Microchipping Employees: Why or Why Not?

Microchipping Employees: Why or Why Not?

microchipping employees.jpg

Facilitator: SFIS Professor Katina Michael

Description: Recently companies internationally are considering implanting their employees with microchips to improve security which has naturally raised some public concerns about the risks, barriers and future uses of micro-chipping. Could companies sell employees' personal data to third parties? Could employers know if staff contacted a competitor about a job? Join us for an informal conversation to tease out the main issues that we see from an STS lens on implanting employees in the workplace.

Readings for Discussion: Christine Perakslis, Katina Michael, M. G. Michael, Robert Gable, "Perceived barriers for implanting microchips in humans", 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW), Date of Conference: 24-26 June 2014, Date Added to IEEE Xplore: 08 September 2014. DOI: 10.1109/NORBERT.2014.6893929

"Microchipping Employees and Potential Workplace Surveillance"

"A Wisconsin company offers to implant remote-control microchips in its employees" (2018)

Interview with Gary Retherford of CityWatcher.com (2009)

http://www.securitysystemsnews.com/article/security-company-gets-under-skin-embedded-access-chips (2006)

Bio: Katina Michael has been studying embedded technologies in humans for over 20 years. She has a joint appointment in the School for the Future of Innovation in Society and the School of Computing, Informatics and Decisions Systems Engineering. Katina is the founding editor in chief of IEEE Transactions on Technology and Society. Find out more at www.katinamichael.com

MasterClass on Interviews

I met with six undergraduate scholarship students at ASU today, focused on presenting a masterclass on interviews. Our brainstorming led us to consider the questions we need to think about when preparing to conduct an interview with a given stakeholder, toward exploratory research.

I used examples from decades of interview practice, some of which I have been able to post here: www.katinamichael.com/interviews

Collectively, the students and I had a discussion along key areas of interviews, as shown in the whiteboard brainstorming session above. We also managed to do a mock interview on tattooing (interviewer and tattoo business owner) in preparation for one of the student’s projects. We all agreed that doing a mock interview could be harder than participating in the real thing. We also noted, that one can learn from each interview, and after doing the first five or six, the skills required to elicit responses to questions would be enhanced.



Interview Research - Research Methods Guide: https://guides.lib.vt.edu/researchmethods/interviews

Powerpoint on Overview of Interviews: https://www.public.asu.edu/~kroel/www500/Interview%20Fri.pdf

Qualitative Research Method - Interviews and Observations: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194943/

Interviewing as a Data Collection Method- A Critical Review: http://www.sciedu.ca/journal/index.php/elr/article/download/4081/2608

Interview Methodology and Questions: An Example: http://www.ipfcc.org/bestpractices/interview-method.pdf

The Walking Interview: https://www.sciencedirect.com/science/article/pii/S0143622810001141

Semi-structured Interviews: http://designresearchtechniques.com/casestudies/semi-structured-interviews/

The Magic of Data Driven Regulation

An inaugural presentation of the Allens Hub at the University of New South Wales.

I had the good fortune of meeting Mireille Hildebrandt in 2008 while we were both at the London School of Economics presenting at an ID Systems Conference. And while on a recent trip to Sydney I learnt unexpectedly from Lyria Bennett Moses that Mireille would be presenting, so I stayed behind another few days. It was worth listening to her methodical presentation live!


My notes were comprehensive but may not have been altogether accurate and organised as presented (I take full responsibility for inaccuracies). Sadly there were likely another 20 or so slides that were glossed over all too quickly toward the end of the talk, but this only promises for Mireille to come out to Australia again for a part 2. I learnt some very profound things that night- and I had several k-ching moments throughout the night. This is what a brilliant academic can do- take the audience on a long metaphor and then come in with the practice. As a philosopher Mireille has a big advantage over your standard lawyer- and her sources demonstrated her art in her craft- we were spoiled with references to mathematicians, anthropologists, computer scientists, technology lawyers and more. Thank you Mireille! We look forward to the book.

Katina’s Notes

Mireille has a full time affiliation with Vrije Universteit Brussel and several other minor affiliations.

