Is a programming language a hacking tool? How about software to detect security weaknesses?

https://gizmodo.com/the-u-s-wants-to-crack-down-on-sales-of-commercial-hac-1847904305

The U.S. Wants to Crack Down on Sales of Commercial Hacking Tools for Obvious Reasons

After a slew of hacking scandals involving private surveillance companies, the U.S. is looking to impose new restrictions on the sale of commercial hacking tools—in the hopes of clamping down on abuse perpetuated by the industry.

On Wednesday, the Commerce Department announced a rule change that will put new limitations on the resale or export of “certain items that can be used for malicious cyber activities.” This applies to tools used to infiltrate digital systems and conduct surveillance—such as the notorious commercial spyware, Pegasus —as well as other hacking and “intrusion” software, the Washington Post first reported. The rule, which has reportedly been in development for years, will be put into effect in 90 days.

While the intricacies of the new 65-page rule are somewhat thorny, the biggest result is a new license requirement for American companies that want to sell hacking tools to countries “of national security or weapons of mass destruction concern,” as well as to “countries subject to a U.S. arms embargo,” the Commerce Department’s announcement says.

The new world order. Familiar technology vs. those incomprehensible idiots in Washington? Who do you trust? Who had responses ready and the will to use them?

https://www.foreignaffairs.com/articles/world/2021-10-19/ian-bremmer-big-tech-global-order

The Technopolar Moment

How Digital Powers Will Reshape the Global Order

After rioters stormed the U.S. Capitol on January 6, some of the United States’ most powerful institutions sprang into action to punish the leaders of the failed insurrection. But they weren’t the ones you might expect. Facebook and Twitter suspended the accounts of President Donald Trump for posts praising the rioters. Amazon, Apple, and Google effectively banished Parler, an alternative to Twitter that Trump’s supporters had used to encourage and coordinate the attack, by blocking its access to Web-hosting services and app stores. Major financial service apps, such as PayPal and Stripe, stopped processing payments for the Trump campaign and for accounts that had funded travel expenses to Washington, D.C., for Trump’s supporters.

The speed of these technology companies’ reactions stands in stark contrast to the feeble response from the United States’ governing institutions. Congress still has not censured Trump for his role in the storming of the Capitol. Its efforts to establish a bipartisan, 9/11-style commission failed amid Republican opposition. Law enforcement agencies have been able to arrest some individual rioters—but in many cases only by tracking clues they left on social media about their participation in the fiasco.

States have been the primary actors in global affairs for nearly 400 years. That is starting to change, as a handful of large technology companies rival them for geopolitical influence. The aftermath of the January 6 riot serves as the latest proof that Amazon, Apple, Facebook, Google, and Twitter are no longer merely large companies; they have taken control of aspects of society, the economy, and national security that were long the exclusive preserve of the state. The same goes for Chinese technology companies, such as Alibaba, ByteDance, and Tencent. Nonstate actors are increasingly shaping geopolitics, with technology companies in the lead. And although Europe wants to play, its companies do not have the size or geopolitical influence to compete with their American and Chinese counterparts.

Maturity models provide a good outline for thinking about improvement…

https://www.zdnet.com/article/ai-ethics-maturity-model/

AI ethics maturity model: A company guide

How to develop a maturity model for building an ethical and responsible AI practice.

All you have to get right is the hardest part.

https://techxplore.com/news/2021-10-machine-fair-accurate.html

How machine learning can be fair and accurate

Carnegie Mellon University researchers are challenging a long-held assumption that there is a trade-off between accuracy and fairness when using machine learning to make public policy decisions.

As the use of machine learning has increased in areas such as criminal justice, hiring, health care delivery and social service interventions, concerns have grown over whether such applications introduce new or amplify existing inequities, especially among racial minorities and people with economic disadvantages. To guard against this bias, adjustments are made to the data, labels, model training, scoring systems and other aspects of the machine learning system. The underlying theoretical assumption is that these adjustments make the system less accurate.

