Manuel O’Brien Hughes, IBM Government Relations Leader in Chile and Mexico, shared his opinion with Cointelegraph en Español about the data protection in the age of artificial intelligence.
“We are in the age of Artificial Intelligence (AI) everywhere. In fact, 84% of people in the world today use an AI-enabled device or service. As more people share their data to be leveraged by these systems, trust becomes the cornerstone of interactions with organizations. This trust is manifested when the devices or services used do what we expect of them, in the same way that we learned to trust that a banking application will carry out accurate transactions.O’Brien Hughes expressed, adding: “However, the way in which companies use our data or AI to provide us with multiple benefits can vary, as can the associated risks, since not all data is managed in the same way and not all AI technologies have the same process. of creation. In that context, regulation is essential”.
Regulating data management in the data economy
“If data is like oil, we could say that, once refined, you get AI gasoline. This combination is the one that can deliver value, both to people and to companies in the data economy. However, there is one key element that has yet to be meaningfully addressed in regulatory frameworks: the different risks that data-driven business models pose to individuals.says Manuel.
From IBM, they believe that there is two different categories of data-driven business models:
- Those at high risk: who use people’s data as a source of income (monetization of external data). In this model, people have little understanding of how their data is accessed, used in the data economy, or the level of risk they take in providing it.
- Low-risk: using data to improve operations, products or services (internal data monetization). In general, people can hope that their data does not leave this relationship or they can vote with their wallet if they are not satisfied.
“This distinction will allow us to seek a more appropriate regulation with a better balance. For example, you can adjust the regulatory burden to be proportional to the risks of data-driven business models, increase the transparency of data reselling, require buyers to verify that the data was handled legally and transparently, among other obligationsO’Brien Hughes clarifies.
Precisely regulating Artificial Intelligence technologies
From an AI perspective, we at IBM believe that: “these systems must prioritize the privacy of individuals and the rights of data owners. That’s why we’ve ordered around the world that there be precise regulation for AIin order to establish stricter controls and policies on the final uses of the technology, where the risk of social harm is much greater”.
“In this line, it is essential that the regulation around AI consider three principles: first, that the purpose of AI is to enhance Human Intelligence, not replace it; second, what the data and insights generated by that data belong to its creator; and third, that powerful new technologies like AI must be transparentexplainable and should mitigate harmful and inappropriate biases”, they comment from IBM.
“Our call to action is clear: it is essential build trust without stifling innovationwhile we safeguard problematic use cases so that a change or fix is in progressO’Brien Hughes explains.
The Government Relations Leader for IBM suggests that some aspects to guide regulation around Artificial Intelligence are:
- demand transparency: Organizations need to be up front about when, where and how they are using AI and data. For example, if a person is talking to an AI virtual assistant, they should be informed that they are talking to an AI and not a live person.
- Propose different rules for various use cases: Policies should reflect distinctions between higher risk and lower risk applications. For example, the risks posed by a virtual assistant are not the same as those of an autonomous vehicle.
“The ways organizations use people data and AI are continually evolving. It is not about implementing new data protection rules prematurely or banning technology because both are transversal axes of innovation, it’s about collaborating to address the risks of data monetization, promote the responsible advancement of technology, and push the boundaries of innovation by pooling our collective resources and expertise to enhance our collective and individual well-being”, concludes Manuel O’Brien Hughes.
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