Interfacing law and technology

Lawyer and philosopher; part-time chair with CS; lawyers begin to interact with CS

Legal tech

Responses from lawyers regarding legal tech

Some of the responses include:

·       AI in the law is nonsense, not feasible, waste of time

o   Old logic

·       AI in law will democratize the provision of legal services

o   Apps. Landlord vs big company

o   Find the app and get a prediction of whether you will win a case or not

·       AI in law will solve many legal problems caused by text-driven complexity

o   Text is naturally ambiguous

o   Legislation

o   Enormous; so much text; especially with international jurisdictions; the contradictions are too much. Without AI it is impossible?

·       AI in law will solve some problems and create new ones

·       AI will depend on how we will develop “legal tech” and by whom?

o   Lawyers, CS, consultants, policymakers?

o   And for WHOM?

§  Who is paying for this?

o   What are we optimizing for?

·       Is legal text about reasoning, meaning; but in a machine legal text is just “Data” that you are trying to correlate, and what you are asking algorithm to do is “optimize”. The question is what are you optimizing for? If you have no answer to this the machine will not learn anything.

Should we understand how the technology of legal tech works?

·       We can drive a car without knowing how the engine works?

o   Remember Pirsig’s “The Art of Motorcycle Maintenance”

·       We act on doctor’s advice though we don’t know how they get their diagnosis?

o   Note that doctors generally employ ‘diagnosis by treatment’

o   Doctor has potential diagnosis or none- and he will try something- and then start to figure out what is wrong with you

§  We expect doctor knows and we don’t have to get involved

§  There is trust in a doctor’s knowledge and that the ‘engine is built well’

·       Can we trust legal technology?



·       We must not believe

o   “prestidigitator enables him to do things that are not noticed by those whom he is engaged in fooling (1939-John Dewey)

·       Some people claim a trade-off between interpretability and accuracy:

o   The less “ordinary folk” understand AI, the better it functions

§  If we have this legal tech we can predict the case solution

§  Dangerous way of going about things

§  Some people not necessarily within CS, they claim there is an accuracy issue

§  AS systems become more difficult to understand they will become more accurate

§  Don’t try to make them understandable because it downgrades their functionality

§  If a then b does not imply if b than a…

§  If the developer says they do not understand what the AI is doing, raises the question “What the accuracy actually means”? Is this truly accuracy?

·       Some people claim that blockchain application support trustless transactions

o   Trust is displaced from institutions, and put in technology instead

§  Trust in the minors… people who write the code for smart contracts

·       This is the lure of magic:

o   We don’t have to drive or trust the doctor, so we don’t have to understand the code to use it

·       In anthropology:

o   Mistaken attribution of causality (the rain dance)

o   Raising fear and inviting subservience (the power of the priest)

o   We are now warned of an arms race in AI and asked to submit, e.g. our data

§  Behavioural

·       Such magic is not reserved for “primitive society”

o   All types of society are vulnerable to such thinking

o   All types of society found ways to fact-check and to call-to-account

§  To call the priests to account, the board of governors etc… requires the below…

o   This requires resilience, patience and a serious effort to understand

§  Lawyers need to understand this tech

o   And a well designed system of checks and balances (e.g. re car and doctor)

§  That is why we can afford to say as a user- I don’t need to know more about it.

o   We call it Rule of Law: legality, auditability and contestability

Counting as a human being in the era of Computational Law (COHUBICOL)

·       However not everything that can be counted counts, and not everything that counts can be counted. “William Cameron”

·       To count to calculate to compute

o   Incomputability from a CS perspective

§  Maths and CS term

o   Godel, Wolpert, Mitchell

§  Moving from axcioms to deductively drawing conclusions

§  Mathematical proof “no lunch theorem”

o   Inferences from that data to predict new data

§  There is one thing that limits machine learning from predictions, the simple fact that you can only train on present and historical data

·       You cannot train on future data…

·       This is what limits machine learning

·       The mathematical assumption of ML are incorrect but productive

o   They can do great work but are incorrect

·       To count, to quality, to matter:

o   Incomputability from an anthropological perspective about how people interact

o   G.H. Mead, Arendt, Plessner, Ricoeur

o   The co-constitution of self, mind and society

o   “Imagine that I talk to a small child of 1-1.5 yrs old, the child learning to speak, and I tell the child you are Charlotte and I am Mireille, and then the child will say opposite… and then say again… “You and I”… and then the child, that “she is you to me”, and then decentering, and that looking back at yourself from a perspective of being human

§  We are trying to make computer a different thing

o   When I say “I” to constitution of self, we are not born into a self. We are developing into a self because we are being addressed by others.