… "You actually can get both. You don't have to sacrifice accuracy to build systems that are fair and equitable," Ghani said. "But it does require you to deliberately design systems to be fair and equitable. Off-the-shelf systems won't work."

Kit T. Rodolfa et al, Empirical observation of negligible fairness–accuracy trade-offs in machine learning for public policy, Nature Machine Intelligence (2021). DOI: 10.1038/s42256-021-00396-x

Journal information: Nature Machine Intelligence

How would you redesign a university to take advantage of these new technologies?

https://theconversation.com/future-of-college-will-involve-fewer-professors-166394

Future of college will involve fewer professors

Can’t hurt…

https://www.makeuseof.com/useful-web-tools-student-should-use/

5 Useful Web Tools Every Student Should Use

Also, take advantage of the employees you are paying all that tuition money for. Talk to their Computer Security instructor and see what projects you can guide.

https://threatpost.com/guide-cyberintelligence-restricted-budget/175574/

A Guide to Doing Cyberintelligence on a Restricted Budget

… In a recent SANS 2021 survey, “Threat Hunting In Uncertain Times,” we were shown that 11 percent of organizations have had their threat-hunting and intelligence programs impacted by the pandemic, with 12 percent of the organizations polled stopping their hunting programs altogether. With ransomware affiliate actions on the rise and organizations constantly under the target of business email compromise (BEC) scams, this is a horrible time to be stuck with a shrinking budget.

In light of this, we’re going to go through some broad suggestions and checklists for how to do 80 percent of what you need to do on the cyberintelligence front, at just 20 percent of the typical cost for an enterprise program.

Do you have cameras in your home?

https://www.pogowasright.org/woman-finds-amazon-has-thousands-of-recordings-of-her-all-from-home-devices/

Woman finds Amazon has thousands of recordings of her – all from home devices

John Bett reports:

A woman was shocked to discover how much information Amazon had collected on her from just a few devices – and created a video to shared the shocking truth with others.

TikTok star @my.data.not.yours uploaded a clip for her fans documenting all the information that the tech giant had collected about her.

Read more on The Mirror.

Curious. Is this likely to change Facebook’s strategy going forward?

https://www.pogowasright.org/zuckerberg-to-be-added-to-facebook-privacy-suit/

Zuckerberg to Be Added to Facebook Privacy Suit

Cecilia Kang reports:

The attorney general for the District of Columbia plans to add Facebook’s chief executive, Mark Zuckerberg, to a consumer protection lawsuit, in one of the first efforts by a regulator to expose him personally to potential financial and other penalties.

The attorney general, Karl Racine, said on Tuesday that continuing interviews and reviews of internal documents for the case had revealed that Mr. Zuckerberg played a much more active role in key decisions than prosecutors had known.

The complaint against Facebook was filed in December 2018 in the Superior Court of the District of Columbia. The suit alleges that Facebook misled consumers about privacy on the platform by allowing Cambridge Analytica, a political consulting firm, to obtain sensitive data from more than 87 million users, including more than half the district’s residents.

Read more on New York Times.

Thinking about data...

https://www.technologyreview.com/2021/10/16/1037303/in-unpredictable-times-a-data-strategy-is-key/

In unpredictable times, a data strategy is key

… . According to the survey, the most common value companies are hoping to take advantage of is smarter decision-making (79%). They also want to more deeply understand their customers and industry trends (61%), provide better services and products (42%), and implement more efficient internal operations (33%).

Companies also learned valuable lessons about the importance of data as they struggled to stay competitive during the pandemic. Roughly four out of 10 survey respondents, for example, report that they need to look at more sources of data, including demographic, geospatial, and competitor information. More than a third (37%) are evaluating machine learning and analytics—technologies essential to extract critical insights from their data. And 34% need help acting on the vast sums of data they gather and process.

Download the full report.

