This month, Andrew Bernier, a US Army Corps of Engineers researcher and a union leader, says that he has received a barrage of menacing messages from the same anonymous email account. Unfolding like short chapters in a dystopian novel, they have spoken of the genius of Elon Musk, referenced the power of the billionaire's so-called Department of Government Efficiency (DOGE), and foretold the downfall of "corrupt" union bosses. But the most eerie thing about the emails, which Bernier says began arriving after he filed an official charge accusing the Trump administration of violating his union's collective bargaining agreement, is that they included personal details about his life--some of which he believes might have come from surveillance of his work laptop. The author referenced Bernier's union activities, nickname, job, travel details, and even the green notebook he regularly uses. The most recent email implied that his computer was loaded with spyware.

Automated decision systems are increasingly used to take consequential decisions in problems such as job hiring and loan granting with the hope of replacing subjective human decisions with objective machine learning (ML) algorithms. However, ML-based decision systems are prone to bias, which results in yet unfair decisions. Several notions of fairness have been defined in the literature to capture the different subtleties of this ethical and social concept (e.g., statistical parity, equal opportunity, etc.). Fairness requirements to be satisfied while learning models created several types of tensions among the different notions of fairness and other desirable properties such as privacy and classification accuracy. This paper surveys the commonly used fairness notions and discusses the tensions among them with privacy and accuracy. Different methods to address the fairness-accuracy trade-off (classified into four approaches, namely, pre-processing, in-processing, post-processing, and hybrid) are reviewed. The survey is consolidated with experimental analysis carried out on fairness benchmark datasets to illustrate the relationship between fairness measures and accuracy in real-world scenarios.

Artificial intelligence (AI) has the potential to solve many routine business challenges -- from quickly spotting a few questionable charges in thousands of invoices to predicting consumers' needs and wants. But there may be a flipside to these advances. Privacy concerns are cropping up as companies feed more and more consumer and vendor data into advanced, AI-fuelled algorithms to create new bits of sensitive information, unbeknownst to affected consumers and employees. This means that AI may create personal data. When it does, "it's data that has not been provided with [an individual's] consent or even with knowledge", said Chantal Bernier, assistant and interim privacy commissioner in the Office of the Privacy Commissioner of Canada from 2008 until 2014 who now consults in the privacy and cybersecurity practice of global law firm Dentons.

Artificial intelligence (AI) has the potential to solve many routine business challenges -- from quickly spotting a few questionable charges in thousands of invoices to predicting consumers' needs and wants. But there may be a flipside to these advances. Privacy concerns are cropping up as companies feed more and more consumer and vendor data into advanced, AI-fuelled algorithms to create new bits of sensitive information, unbeknownst to affected consumers and employees. This means that AI may create personal data. When it does, "it's data that has not been provided with [an individual's] consent or even with knowledge", said Chantal Bernier, assistant and interim privacy commissioner in the Office of the Privacy Commissioner of Canada from 2008 until 2014 who now consults in the privacy and cybersecurity practice of global law firm Dentons.