Leading organizations are rapidly expanding the role of privacy practices in response to the rise of AI. They know that the success of utilizing AI depends on the governance structure.
But most organizations lack the complex structures needed to effectively oversee data-science teams and teams that may acquire AI solutions, including operations and HR, and core teams, such as privacy, traditionally handle governance.
As the focus on regulation is increasing, and organizations are emphasizing data, analytics, and AI-driven decision-making more, insufficient governance exposes organizations to unnecessary risks, especially when teams are aware of data restrictions under the law.
Privacy teams are now positioned as a core element of the emerging AI governance area.
The term "privacy" can have different meanings for data science and privacy teams.
In the context of privacy regulations, "transparency" means informing subjects about how their data is processed.
In contrast, in data science, transparency explains how a model makes its decisions.
In the privacy realm, "accuracy" concerns the correctness of a subject's data.
In contrast, in data science, it refers to how well a model performs on a population, i.e., the proportion of correct decisions made by the model.
Privacy has always meant collecting the minimum amount of data necessary for a specific purpose and restricting access and retention to what is essential.
On the other hand, AI requires large amounts of data from diverse sources to extract insights.
These two worlds are now converging; as they do, they can address the promises and threats of data privacy and AI.
Is your privacy governance ready for AI?
Given the significant data requirement of AI applications to make robust decisions, balancing benefits and risks is often necessary, especially given AI's impact on stakeholders.
By integrating ethics into privacy and data protection practices, organizations entrust privacy teams with increased responsibility to oversee AI.
This change demands that privacy groups understand how models are developed and tested to evaluate development practices such as bias mitigation.
Privacy teams must play an active role in this shift, as they are not the only group taking charge of AI governance.
Data science teams, business units, and functional groups are all moving toward more robust operating models around AI.
"Currently, Meta charges regional users €9.99/month on web (or €12.99/month on mobile) to opt out of seeing any adverts per linked Facebook and Instagram account. The only other choice EU users have if they want to access Facebook and Instagram is to agree to its tracking — meaning the offer is to literally pay for privacy, or “pay” for free access by losing your hashtag#privacy."
Enterprise AI, a toolbox for risk management https://wildintelligence.substack.com/p/enterprise-ai-a-toolbox-for-risk-management
"Currently, Meta charges regional users €9.99/month on web (or €12.99/month on mobile) to opt out of seeing any adverts per linked Facebook and Instagram account. The only other choice EU users have if they want to access Facebook and Instagram is to agree to its tracking — meaning the offer is to literally pay for privacy, or “pay” for free access by losing your hashtag#privacy."
Meta’s ‘consent or pay’ data grab in Europe faces new complaints: https://techcrunch.com/2024/02/28/meta-consent-or-pay-consumer-gdpr-complaints/