This adds several steps that must be documented and explained. Is it worth the added complications?

https://sloanreview.mit.edu/article/the-real-deal-about-synthetic-data/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+mitsmr+%28MIT+Sloan+Management+Review%29

The Real Deal About Synthetic Data

… Synthetic data is artificially generated by an AI algorithm that has been trained on a real data set. It has the same predictive power as the original data but replaces it rather than disguising or modifying it. The goal is to reproduce the statistical properties and patterns of an existing data set by modeling its probability distribution and sampling it out. The algorithm essentially creates new data that has all of the same characteristics of the original data — leading to the same answers. However, crucially, it’s virtually impossible to reconstruct the original data (think personally identifiable information) from either the algorithm or the synthetic data it has created.

Motivation is a two edged sword?

https://dilbert.com/strip/2021-10-20

You can be anything anyone you want to be… A common risk for such databases.

https://www.databreaches.net/hacker-steals-government-id-database-for-argentinas-entire-population/

Hacker steals government ID database for Argentina’s entire population

Catalin Cimpanu reports:

A hacker has breached the Argentinian government’s IT network and stolen ID card details for the country’s entire population, data that is now being sold in private circles.

The hack, which took place last month, targeted RENAPER. which stands for Registro Nacional de las Personas, translated as National Registry of Persons.

Read more on The Record.

I just love a good rant.

https://thenextweb.com/news/facial-recognition-could-solve-any-social-problem?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+TheNextWeb+%28The+Next+Web+All+Stories%29

Facial recognition to eat lunch? Why stop there, you cowards!?

The tech could solve any social problem

Does seem a little snoopy. The IRS will love it.

https://www.pogowasright.org/a-privacy-breach-waiting-to-happen-oklahoma-banks-speak-out-against-new-banking-reporting-proposal/

“a privacy breach waiting to happen” — Oklahoma banks speak out against new banking reporting proposal

Thomas Fleming reports:

Oklahoma Attorney General John O’ Conner is one of 20 Republican attorneys general who are calling on the Biden administration to drop a recent proposal that would increase reporting requirements for banks.

They sent a letter Friday to both President Biden and Treasury Secretary Janet Yellen. They say the proposal stands in direct opposition to privacy that Americans are entitled to.

If the proposal were signed into law, banks would have to annually report on total inflows and outflows on accounts, both business and personal, with a balance over $600.

Read more on KFOR.

Well, probably not dull old me, but you get the idea.

https://www.theatlantic.com/ideas/archive/2021/10/china-america-surveillance-hikvision/620404/

China Is Watching You

With generous state support at home and low-cost sales abroad, Hikvision has become a world heavyweight.

Even if you have never set foot in China, Hikvision’s cameras have likely seen you. By 2017, Hikvision had captured 12 percent of the North American market. Its cameras watched over apartment buildings in New York City, public recreation centers in Philadelphia, and hotels in Los Angeles. Police departments used them to monitor streets in Memphis, Tennessee, and in Lawrence, Massachusetts. London and more than half of Britain’s 20 next-largest cities have deployed them.

Hikvision’s reach requires a map to fully appreciate it. A recent search for the company’s cameras, using Shodan, a tool that locates internet-connected devices, yielded nearly 5 million results, including more than 750,000 devices in the United States.

… Offering huge discounts to American redistributors, Hikvision has supplied cameras to Peterson Air Force Base, in Colorado, as well as the U.S. embassies in Kyiv, Ukraine, and Kabul, Afghanistan. More than 90 companies relabeled the cameras with their own brands, according to IPVM, a surveillance-industry-research group. Citing national-security concerns, Congress ordered federal agencies to remove Hikvision cameras by August 2019. The U.S. government struggled to find them all.

Machine learning for fun and profit. This will work on any keypad, not just ATMs.

https://www.schneier.com/blog/archives/2021/10/using-machine-learning-to-guess-pins-from-video.html

Using Machine Learning to Guess PINs from Video

Researchers trained a machine-learning system on videos of people typing their PINs into ATMs:

By using three tries, which is typically the maximum allowed number of attempts before the card is withheld, the researchers reconstructed the correct sequence for 5-digit PINs 30% of the time, and reached 41% for 4-digit PINs.

