Data Privacy Day: Why data quality is the foundation of trustworthy AI
Data Privacy Day is January 28.
SAS VP of Data Ethics, Reggie Townsend :
I see the term resilience in a lot of business literature these days. Intuitively, it makes sense. After a pandemic, global supply chain disruptions and resulting economic fragility, executives understandably consider adaptability, durability and how best to operate with a strength of character – all attributes that define resilience.
Many factors contribute to resilience, but data quality is among the most important. Data is the foundation of AI; if the data is bad, the AI will be bad. That's why it's so important to make sure that data is collected, stored and used ethically.
Ethical data collection means being transparent about what data is being collected and how it will be used. It also means getting consent from the people whose data is being collected, storing data securely and protecting it from unauthorized access. Finally, ethical data use respects people's privacy and does not discriminate against them.
When businesses prioritize data quality, they build a foundation for resilience. They are creating a business that can withstand shocks and disruptions and setting themselves up for sustainable success.
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SAS recently conducted a survey to learn more about why resilience has become such an important topic to executives around the globe. The findings, published in Resiliency Rules , reveal something interesting from an AI perspective.
In the survey, we've identified five rules for resiliency, and one of those rules is "equity and responsibility." Somewhat shockingly, the importance of equity and responsibility among over 2,400 senior executives surveyed is more than aspirational. In fact, many cited implementing technology solutions to ensure ethical innovation, and routinely, data quality was the most important technical factor in achieving that end. Not surprisingly, those considered less resilient note costs and data quality as barriers.
Given this focus on equity and responsibility, why are more equitable societal outcomes so elusive? And can AI help improve those outcomes?
Read more in Reggie's blog: Data quality: The foundation for trustworthy AI - SAS Voices
#TrustworthyAI #Data #DataPrivacyDay
Consultant at North Highland | AI Strategy | Data Science | Machine Learning | Python | Analytical Systems
10 个月Thanks for the thoughtful post. There’s a very important discussion to be had regarding data validation and representative sampling. As the field of data science matures, we need to make sure we are employing methodological rigor, documenting our decisions, and prioritizing explainability to maintain credibility and professionalism within the field.