Is our definition of Privacy narrowing or expanding.

https://www.semanticscholar.org/paper/A-Technical-Look-At-The-Indian-Personal-Data-Bill-Singh-Ruj/d1f406a28b4c0e5c9a28683f5298b936a45fb095

A Technical Look At The Indian Personal Data Protection Bill

The Indian Personal Data Protection Bill 2019 provides a legal framework for protecting personal data. It is modeled after the European Union’s General Data Protection Regulation(GDPR). We present a detailed description of the Bill, the differences with GDPR, the challenges and limitations in implementing it. We look at the technical aspects of the bill and suggest ways to address the different clauses of the bill. We mostly explore cryptographic solutions for implementing the bill. There are two broad outcomes of this study. Firstly, we show that better technical understanding of privacy is important to clearly define the clauses of the bill. Secondly, we also show how technical and legal solutions can be used together to enforce the bill.

For my Computer Forensics students.

https://www.semanticscholar.org/paper/AI-Forensics%3A-Did-the-Artificial-Intelligence-Do-It-Schneider-Breitinger/637826b23b22962d4ec6fd351cc92f5eb2289e80

AI Forensics: Did the Artificial Intelligence System Do It? Why?

In an increasingly autonomous manner AI systems make decisions impacting our daily life. Their actions might cause accidents, harm or, more generally, violate regulations – either intentionally or not. Thus, AI systems might be considered suspects for various events. Therefore, it is essential to relate particular events to an AI, its owner and its creator. Given a multitude of AI systems from multiple manufactures, potentially, altered by their owner or changing through self-learning, this seems non-trivial. This paper discusses how to identify AI systems responsible for incidents as well as their motives that might be “malicious by design”. In addition to a conceptualization, we conduct two case studies based on reinforcement learning and convolutional neural networks to illustrate our proposed methods and challenges. Our cases illustrate that “catching AI systems” seems often far from trivial and requires extensive expertise in machine learning. Legislative measures that enforce mandatory information to be collected during operation of AI systems as well as means to uniquely identify systems might facilitate the problem.

Even if AI was a ‘person’ it wouldn’t have deep enough pockets…

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3592546

Who Pays for AI Injury?

Algorithms hurt people everyday. Are such injuries just regrettable externalities of technological progress that victims should be left to bear? Or should someone else be civilly or criminally liable for the injury? The law does not always provide an answer, which can leave innocent victims holding the bag. Traditional doctrines condition liability on faulty conduct from a human agent, but we are now entering a phase of technological and economic progress where the people involved might be doing everything they should, and it is the machines that are misbehaving. The trouble is that machines are not cognizable legal actors.

This short paper turns to corporate law for a solution. While algorithms are not legal actors, the corporations that develop and run them are. The law should recognize that corporations act through the algorithms over which they have beneficial control. Then the social control that the law exercises over corporate harm would come to bear on algorithmic harm too.

Another backgrounder.

https://www.businessinsider.com/intelligence/ai-101?IR=T&itm_source=businessinsider&itm_medium=content_marketing&itm_campaign=content_marketing_leadgen_link&itm_content=leadgen_teaser&itm_term=leadgen_teaser-ai-101-how-learning-computers-are-becoming-smarter-2018-2&vertical=mobile#!/

AI 101

… In The AI 101 Report, Business Insider Intelligence, Business Insider's premium research service, describes how AI works and looks its present and potential future applications.

Perspective.

https://www.nytimes.com/interactive/2020/05/27/magazine/amazon-coronavirus.html

Amazon’s Big Breakdown

… At the online retailer, however, things were not going well. For many shoppers, it was the first place to turn, but demand for certain items was overwhelming the company’s ability to fulfill orders, not just for panic buyers but in general. By March 17, Amazon had suspended shipments to its warehouses of items that were not in ‘‘high demand,’’ scrambling, and often failing, to keep up with orders for soap, sanitizers and face masks, as well as a wide range of household staples, including food. By then, customers looking for these items were, for the first time, experiencing an Amazon that was conspicuously broken. Empty shelves in a supermarket are self-explanatory. But on Amazon, customers were confronted with failures that were much weirder and harder to understand, with, of course, nobody around to explain them.

… In other words, April 2020 wasn’t far off from where things might be in 2025 or even 2030. When millions of people showed up five or 10 years too soon, Amazon’s systems weren’t ready to accommodate them.