This works even if the person is covering the pad with their hands.

The article doesn’t contain a link to the original research. If someone knows it, please put it in the comments.

I’m more likely to have lots if “small data,” but will I recognize its potential?

https://www.scientificamerican.com/article/small-data-is-also-crucial-for-machine-learning/

‘Small Data’ Is Also Crucial for Machine Learning

When people hear “artificial intelligence,” many envision “big data.” There’s a reason for that: some of the most prominent AI breakthroughs in the past decade have relied on enormous data sets. Image classification made enormous strides in the 2010s thanks to the development of ImageNet, a data set containing millions of images hand sorted into thousands of categories. More recently GPT-3, a language model that uses deep learning to produce humanlike text, benefited from training on hundreds of billions of words of online text. So it is not surprising to see AI being tightly connected with “big data” in the popular imagination. But AI is not only about large data sets, and research in “small data” approaches has grown extensively over the past decade—with so-called transfer learning as an especially promising example.

Also known as “fine-tuning,” transfer learning is helpful in settings where you have little data on the task of interest but abundant data on a related problem.

Go where the money goes? (Try to get there first.)

https://www.zdnet.com/article/gartner-survey-of-cios-highlights-investments-in-ai-cloud-and-cybersecurity/

Gartner survey of CIOs highlights investments in AI, cloud and cybersecurity

A new survey from Gartner found that a majority of CIOs are focusing their investments this year and next year on AI and distributed cloud technology.

… The survey focused on "business composability", -- which involves the mindset, technologies and set of operating capabilities that enable organizations to innovate and adapt quickly to changing business needs.

… "63% of CIOs at organizations with high composability* reported superior business performance compared with peers or competitors in the past year. They are better able to pursue new value streams through technology, too,"

(Related) Worth a detailed read.

https://www.techrepublic.com/article/gartner-analyst-12-technologies-to-accelerate-growth-engineer-trust-and-sculpt-change-in-2022/

Gartner analyst: 12 technologies to accelerate growth, engineer trust and sculpt change in 2022

… CEOs and boards are striving to grow and are willing to spend for digital investments to make direct connections with customers, Groombridge said. He cited 12 technologies that can enhance organizational efforts to accelerate growth, engineer trust and sculpt change.

 

A tool for my Computer Security students.

https://www.makeuseof.com/what-is-wireshark/

What Is Wireshark and How Can You Use It to Secure Your Network?

This free cross-platform packet sniffer can securely analyze data. Here's how you can use Wireshark to troubleshoot network issues.

At its most basic, Wireshark is an open-source and free network analyzer. It’s a piece of software that allows you to capture data packets from a private or public network connection. It also gives you the freedom to browse the data traffic going through the network and interact with it in real-time.

Always worth learning / reviewing.

https://www.theregister.com/2021/10/18/rubrik_zero_trust_architecture/

Whatever sort of disaster we’re talking about, if your backups are fried, you’re not going to recover

… while you can’t accurately predict when your facilities are likely to be hit by an earthquake, flood, or plague of locusts you can probably be assured that your systems are going to be constantly bombarded by cyberthreats, which increasingly means malware.

That’s why a zero trust approach to security is a given, as is a focus on how quickly you can recover your data if an attack does hit home, and that means immutable backups and rock solid data management.

But how does this all work in practice? You can take a deep dive with this upcoming webcast, Zero Trust Data Management and Near-Zero RTOs for your Vms on October 27 at 5pm BST (9am PT).

… Joining us is simplicity itself. Just jump over to the registration page here and drop in your details.

