Collective Data Protection For AI And Machine Learning Applications

I am delighted to reveal my newest article on predictive privacy. In it, I take on the difficulty created by big data and AI, which enable predictions about people based on anonymous information from many sources.

This paper further explores the philosophical and legal implications of predictive privacy beyond what was discussed in my 2021 article in Ethics & Information Theory. It offers a more precise definition and an argument for why it should be recognized as a new type of protected good.

The potential of predictive analytics to be misused and lead to social inequality, discrimination, and exclusion is concerning. Data privacy laws are ineffective in preventing these potential harms, leaving the use of anonymized mass data largely without regulation. In this paper, I introduce ‘predictive privacy’ as a suitable approach to data protection that could help counter such risks.

Predictive privacy is infringed upon when personal information is predicted without an individual’s cognizance or against their wishes, based on data from many others. In this regard, I suggest that predictive privacy be acknowledged as a right and measures should be taken to amend existing data protection regulations.

At last, I draw attention to the need for supervising ‘prediction power’ – a contemporary example of the information power imbalance between tech companies and the public. To be at the forefront of private investigation and have an effect on forming data security in the future, click here to access this complete article.

The article “Predictive Privacy: Collective Data Protection in the Context of AI and Big Data” provides valuable insights into the pressing issue of data privacy in the era of data-driven technologies. The concept of predictive privacy and the call for collective efforts highlight the need for responsible and ethical data protection practices to safeguard privacy rights. As AI and Big Data continue to shape our society, it is imperative to prioritize privacy considerations and work collaboratively to ensure that data-driven technologies are developed and used to respect individual privacy and uphold ethical principles.

Source: Rainer Mühlhoff

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