Today in AI: AI researchers accidentally expose 38 terabytes of private data, can we censure false AI predictions and more

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AI researchers from Microsoft accidentally expose 38 terabytes of internal data

The data included personal backups of Microsoft employees, personal data, passwords and private keys to internal Microsoft messages.

• The flaw was with the open-source training data for the AIs that the team published on code repository, GitHub.
• As reported by TechCrunch, research report from the cloud security company Wiz has revealed that the URL for downloading the data inadvertently provided public access to Microsoft's entire storage account. The data included personal backups of Microsoft employees, personal data, passwords and private keys to internal Microsoft messages.
• Wiz told Microsoft about the problem on June 22, and the company revoked the permissions on June 24. Microsoft said that it had conducted an internal investigation on August 16.

The AI ate my homework, and my schoolwork too

To be sure, AI will change the nature of not only homework but also instructions.

• Homework has long been a staple of the academic experience. How will it evolve as more students master the capabilities of (rapidly improving) AI systems? Or, to ask a slightly more pointed question: How am I supposed to know whether I am grading the student or the AI?
• Big changes are in the offing, but they will arrive slowly. Classroom practices, for better or worse, are among the stickiest of human institutions. A lot of instruction hasn’t changed much for thousands of years, even if modern chalk is better than its ancient precursors.

Can we censure AI for causing exclusion, false predictions?

Exclusion and false predictions happen not only during AI development due to biases harbored by individual developers, but they can also result from deploying AI for purposes to which it is not suited.

• One of the main concerns around Artificial Intelligence is the production of biased outputs and false predictions, which could lead to the exclusion of impact populations, traditionally excluded in real life. India is a diverse and complex country with historic dispositions like patriarchy, caste discrimination, etc.
• At the development stage, predominantly cognitive biases, historical biases, representation biases, measurement biases, etc., subjected to humans, shape the developed AI technology, ultimately leading to exclusionary outcomes.