§  System of “law” to address others.

·       Law co-constitutes us in our expectations

o   Descartes: I think therefore I am

§  He did not get it…

§  “Being profiled”; you think therefore I am.

§  The constitution of the self

o   Legal protection

§  Because we are being profiled, we get better results from search engine

§  It is not because we have computers we are constituted by things, it is not because of machines…

§  Currently through “human interactions”

§  Something changes when the emancipation comes from machines and not other human beings.

·       To what extent is being profiled by machines, different from other people or constitutions, or the law, and what is changing because of computation background because of systems profiling us

§  Against being “overdetermined”

·       By gaze of the other (Mead)

·       Soi-meme…

·       How state sees us

·       So what happens if something like the law that is nl and text, that the law is based in legal tech


Data-driven legal tech:

·       eDiscovery

·       Argumentation mining

·       Prediction of judgements

·       So when I have a particular legal case I can look for things that count in the law

·       Most dangerous type of AI is the prediction of judgements

o   Code driven legal tech

o   Code driven legislation (policy articulated in code), contracts (in fintech, transfer of assets), decisions (public administration), connected with blockchain

o   Instead of writing a contract in NL, you translate the content into CODE, and make that code “Self-Executing”

o   Can do the same with regulation; could have a policy written into code

o   With every move you make, the text is executed autonomically

Machine learning

o   What is A/B Testing?

o   All the web sites are liked, and are experienced, there is A/B testing happening continuously

o   A small change that is tested.

§  Do you like version A or version B?

§  Software-based

§  Software automatically calculates which attracts the most favourable behavior (click-based behavior; or purchasing behavior; preferred?)

§  Which site has better outcome, that is the site that has better implementation

§  Continuous process

§  Changing buttons; +3 days keep up with competitors

o   We are continually being nudged by certain things running in the background

o   Online environment is “Add revenue”

o   Optimize web site means put content that attracts more $

o   We are surrounded by online environments that have ad driven content

§  This microtargeting does not really work because human behavior is far too complex and far too smart

§  From side of CS, there is an urge to think it works

§  What is the statistical relevance?

·       Because moving too fast, there is a lure to do “p-hacking”

o   If you have significance you find favourable, then you STOP, it is methodologically unsound but people continue buying into this

§  Proctor and Gamble withdrew their budget, and this year it said “it didn’t cost us anything”

o   Lawyers must not make that same mistake

o   We don’t want “Crappy Machine Learning” giving verdicts

o   Tom Mitchell:

§  A computer program is said to learn from experience E, with respect to some class of tasks T (prediction of judgments)…

o   ML often parasites on human domain expertise: what cs calls “ground truth”

o   The politics are in who get to determine E, T, and P

o   The ethics are in how they are determined

§  Law concerns the contestability

o   Are you optimizing to cut out racism or for banal statistics… triggered a whole discussion on “fairness” from statistical point of view

o   But law is about contestability? How can we make law this way and make sense in criticizing it

o   Company that has the priority software to do this (how they handle the stats), the statistical error for black people is to their detriment, and for white people it is beneficial.

o   But what we have to do is to “compensate” and understand the error rates

o   “AI Program able to predict human rights trials with 79% accuracy”

o   Is it in use?

o   “Assumption: text extracted from published judgements are a proxy for applications lodged with the Court”

§  But not accurate. They only used cases in English. And not everything published. Doesn’t have all relevant data.

§  Get to the “low hanging fruit” but why?

·       So don’t suggest you have it right for 79% of cases

·       Problem: as authors state, facts may be articulated by court to fit the conclusion

o   As selected and rendered by courts as they have found in their mind

o   Cases held inadmissible or struck out beforehand are not reported, which entails that a text based predictive analysis of these case is not possible

o   The experience is reduced

o   Why? Admissible cases = low hanging fruit

o   Problem that cases that are not reported applications, they would make a difference which now remains invisible

o   Data on cases related to art. 3, 6, 8 ECHR (privacy, torture…)

o   Why? Because they provided the most data to be scraped, and sufficient cases for each

o   Problem: here you are framing things, that all cases are either 3, 6, or 8 but the rest remain invisible (e.g. art. 5, 7, 8, 10, 14)… but the way it works, that all interlinked articles, and they say x y z is obvious. But you are presenting these as independent variables.

