AI News, BOOK REVIEW: Consumer Privacy artificial intelligence

Ethical Conundrums of Artificial Intelligence- Data Privacy within AI

      Data exploitation: Consumers are unaware of the data that their gadgets (smartphones, computers, electrical appliances, etc.,) process and share from time to time.

      Prediction: AI-based systems possess the ability to employ complex algorithms to decode/predict sensitive information from a non-sensitive data pool.

Metrics like, location data and activity logs can expose sensitive information, like political views, mental status, ethnic makeup, etc., of an individual, which can be consumed by corporations to nudge consumers in lucrative directions GDPR and CCPA- Bastions of Data Privacy: ·

      RAIRD: Developed by Statistics Norway (SSB) and the Norwegian Centre for Research Data (NSD), this system allows the dissemination of data by permitting access to a metadata of the underlying dataset, while upholding confidentiality Best Practices to maintain data privacy within AI: ·

So it covers you from that point of view of saying I retained this for this time required, I can prove it and show you it was available for this period of time and show you on this date it was no longer available” says Rod Harrison, CTO of StorCentric and Vice President of Engineering ·

  Security and disaster recovery: The advanced technologies that are similar to blockchain are used for securing data against viruses, malware, etc., Competent solutions in this field let organizations classify stored data into different tiers.

Harrison says “you can essentially bootstrap your business back to life after a catastrophic event in exactly the perfect order that you need, rather than trying to guess what to restore first.” Conclusion: Data Privacy within AI is a heavily layered concept that currently requires our collective attention to take a step towards a world destitute of privacy lapse.

Social actors, who use data mining techniques need to take the onus and make an effort towards creating intelligent systems that conform to data privacy laws and uphold the confidentiality and trust of the society as a whole.

On a larger scale, the private sector, consumers, and the academic community can come together to further the development of an ethical code that keeps up with the technological, social and political developments.

For a comprehensive security product with already built-in solutions around GDPR and other data security and privacy regulations, write to contact@kogni.io or visit kogni.io References - Michael Daene., (September, 2018): AI and the Future of Privacy.

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How Clean Data Efforts Support Consumer Privacy Efforts

Structuringclean data discussions with privacy compliance in mind can highlight how an organization can better conduct compliance and ease regulatory fears.

The key is to consider three critical aspects that define clean data in an advanced model and consequently define the activities needed.

Your understanding will be based on the subject in which the data is being applied and will drive the degree of data literacy needed to properly clean the data.

The data has to be organized in a way to allow use in a data model, no matter if the data fields appear in a .csv file or SQL databases.

These are the teams responsible for identifying the impact of data usage within an organization, such as retention of data, declaring the purpose for data collection, and documentation of associated processes.

The discussion on intended format can reveal how the data could potentially be combined to reveal someone’s identity in a data breach, making it clearerwhich data fields are critical for identity protection.

But with the right mindset, this task can highlight how to best manage privacy and accurate model analysis, two duties that are becoming essential to any successful organization.

Catch-22? Developing AI Under GDPR

With 99% of state services online and a back-end digital infrastructure for facilitating synergy between government databases, the small Baltic country hosts a big suite of high-tech tools.  In recent years, it has remained progressive even on the latest digital frontier of artificial intelligence (AI).  AI or machine learning is already implemented in 13 projects across the country, where algorithms aid or substitute human decision-making in agriculture, school enrollment, and even courts of law.  The Kratt Report, published in May, presented a national plan to accelerate AI development in both the private and public sectors.  Despite Estonia’s present advancement, however, one unexpected threat may stifle further AI progress: privacy regulations in the EU.

Europe already lags behind the US and China by several measures of AI activity, including early-stage investment, scale, and diffusion.  AI remains a higher priority to executives in China and the US than to executives in several European countries.  North America is on average better poised for government AI readiness than Europe, with a potential risk for widening inequality between the regions ever possible.  If GDPR significantly stymies Europe’s AI presence, other countries could surge even further ahead to establish AI hegemony—an achievement that various world leaders have heralded as ensuring “quality of life,” “national competitiveness,” and even the ability to “rule the world.” It may be impossible for the EU to redress its AI gap without regressing to weaker privacy practices.  But the approaches of its competitors in the US and China might offer some solutions to practitioners debating the tension between AI and privacy at large.

The US, similar to China, has found itself caught between conflicting ideals of privacy protection and at-all-costs tech progress, exemplified by Facebook’s slew of privacy smackdowns.  Their steady increase in federal spending on AI, accompanied by a recent executive order establishing the American AI Initiative, points to consensus on whether the country should pursue AI progress, but variation between state and federal laws suggest wide disagreements on the privacy front.  On one end of the US legal spectrum, no federal privacy law exists to govern the use or collection of consumer data.  On the other end, the California Consumer Privacy Act (CCPA) mirrors the GDPR’s stringent efforts to enhance users’ awareness of and control over the collection of their data, greatly restricting companies’ ability to collect identifiable data.  Its strictness creates many of the same problems for AI development evident in GDPR.  Opt-out capabilities force companies to establish manual review processes should users choose not to allow automatic data processing;

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