After reading this article, I asked myself if it could translate to other industries. I think the answer is yes. As long as you can be viewed as an authority in whatever field you choose.

https://www.bespacific.com/harvard-a-media-company/

Harvard, a Media Company

The Generalist: “Harvard, a Media Company. Harvard Business Review is a surreptitious media giant. Actionable insights – If you only have a couple minutes to spare, here’s what investors, operators, and founders can learn from Harvard, as a media company.

• There are different paths to success. The Harvard Business Review (HBR) was founded in 1922 and struggled to breakeven for more than 25 years. Today, it’s one of the world’s most impactful media organizations.

• Media can turn expenses into revenue. Plenty of businesses devote time to content marketing. But by building a media arm worth paying for, companies are able to turn marketing expenses into a revenue stream.

• The next HBR could be built by a startup. Edtech companies and fundraising platforms look well-positioned to run the Harvard playbook and create a durable, valuable media arm.

• Adapt or die. HBR adapted its content and form multiple times over its history to appeal to contemporary readers. It did so while preserving its foundational value.

Harvard Business School is a bigger media company than Forbes. Though best known as a home for higher learning, America’s most prestigious scholarly institution is sneakily, surreptitiously also a publisher par excellence with financials to match. Since its founding in 1922, the Harvard Business Review (HBR) has become a defining voice in the media landscape, bolstering the authority and reputation of its parent organization, while simultaneously bringing in hundreds of millions in revenue. It begs the question: is Harvard Business School a media company in disguise? And if it is, who else might unknowingly be a publisher, wrapped in another business? We’ll interrogate these questions in today’s piece. In particular, we’ll touch on:

• HBR’s long road to success.

• How the Review compares to other publishers.

• The case for Harvard as a media company.

• Other media empires in the making…”

I like lists. You never know what you might find useful.

https://www.infoworld.com/article/3637038/the-best-open-source-software-of-2021.html#slide1

The best open source software of 2021

InfoWorld’s 2021 Bossie Awards recognize the year’s best open source software for software development, devops, data analytics, and machine learning.

If not anti-social, at least asocial?

https://dilbert.com/strip/2021-10-18

Reducing lawyers to a math formula – I love it!

https://link.springer.com/article/10.1007/s10506-021-09300-9

Contract as automaton: representing a simple financial agreement in computational form

We show that the fundamental legal structure of a well-written financial contract follows a state-transition logic that can be formalized mathematically as a finite-state machine (specifically, a deterministic finite automaton or DFA). The automaton defines the states that a financial relationship can be in, such as “default,” “delinquency,” “performing,” etc., and it defines an “alphabet” of events that can trigger state transitions, such as “payment arrives,” “due date passes,” etc. The core of a contract describes the rules by which different sequences of events trigger particular sequences of state transitions in the relationship between the counterparties. By conceptualizing and representing the legal structure of a contract in this way, we expose it to a range of powerful tools and results from the theory of computation. These allow, for example, automated reasoning to determine whether a contract is internally coherent and whether it is complete relative to a particular event alphabet. We illustrate the process by representing a simple loan agreement as an automaton.

(Related)

https://www.amazon.com/Digital-Lawyering-Technology-Practice-Century-ebook/dp/B09HVDZCNJ/ref=sr_1_1?dchild=1&keywords=Digital+Lawyering%3A+Technology+and+Legal+Practice+in+the+21st+Century&qid=1634475734&s=books&sr=1-1

Digital Lawyering: Technology and Legal Practice in the 21st Century

Digital technologies have already begun a radical transformation of the legal profession and the justice system. Digital Lawyering introduces students to all key topics, from the role of blockchain to the use of digital evidence in courtrooms, supported by contemporary case studies and integrated, interactive activities. The book considers specific forms of technology, such as Big Data, analytics and artificial intelligence, but also broader issues including regulation, privacy and ethics. It encourages students to explore the impact of digital lawyering upon professional identity, and to consider the emerging skills and competencies employers now require. Using this textbook will allow students to identify, discuss and reflect on emerging issues and trends within digital lawyering in a critical and informed manner, drawing on both its theoretical basis and accounts of its use in legal practice.