o   Dataset = publicly available

o   Need to distinguish it is EXPLORATORY RESEARCH

§  You don’t have the full dataset

§  For each article: all cases (apart from non-English judgements)

§  Equal amount of violation and non-violation cases

§  Test extraction by using regular expressions, excluding operative provisions (of, and on, and the)

o   Circumstances and topics are best predictors, combined works best

o   Law has the lowest performance

§  Discussion: facts are more important than the law

§  Legal formalism and realism: evidence that legal realism is realistic

o   This is nonsense

§  Facts has been framed in a way that they are “Determined” but you don’t have the facts…

§  In a lot of the cases there are no law section due to an inadmissibility judgement

o   SO sometimes there is no law


o   I’ve asked lawyers and look the program is better! NO!



Text-driven interpretation:

o   Only a good lawyer does close reading and bounded rationality

o   Integrity of law and logical coherence

o   Treating like cases alike

o   Legal certainty as contestability

o   Ambiguity tells us how text will affect life and opens for contestation and argumentation… this is very different to code law

Data- and code-driven interpretation:

o   These systems do “remote reading”

o   With new technologies we can do remote.. instead of how we read a canonical text… and machine can read everything and do inferences

o   Remote reading based on NLP

o   Coherence based on the approximation of a mathematical target function

o   Input and output- mathematical function (not reasoning), so there is an assumption that with machine learning our world (and our legal world), is ruled with mathematical functions

o   By getting hold of maths function someone become a good lawyer- no!

o   But if we outsource tasks to these technologies it is based on these assunptions that a mathematical “Target function”, based on “predictive accuracy” or blockchain type legal tech

Text-driven normativity and legal protection

o   Because of ambiguity in human language but not so flexible … lawyers contrained by legal norms… higher court can impose interpretation on them

o   Suspension of judgement, contraints upon personal opinion

o   Practice and effective legal remedies with institutions checks and balances

Data-and-code-driven normativity and legal protection

o   Either freezing the future by making predictions based on historical data

o   Or by way of deterministic self-executing code

o   Contesting statistics and contesting execution of irreversible code

Lawyers will have to make a good grip on statistics to contesting


Learners and decisional algorithms

o   The learner…

o   Once the system has learned then you can translate that output to another algorithm

o   Cases with these 4 characteristics will always be judged like that

§  Then develop another algorithm—this causes violation

Illusion of legal certainty



Legal protection by design vs legal design

o   Can you use it enforce compliance

o   If you reorganize legislation and contracts, where non-compliance is ruled out, then it is technical management, and administration

o   Legal protection by design

o   We cannot think of legal protection in same way as always done

o   We have no tools to protect ourselves as lawyers and people we defend

§  If you develop and employ legal technology, then you have to embed in the design of that technology, the legal protection must be embedded

§  Democratic legitimization (representation, deliberation, participation)

§  Enabling the contestation into the design of these systems

o   Legal protection impact assessment

The legal tech is NOT magic (not like the car or medical science)

IT does not deserve blind trust

Law shapes the checks and balances that enable trust in engines and medicine

IKEA: An Operations Management Case Study

I was asked by Dr Roba Abbas to give a guest lecture to her Operations Management course. What an honour to be doing so! My slide-deck is below.


Creating an Interplanetary Skin - Interplanetary Network of Things (INoT)

Problem: Will there be Netflix on Mars?

Interplanetary skin; Interplanetary Networks of Things; Interplanetary Internet; Extreme Environments; Deep Space Network; Network of Nodes; Network of Networks; Interplanetary Networking (IPN); Challenges; Connectivity; Building a Space Internet; Bundle Protocol; Transmission Delays; Mars Telecommunications Orbiter (MTO); Talking by Laser; DTN Based Communications; User Applications

“The Future of Space travel demands better communications”

“Outer Space Forbids Constant Connectivity”



Network of Nodes

Ever since the first American spacecraft went orbital in 1958, NASA's craft have communicated by radio with mission control on Earth using a group of large antennas known as the Deep Space Network. For a few lonely probes talking to the home planet, that worked fine. In the decades since then, as NASA and other space agencies have accumulated dozens of satellites, probes, and rovers on or around other planets and moons, the Deep Space Network has become increasingly noisy. It now negotiates complex scheduling protocols to communicate with more than 100 spacecraft.

