Disguising malware as something
that’s supposed to help you secure your data.
Cryptanalyzing
a Pair of Russian Encryption Algorithms
A
pair of Russia-designed cryptographic algorithms -- the Kuznyechik
block cipher and the Streebog hash function – have the same flawed
S-box that
is almost certainly an intentional
backdoor.
It's just not the kind of mistake you make by accident, not in 2014.
Sounds like
something I should try with my students.
Some
assembly required: building an interdisciplinary superteam to tackle
AI ethics
Harvard
Business School Digital Initiative –
“What do a communications studies professor, a politics PhD, a
technology policy advisor, and a machine learning engineer have in
common? They share deep expertise in the ethics and governance of
artificial intelligence — and they’re members of the 2019
Assembly
program.
Hosted by the Berkman Klein Center for Internet & Society and
the MIT Media Lab, Assembly brings together a small cohort of
technologists, managers, policymakers, and other professionals to
confront emerging problems related to the ethics and governance of
AI.
AI
technologies are increasingly embedded in our lives at home and work
— powering our virtual assistants, moderating content on social
networking platforms, and helping companies hire new employees. Yet,
as AI technologies become more ubiquitous, applying them can raise
serious ethical concerns. AI systems are trained using data from the
past to make decisions or predictions about the future. This can
pose serious risks as societal biases embedded in data get baked into
new technical systems. Biased
algorithmic outputs are opaque; sometimes even a system’s
programmers aren’t sure how a prediction was made. In a world
plagued by systemic bias, how do we create AI systems that reduce
inequality, rather than perpetuate it? What frameworks can companies
use to determine if the application of a machine learning system is
unethical? How do we bring communities impacted by AI systems into
conversations about AI design and use?..”
(Related)
The AI
Boom: Why Trust Will Play a Critical Role
Artificial
Intelligence is on the cusp of becoming the biggest technology of the
information age, says Horacio Rozanski, president and CEO of Booz
Allen Hamilton. However, we need to bake human judgement into it
before it is too late, he writes in this opinion piece.
(Related) A
useful comparison of ethical guidelines.
The
Ethics of AI Ethics -- An Evaluation of Guidelines
Depressing
news for my Privacy Lawyer friends?
The
U.S. and Europe Are Approaching GDPR and Data Privacy Much
Differently
Well, GDPR is not scaring anyone. In fact, it’s
a lawyer’s dream come true. It’s becoming quite clear Europe and
the U.S. are attacking GDPR compliance problems from different
angles. In Europe, the compliance budget covers lawyering up,
whereas the on the other side of the pond, the Americans are using
their compliance budgets to solve the problems with automated
solutions. Which is the opposite if what we’d expect given the
litigious nature in the U.S. It seems the worm has turned.
(Related)
GDPR –
The Work Ahead
… The effect of the GDPR has been noticeable,
but in a subtle sort of way. However, it would be hugely mistaken to
think that the GDPR was just a fad or a failed attempt at helping
privacy and data protection survive the 21st century. The true
effect of the GDPR has yet to be felt as the work to overcome its
regulatory challenges has barely begun. So what are the important
areas of focus to achieve GDPR compliance?
An essential ‘GDPR To Do’ list for the months
ahead looks as follows:
Background.
This is well done.
Machine
learning algorithms explained
… Recall
that machine
learning is
a class of methods for automatically creating predictive models from
data. Machine learning algorithms are the engines of machine
learning, meaning it is the algorithms that turn a data set into a
model. Which kind of algorithm works best (supervised, unsupervised,
classification, regression, etc.) depends on the kind of problem
you’re solving, the computing resources available, and the nature
of the data.
Perspective.
Facebook
is not a monopoly, and breaking it up would defy logic and set a bad
precedent
Facebook
co-founder Chris Hughes laid out his arguments for breaking up the
company in a lengthy op-ed for The
New York Times on
Thursday.
The
essence of his argument seems to be that a single person, Mark
Zuckerberg, has too much control over the communications platforms,
including Facebook, Instagram and WhatsApp, that billions of people
use. Therefore, the government should force Facebook to divest its
other communications platforms and create a new agency to regulate
tech companies, particularly around privacy.
The
break-up argument is compelling if you're predisposed to dislike
Zuckerberg and Facebook after the last few years of blunders related
to user data and misinformation, and Facebook's often tone-deaf or
seemingly indifferent responses to these incidents
… It's
also illogical, difficult and a waste of time.
Facebook
is not a monopoly in its actual market — advertising — and the
product it offers is not essential to the U.S. economy or society.
Even worse, it's not clear that breaking Facebook up would solve the
biggest problems with the platform, such as misinformation and data
collection. Those problems would better be solved through targeted,
strictly enforced regulation.
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