Topical.
Professing
Principles of Digital Ethics and Privacy
Dr.
Anita Allen serves as Vice Provost for Faculty and Henry R. Silverman
Professor of Law and Philosophy at the University of Pennsylvania.
Dr. Allen is a renowned expert in the areas of privacy, data
protection, ethics, bioethics, and higher education, having authored
the first casebook on privacy law and has been awarded numerous
accolades and fellowships for her work. She earned her JD from
Harvard and both her Ph.D. and master’s in philosophy from the
University of Michigan. I had the opportunity to speak with her
recently about her illustrious career, the origins of American
privacy law and her predictions about the information age.
No
subtle argument.
Moscow’s
105,000 Facial Recognition Cameras Here to Stay as Country’s Court
System Entrenches Video Surveillance
… A
recent ruling that facial recognition does not violate citizen
privacy would appear to be the definitive statement that Moscow will
now be the largest city outside of China to track people with facial
recognition in nearly every inch of public space.
… The
lawsuit claimed that Russia’s constitution guarantees personal
privacy and the existing data protection laws specified that
biometric data can only be processed with written consent. The
courts decided against her in November, and installation of the new
facial recognition system immediately began.
… The
death blow for the efforts of privacy advocates was the more recent
ruling against a second lawsuit, which was supported by opposition
party Solidarnost and the civil rights group Amnesty. The ruling
ensures that the new video surveillance system will remain in
operation, and that citizens of Moscow will not be able to bring
legal complaints alleging invasion of privacy or misuse of personal
data due to facial recognition.
...and
eventually, to my students.
Explaining
machine learning models to the business
is
a sub-discipline of artificial intelligence (AI) and machine learning
that attempts to summarize how machine learning systems make
decisions. Summarizing how machine learning systems make decisions
can be helpful for a lot of reasons, like finding data-driven
insights, uncovering problems in machine learning systems,
facilitating regulatory compliance, and enabling users to appeal —
or operators to override — inevitable wrong decisions.
Of
course all that sounds great, but explainable machine learning is not
yet a perfect science. The reality is there are two major issues
with explainable machine learning to keep in mind:
- Some “black-box” machine learning systems are probably just too complex to be accurately summarized.
- Even for machine learning systems that are designed to be interpretable, sometimes the way summary information is presented is still too complicated for business people.
Useful
reference?
LibGuide
– Legal Responses to Coronavirus
“Lynn
McClelland has created a
new LibGuide on the U.S. legal responses to COVID-19.
Many units of government at all levels have issued, and continue to
issue, legal responses to the coronavirus epidemic, and some states
have laws pre-dating the epidemic but that have become more relevant,
such as quarantine statutes and requirements for paid sick leave.
Lynn’s guide identifies and provide links to primary sources and
high-quality summaries of primary sources. If you know of additional
resources that should be added to the guide, please feel free to
share them with Lynn (mcclelland@law.ucla.edu
).”
[via Rachel E. Green, Faculty Services Librarian, Hugh & Hazel
Darling Law Library, UCLA School of Law]
There
must be a need, right?
Bnh.ai
is a new law firm focused only on AI
When
VentureBeat asked Andrew Burt why he was starting an AI-focused law
firm, Burt was quick to clarify that it’s about AI and analytics.
But that didn’t answer the underlying question of why the world
needs a law firm focused so precisely on this one key area.
“The
thesis behind the law firm is that traditional legal expertise on its
own is not sufficient,” said Burt, a Yale Law School alum. His
partner is data scientist Patrick Hall, and together they aim to
provide legal acumen around AI and analytics that’s bolstered by
technical understanding. “If we are going to successfully manage
the risks of AI and advanced analytics, we need both of these types
of expertise commingled,” added Burt.
Called
bnh.ai
(techy
shorthand for “Burt and Hall”), the firm is located in
Washington, D.C., which Burt says confers a key advantage. “There’s
a rule in D.C. It’s
called 5.4b,
and it basically allows Washington, D.C. to be the only place in the
country where lawyers and non-lawyers can jointly run law firms
together,” he explained. That’s why Hall, who is not an
attorney, can be a partner in this law firm.
… AI
presents novel problems that, naturally, have legal ramifications.
For example, there’s debate about whether
an AI can hold a patent or
copyright
a written work.
As the medical field adopts more machine learning and computer vision
tools in patient diagnostics, questions
about physician liability continue to percolate.
Meanwhile, lawmakers are wrestling with how
to understand and regulate facial recognition.
Perspective.
War and plague are economic events.
Tech’s
big five lost $1.3 trillion in value since market peak one month ago
When
you reach the end of your binge watching…
450
free Ivy League university courses you can take online
… Here’s
a collection of all of them, split into courses in the following
subjects: Computer Science, Data Science, Programming, Humanities,
Business, Art & Design, Science, Social Sciences, Health &
Medicine, Engineering, Education & Teaching, Mathematics, and
Personal Development.
(Related)
Scribd
is giving away 1 month of unlimited access for free
Good
e-Reader:
“Reading subscription service Scribd is offering free access to
its library of over one million ebooks, audiobooks, magazines and
more for the next 30 days (no commitment or credit card information
required). Scribd told Good e-Reader that “With the spread of
COVID-19 and new regulations put into effect, we know many people are
staying close to home, yet still looking for information,
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