Most rovers (both lunar and Martian) talk to the Deep Space Network in one of two ways: by sending data directly from the rover to Earth or by sending data from the rover to an orbiter, which then relays the data to Earth. Although the latter method is wildly more energy efficient because the orbiters have larger solar arrays and antennas, it can still be error-prone, slow, and expensive to maintain.

The future of space travel demands better communication. The pokey pace at which our current Martian spacecraft exchange data with Earth just isn't enough for future inhabitants who want to talk to their loved ones back home or spend a Saturday binge-watching Netflix. So NASA engineers have begun planning ways to build a better network. The idea is an interplanetary internet in which orbiters and satellites can talk to one another rather than solely relying on a direct link with the Deep Space Network, and scientific data can be transferred back to Earth with vastly improved efficiency and accuracy. In this way, space internet would also enable scientific missions that would be impossible with current communications tech.


The Tree of Mars

Venn Diagram: 3 sets, unions, intersections and complements

High-Level Mindmap

Ultra High Level Network Plan with Communities of Interest

Ultra High Level Network Plan with Communities of Interest

Numbers + Simplification

Numbers + Simplification

Exploratory Learning Session at ASU



I went to a meeting hosted by the Interplanetary Initiative today and found myself volunteering to teach an Introduction in the area of Exploratory Learning.

Exploratory learning can be defined as an approach to teaching and learning that encourages learners to examine and investigate new material with the purpose of discovering relationships between existing background knowledge and unfamiliar content and concepts.

The direct link to the Interplanetary Initiative’s Exploratory Learning module is here.

Through exploration learning, learners should:

  • Recognize and be unafraid of unsolved problems,

  • Be curious about what is known and how we know it,

  • Be willing to work toward answers in steps over time,

  • Develop independence and initiative in working toward solutions,

  • Have patience with ambiguity,

  • Have patience with dead ends (“failures”) and thus build resilience,

  • Understand the difference between a problem they have not solved, and a problem no one has solved,

  • Practice listening and respecting the contributions of teammates and

  • Experience knowledge creation.

During exploration learning, learners should do one or more of:

  • Practice asking questions,

  • Learn how to improve their questions,

  • Solve problems that require multiple steps and may not have single answers,

  • Identify and tackle problems whose solution is not known to the team or instructor (knowledge creation),

  • Obtain and assess the quality of the content they use to reach answers,

  • Assess the quality of the answers they produce, and

  • Work in interdisciplinary groups where all voices contribute.

What is a Planetary Skin?

The launch of Planetary Skin by NASA and Cisco Inc., a new platform for measurement, reporting and verification is hoped to enable the unlocking of US$350 billion per year in 2010–2020 for mitigation and adaptation to climate change.

Planetary Skin is a global-monitoring system of environmental conditions intended to help effective decision making with data collected from various sources which includes space, airborne, maritime, terrestrial and people-based sensor networks. It is then analyzed, verified and reported over an open standards based Web 2.0 and 3.0 collaborative spaces.

Useful Links

Cisco and NASA R&D public that cuts across institutional, disciplinary, and national boundaries and create a space for flexible pooling of assets and ideas between stakeholder networks.

Planetary Skin Institute

From One Earth To One World

Alerts on the Planetary Skin

How NASA, Cisco, And A Tricked-Out Planetary Skin Could Make The World A Safer Place

Planetary Skin Institute is a bridge between organizations like the World Economic Forum, NASA, and the University of Minnesota. It takes in massive amounts of data from space-to-mud-to-ocean sensors. And it uses experts and big data analytics to help emerging market governments know things like where to build infrastructure and where droughts will hit.

Its latest project: Developing virtual weather stations using “exhaust” cell phone data. And helping the government of Brazil create a national monitoring and early warning system for natural disasters–a system few countries have, but all need.


One notable example of these risk management and prevention tools: new virtual weather stations currently being tested by Planetary Skin Institute and their partners.

