POLICY BLOG SERIES

This raises a number of questions, most notably concerning how to collect and get access to trustworthy, valid and reliable data? How can companies and governments align data from different sources.

The discussions around the concept of emotional AI is relatively fresh in comparison to more standardised forms of AI we’re traditionally exposed to within the field of regulation and policymaking.

The concept of data regulation is an ever-evolving field, in which scholars are continually debating how the law which regulates this use of data can be moved forward in a realistic and progressive manner.

The impact of data driven innovations on our society and economy is highly dependent on the framework conditions that are set by the regulator. So far, our blogs have provided a bird’s-eye view on the regulatory landscape from legal scholars. Therefore, to get a more accurate picture, this blog will provide insights on the challenges and recommendations for the big data landscape stemming from a commercial context.

So far, we have been discussing the policy framework for Big Data. Data is the fuel for AI and we need a solid data framework, because otherwise the uptake of AI will not be successful. AI has received a lot of attention, mainly focusing on the risks it brings about. In this blog we go beyond these aspects of AI by looking at the benefits of AI and how to mitigate these risks.

Mercè Crosas is University Research Data Officer at Harvard University and Chief Data Science and Technology Officer at the Institute for Quantitative Social Science.

Analysing the relationship of these sector-specific regimes with the GDPR’s RtDP that applies horizontally to the entire economy.