Ever Evolving Data Governance
As we train and grow Large Language Models, it is important to consider not only how we gather and store data, but also how we govern it. The dynamics of Data Governance are evolving with the introduction of advanced technologies such as AI and machine learning, particularly when it comes to language models.
?? Data Integrity The value of the insight we derive is intrinsically tied to the calibre of the data fed into these models. What defines 'quality,' though? Is it the sheer volume of data, or does it also include the precision and richness of the information?
Quality data should be abundant, diverse, free from bias, and mirrors real-life situations.
??? Data Oversight: Effective governance lays the groundwork for establishing both trust and responsibility, aspects that are growing more critical as language algorithms increasingly influence our choices.
?? Ethical Considerations: As these language algorithms advance, the spotlight is increasingly on ethical concerns like inherent biases and discrimination. While the algorithm may be neutral, any historical data used for training could introduce prejudices. For instance, a model based on outdated or biased information could inadvertently give preference to specific outcomes.
?? The Interconnected Nature: Quality, Governance, and Ethics form a complex, interconnected web. Solid governance practices contribute to maintaining high-quality data, which in turn, shapes the ethical boundaries within which the data operates.
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It's a perpetual cycle that demands our shared due diligence.
? Your Thoughts Wanted: Navigating this evolving ecosystem, I'm curious about your opinions and approaches:
How can we innovate and adapt governance structures to be both receptive to new advancements and rigorous enough to maintain corporate safeguards?
What steps should we be taking to verify the reliability of data deployed in machine learning algorithms?
How do we avoid ethical pitfalls, especially when decisions are made by machines?
I'd love to hear your thoughts and perspectives on these questions. In an ever-evolving AI-driven society, the future of data governance is a collective responsibility.
Chief Marketing Officer | Product MVP Expert | Cyber Security Enthusiast | @ GITEX DUBAI in October
11 个月Robin, thanks for sharing!
MD at Master Data Management - 20 years delivery of Data Governance, Data Quality and MDM solutions
1 年Great post Robin M.. This has become a bit of a theme in my blog posts over the last 6 months. I am seeing enterprises rush into AI and ML as the shiny new thing that is going to solve all of their problems without any real investment in these issues. It would certainly worry me if computer "black boxes" were running the planet with absolutely no oversight! Would love to hear your thoughts on some of my posts - and ideas for future themes... https://blog.masterdata.co.za/category/data-analytics/artificial-intelligence/ https://blog.masterdata.co.za/category/data-analytics/ethics/
Data Strategy | Data Governance | Data Quality
1 年A good set of questions in the current landscape of organisations looking to tap into the benefits of LLMs. One of the key areas LLMs or generative AI needs to incorporate that you've not touched upon is the fact that generative AI will create vast amounts of data and this needs to be owned managed and governed. This responsibility will lie with the owners of the AI models. This should also include considerations of data ethics. From a data input perspective the old adage crap in, crap out applies so these models should be fed data which is of a good quality that's curated or governed.