(Related)

https://www.taylorfrancis.com/chapters/edit/10.4324/9780429298219-7/using-artificial-intelligence-enhance-augment-delivery-legal-services-ann-thanaraj

Using artificial intelligence to enhance and augment the delivery of legal services

Artificial intelligence (AI) will redefine the legal profession, changing and evolving the role lawyers perform. New types of work will be available, including symbiotic new specialisms using a perfect blend of human expertise focusing on complex high-level advisory work supported by technology and its affordances. This chapter will explore how AI is already supporting the profession and what the future holds for our further collaboration, questioning the requisite set of skills, tools and assets needed to thrive in the changing legal world. It will also include an ethical exploration of the extent AI decision-making can impact our professional responsibilities and legal ethics and question the need to realign the fundamental tenets of professional ethics for law practice. AI will most certainly be at the forefront of how the legal evolves over the next 50 years.

As a retired auditor, this interests me. Perhaps we should train an AI to conduct audits like this?

https://www.axios.com/algorithmic-audits-ai-bias-a895bba4-05bb-4d6e-bd01-59c18627393d.html

AUDITS ATTEMPT TO CLEAN UP AI BIAS

AI algorithms employed in everything from hiring to lending to criminal justice have a persistent and often invisible problem with bias.

The big picture: One solution could be audits that aim to determine whether an algorithm is working as intended, whether it's disproportionately affecting different groups of people and, if there are problems, how they can be fixed.

… Financial audits exist in part to open up the black box of a company's internal operations to outside investors, and ensure that a company remains in compliance with financial laws and regulations.

In the case of algorithmic audits, however, the actual workings of AI can be a black box to the company itself because unless explainability is built into the foundation of an algorithmic model, it can be easy to get lost.

(Related) I can easily foresee an AI vigilante. Think ‘Terminator.” (Think AIs that think of themselves as victims.)

https://academic.oup.com/ijlit/advance-article-abstract/doi/10.1093/ijlit/eaab008/6389717

AI ethical bias: a case for AI vigilantism (AIlantism) in shaping the regulation of AI

The debate on the ethical challenges of artificial intelligence (AI) is nothing new. Researchers and commentators have highlighted the deficiencies of AI technology regarding visible minorities, women, youth, seniors and indigenous people. Currently, there are several ethical guidelines and recommendations for AI. These guidelines provide ethical principles and humancentred values to guide the creation of responsible AI. Since these guidelines are non-binding, it has no significant effect. It is time to harness initiatives to regulate AI globally and incorporate human rights and ethical standards in AI creation. The government need to intervene, and discriminated groups should lend their voice to shape AI regulation to suit their circumstances. This study highlights the discriminatory and technological risks suffered by minority/marginalised groups owing to AI’s ethical dilemma. As a result, it recommends the guarded deployment of AI vigilantism to regulate the use of AI technologies and prevent harm arising from AI systems’ operations. The appointed AI vigilantes will comprise mainly persons/groups with an increased risk of their rights being disproportionately impacted by AI. It is a well-intentioned group that will work with the government to avoid abuse of powers.

(Related) True bias or simple math?

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3940705

When a Small Change Makes a Big Difference: Algorithmic Fairness Among Similar Individuals

If a machine learning algorithm treats two people very differently because of a slight difference in their attributes, the result intuitively seems unfair. Indeed, an aversion to this sort of treatment has already begun to affect regulatory practices in employment and lending. But an explanation, or even a definition, of the problem has not yet emerged. This Article explores how these situations—when a Small Change Makes a Big Difference (SCMBDs)—interact with various theories of algorithmic fairness related to accuracy, bias, strategic behavior, proportionality, and explainability. When SCMBDs are associated with an algorithm’s inaccuracy, such as overfitted models, they should be removed (and routinely are.) But outside those easy cases, when SCMBDs have, or seem to have, predictive validity, the ethics are more ambiguous. Various strands of fairness (like accuracy, equity, and proportionality) will pull in different directions. Thus, while SCMBDs should be detected and probed, what to do about them will require humans to make difficult choices between social goals.

