large amounts of data passed through machines for predictive modeling and analysis. It is important for organizations to understand and control how their data is used from collection to storage, offering an additional layer of resistance against potential data misuse. At the same time, transparency into system operations and trust that AI models will deliver accurate data and results are necessary for any organization to achieve the desired impact of Generative AI.
Organizations have to address the complexities of data governance, transparency, and trust. Organizations must guarantee privacy and security protocols to maintain data integrity and prevent potential data leakage. Data transparency is also important; stakeholders must be able to understand how their data is being used and how the data is driving decisions. Additionally, organizations need to be cognizant of potential biases in data or models that may lead to unfair or unchecked outcomes, as well as establish procedures to ensure data accuracy and reliability.
Moreover, due to Generative AI’s dependence on data, organizations must invest in quality data sources that ensure accurate information. Models and algorithms must be thoroughly examined for accuracy and robustness before being deployed. It is equally important to assess the privacy and ethical implications of data-driven systems, such as the potential for automated decision-making, profiling, and discrimination.
In order to navigate the complexities of data management, governance, transparency, and trust, organizations must prioritize data security and compliance. Organizations should invest in and implement comprehensive data security measures to ensure data safety and mitigate potential risks to sensitive data. Data teams should actively monitor and audit data stored, collected, and used by AI systems to ensure that data requirements are continuously met. Organizations should also strive to increase transparency, such as providing visibility into the process of data governance, training employees and stakeholders on how to use the data responsibly, and having regular review processes to ensure data accuracy.
In conclusion, data management, governance, transparency, and trust remain serious challenges faced by organizations leveraging Generative AI today. Organizations must address these issues proactively to ensure the trustworthiness and accuracy of Generative AI solutions. In order to ensure data safety and leverage the possibilities of Generative AI, organizations should prioritize investment in data security, transparency, and compliance measures.