In a breakthrough in environmental sensing and a new way to use junk data, the team uses “exhaust data” from cell phone towers to predict weather conditions by monitoring the speed of radio waves as they travel through humid air. This allows sensing of local environmental conditions anywhere there are cell towers–places that rarely have weather monitoring right now because traditional weather stations are too costly or the locations are too remote. It sounds simple, but it is critically important. Tracking the weather allows data scientists to connect that with all sorts of other information and, most immediately, to predict things like landslides. And to do so for people who live in the areas nearest the towers–typically shantytowns where populations are at greatest risk.

The project in Brazil has been running successfully for two years. If it continues to work, it is a risk management approach and toolkit that Planetary Skin Institute is planning to bring to the rest of the world–the next step in their evolution.

Planetary skin institute ALERTS: automated land change evaluation, reporting and tracking system by J. D. Stanley of the Planetary Skin Institute, Proceeding COM.Geo '11 Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications, Article No. 65, Washington, DC, USA — May 23 - 25, 2011.

Planetary Skin: A Global Platform for a New Era of Collaboration, Juan Carlos Castilla-Rubio and Simon Willis, 2009.

Complexity and uncertainty are hallmarks of the early 21st century, as recent developments in the global financial markets demonstrate all too vividly. Responses to the financial crisis have prominently featured demands for global coordination. Our economic woes, however, are dwarfed by the increasing threats of climate change and environmental degradation— and their attendant miseries, such as pandemics and poverty. Unprecedented global coordination and collaboration are the only ways to address these environmental dangers.

Actionable consensus on addressing climate change is now evident in public policy announcements from global leaders, and in the coalescing of private and public opinion that the world needs to address quickly and decisively the varied perils created by man-made climate change. At the World Economic Forum in 2009, public and private sector leaders outlined three basic requirements for mitigating and adapting to changing climate: (1) targets for countries that effectively put a price on carbon; (2) large-scale predictable and sustainable financing for mitigation and adaptation strategies, and, critically (3) the creation of a globally trusted mechanism for measurement, reporting, and verification (MRV).

While measurement is third on the list, it is the essential precondition to any creation of value, or to unlock financial flows. The simple axiom that “you can’t manage what you can’t measure” holds true—especially for the most complex challenges.

NASA-Cisco climate project to flash 'Planetary Skin'

NASA and Cisco Systems Inc. are developing "Planetary Skin" -- a marriage of satellites, land sensors and the Internet -- to capture, analyze and interpret global environmental data. Under terms of an agreement announced during a Capitol Hill climate summit today, NASA and Cisco (Nasdaq: CSCO) will develop the online collaborative platform to process data from satellite, airborne and sea- and land-based sensors around the globe.

The goal is to translate the data into information that governments and businesses can use to mitigate and adapt to climate change and manage energy and natural resources more effectively, NASA and Cisco officials explained in interviews.

"There are a lot of data out there, but we have to turn that into information," explained S. Pete Worden, director of NASA's Ames Research Center. "What we are trying to do is use Cisco's expertise in data handling, put our data in there and explain what's really going on in the rainforests."

Indeed, the partners' first project, "Rainforest Skin," will focus on integrating a comprehensive sensor network in rainforests around the world. The project will examine how to capture, analyze and present information about the changes in the level of carbon dioxide -- the main heat-trapping gas -- in the Amazon and other areas. Information will be posted on the project's Web site.

Other projects during the next 18 months will look at changes in land use and water, Worden noted.

"This will begin to give us a sense of, if we pass cap and trade, is it working," he added.

Now about the project's name: "There are many layers of skin, of information, and this will help us understand all of the interconnected data," explained Worden, whose agency provides continuous global observations using satellites and other spacecraft.

Juan Carlos Castilla-Rubio, who directs Cisco's climate change practice, said the information should help companies manage environmental and financial risks in a carbon-constrained world.

"It's providing the support platforms for people to make decisions because today we fly blind," added Castilla-Rubio, whose San Jose, Calif.-based company specializes in Internet Protocol networking.

The Center for Global Development has developed a Web site of its own, called Carma (Carbon Monitoring for Action), which tracks emissions from 50,000 power plants around the world. The Washington, D.C.-based nonprofit research organization is also developing a way to monitor emissions savings from forest conservation.

"These investments in information now are absolutely critical," said Nancy Birdsall, the center's president, who participated in today's summit with Cisco Chairman and CEO John Chambers. "We have to create that information and track it over time if we're going to have any kind of system at a global level that people in this country and other countries can trust."

"We'll have to have ... something akin to independent monitoring," she added.