I told my AI to file may taxes, is it my fault if it didn’t?

https://ieeexplore.ieee.org/abstract/document/9564076

Delegation of moral tasks to automated agents The impact of risk and context on trusting a machine to perform a task

The rapid development of automation has led to machines increasingly taking over tasks previously reserved for human operators, especially those involving high-risk settings and moral decision making. To best benefit from the advantages of automation, these systems must be integrated into work environments, and into society as a whole. Successful integration requires understanding how users gain acceptance of technology by learning to trust in its reliability. It is thus essential to examine factors that influence the integration, acceptance, and use of automated technologies. As such, this study investigated the conditions under which human operators were willing to relinquish control, and delegate tasks to automated agents by examining risk and context factors experimentally. In a decision task, participants (N=43, 27 female) were placed in different situations in which they could choose to delegate a task to an automated agent or manual execution. The results of our experiment indicated that both, context and risk, significantly influenced people’s decisions. While it was unsurprising that the reliability of an automated agent seemed to strongly influence trust in automation, the different types of decision support systems did not appear to impact participant compliance. Our findings suggest that contextual factors should be considered when designing automated systems that navigate moral norms and individual preferences.

A useful idea?

https://msocialsciences.com/index.php/mjssh/article/view/1086

A Study on the Laws Governing Facial Recognition Technology and Data Privacy in Malaysia

The advancement of technology in the past decade has led humans to achieve many great things. Among that is facial recognition technology that uses a combination of two techniques which is face detection and recognition that is capable of converting facial images of a person into readable data and connecting it with other data sets which enable it to identify, track or compare it. This study delves into the usage of facial recognition technology in Malaysia where its regulation is almost non-existent. As its usage increases, the invasive features of this technology to collect and connect its data posed a threat to the data privacy of Malaysian citizens. Due to this issue, other countries' laws and policies regarding this technology are examined and compared with Malaysia. This enables the loopholes of the current law and policies to be identified and restructured, which create a clear path on the proper regulations and changes that need to be made. Thus, this study aims to analyse the limitation of law governing data privacy and its concept in Malaysia along with changes that need to be made. This study’s finding shows the shortcoming of Malaysia’s law in governing data privacy especially when it involves complex technology that has great data collection capability like facial recognition.

Somewhat a rant, but if it took them 20 years to notice the “surveillance state,” perhaps these aren’t the best observers.

https://www.salon.com/2021/10/16/after-20-years-its-time-to-repeal-the-patriot-act-and-begin-to-dismantle-the-surveillance-state/

After 20 years, it's time to repeal the Patriot Act and begin to dismantle the surveillance state

Another perspective on the inevitable?

https://www.eimj.org/uplode/images/photo/The_Copyright_Protection_of%C2%A0AI_Created_works_by_the_European_Union_Copyright_Legislation..pdf

The Copyright Protection of AI-Created works by the European Union Copyright Legislation

In the European Union, copyright law has increasingly focused on broadening the scope of works that have a right to intellectual property protection. Currently, the law applies to a variety of work categories, including literary works, music, film, and sound recordings, among others. Although breakthroughs in artificial intelligence (AI) continue to contribute to the emergence of machine-generated creative works, the European Copyright Legislation framework does not consider non-human discoveries. There are currently breakthroughs that allow autonomous programs to create products of significant monetary worth ranging from software to literary works, photographs, and music.

To that end, this research study critically examines the current EU copyright legislation in order to understand its position on copyright protections for AI-generated works. Furthermore, the study explains why AI-generated works should be protected and what legal tools should be improved to provide copyright protection.

According to the findings, these creations should be protected as incentive to developers and as a guarantee of technological advancement for the entire society.