The Privacy Foundation ( https://www.law.du.edu/privacy-foundation ) has posted details of their November 12^th Seminar: Privacy-HIPAA/Foundational Principles—Current and Future Trends

Program Details

Is the result similar to a police patrol or something more sinister. Look for yourself.

https://www.wired.com/story/ddosecrets-police-helicopter-data-leak/

1.8 TB of Police Helicopter Surveillance Footage Leaks Online

LAW ENFORCEMENT USE of surveillance drones has proliferated across the United States in recent years, sparking backlash from privacy advocates. But newly leaked aerial surveillance footage from the Dallas Police Department in Texas and what appears to be Georgia's State Patrol underscore the breadth and sophistication of footage captured by another type of aerial police vehicle: helicopters.

The transparency activist group Distributed Denial of Secrets, or DDoSecrets, posted a 1.8-terabyte trove of police helicopter footage to its website on Friday. DDoSecrets cofounder Emma Best says that her group doesn’t know the identity of the source who shared the data and that no affiliation or motivation for leaking the files was given. The source simply said that the two police departments were storing the data in unsecured cloud infrastructure.

DDoSecrets gained notoriety in June 2020 when it published a massive leak of law enforcement data stolen by a hacker associated with Anonymous. The data, dubbed BlueLeaks, included emails, audio, video, and intelligence documents from more than 200 state, local, and federal agencies around the US. The release got DDoSecrets banned from Twitter, and Reddit banned the r/blueleaks subreddit. The group, which essentially sees itself as a successor to Wikileaks, has also courted controversy by publishing leaks of sensitive data taken from the far-right platform Gab and a trove stolen in a ransomware attack on a gas pipeline services firm.

Download here: https://ddosecrets.com/wiki/Aerial_Surveillance_Footage

(Related)

https://arxiv.org/abs/2111.00992#

Artificial Intelligence, Surveillance, and Big Data

The most important resource to improve technologies in the field of artificial intelligence is data. Two types of policies are crucial in this respect: privacy and data-sharing regulations, and the use of surveillance technologies for policing. Both types of policies vary substantially across countries and political regimes. In this chapter, we examine how authoritarian and democratic political institutions can influence the quality of research in artificial intelligence, and the availability of large-scale datasets to improve and train deep learning algorithms. We focus mainly on the Chinese case, and find that -- ceteris paribus -- authoritarian political institutions continue to have a negative effect on innovation They can, however, have a positive effect on research in deep learning, via the availability of large-scale datasets that have been obtained through government surveillance. We propose a research agenda to study which of the two effects might dominate in a race for leadership in artificial intelligence between countries with different political institutions, such as the United States and China.

The original “Big Data” gatherers would naturally find AI beneficial.

https://governmentciomedia.com/ai-helping-refine-intelligence-analysis

AI Helping to Refine Intelligence Analysis

Speaking at the GovernmentCIO Media & Research AI: National Security virtual event, Director of the National Security Agency (NSA) Research Directorate Mark Segal discussed how these new capacities are assisting intelligence analysts in better processing and sorting large quantities of often complex and disparate information.

In outlining the NSA’s research priorities, Segal noted that both AI and machine-learning capacities already showed promise for better organizing the large pools of variable data their analysts sort through in producing regular assessments.

“One of the challenges that we have found AI to be particularly useful for is looking through the sheer amount of data that's created every day on this planet. Our analysts are looking at some of this data trying to understand it, and understand what its implications are for national security. The amount of data that we have to sort is going up pretty dramatically, but the number of people that we have who are actually looking at this data is pretty constant. So we're constantly looking for tools and technologies to help our analysts more effectively go through huge piles of data,” Segal said.

… “Imagine you've got a very large pile of documents, and in some of these documents there are really important things you want analysts to look at while some of the other documents are completely irrelevant. So one of the ways that we've used AI and machine learning in particular is we can have a trained human look at a subset of these documents and train a model to say which ones are really important and which ones are less important. Once you've trained a model and have enough data that you train the model successfully, you can go through a much larger collection of documents much more quickly than a human being could do it,” Segal said.

Another concrete use case that aligns AI with operational efficiency is using tailored algorithms to convert speech to text.

“If you can do that, you can make that text searchable, which once again makes the analyst more productive. So instead of listening to thousands of hours of audio to hear one relevant audio clip, you put in a few keywords and scan all this processed text,” Segal said.

(Related)

https://library.oapen.org/bitstream/handle/20.500.12657/51191/9781000504422.pdf?sequence=1#page=52

The technoethics of contemporary intelligence practice

Intelligence agencies have a history of rapid exploitation of the latest scientific and technological advances, from the electric telegraph to radio transmissions and satellite observation. As with warfare, the history of intelligence can be told in terms of the relative advantage bestowed by a series of technological innovations ( McNeill 1983 ; Warner 2014 ). This historically close relationship between intelligence and technology marks out intelligence as a sphere of activity where issues of “technoethics”1 – of the way in which technological developments impact on the nature of ethical frameworks and judgements and the inter-relationship between the two – are prevalent.

Obvious or not?

https://thenextweb.com/news/slippery-slope-using-ai-and-deepfakes-to-reanimate-history

The slippery slope of using AI and deepfakes to bring history to life

… For the past few years, my colleagues and I at UMass Boston’s Applied Ethics Center have been studying how everyday engagement with AI challenges the way people think about themselves and politics. We’ve found that AI has the potential to weaken people’s capacity to make ordinary judgments. We’ve also found that it undermines the role of serendipity in their lives and can lead them to question what they know or believe about human rights.

Now AI is making it easier than ever to reanimate the past. Will that change how we understand history and, as a result, ourselves?

Perspective.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3954591

Next Era of American Law Is Shaped Via AI And The Law

A commonly accepted notion is that there have been three primary eras of law in the history of American law. We are presumably in the fourth era right now. Legal scholars are apt to contend that AI and the law will abundantly impact the fourth era and fully shape the fifth era.

(Related)

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3955356

Three-Tiered Practice Of Law And AI

The practice of law is potentially beginning to splinter into two tiers, whereby one-tier consists of attorneys and the second tier consists of non-lawyers seemingly practicing law (to a limited extent). This is a controversial shifting of the sands. For some added controversy, we can look toward the future and perchance envision an added tier of AI-based lawyering.

(Related)

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3954531

Legal Argumentation and AI

A mainstay of lawyers is their ability to undertake legal argumentation. This is a skill taught in law school and matured over the course of a legal career. AI is going to up the game, so to speak, by providing legal argumentation enablement. Attorneys need to ready themselves.