Codes of Ethics and the Ethics of Code

Codes of Ethics & the
Ethics of Code
in the AI Era
Overview of big data / ML concerns from IEEE P70nn Working Groups @IEEESA http://sites.ieee.org/sagroups-7000/
Mark Underwood @knowlengr | Synchrony | Views my own | dark@computer.org | v1.4

Disclaimers
 Represents my views only
 Does not represent in any way the views of the following:
 Not view of my employer Synchrony
 Not view of IEEE or IEEE SA
 Not view of the IEEE P7003 WG
 Not view of the NIST Big Data Public WG
 IEEE P7003 standards work still early stage

My Perspective
 Chair Ontology / Taxonomy subgroup for P7000
 Occasional participant in P7007, P7003, P7002, P7010, P7001
 Co-chair, NIST Big Data Security and Privacy Subgroup (SP 1500)
 ASQ, APICS practices
 History
 CAI (70’s)
 Data Fusion / Context Activated Memory Device (80’s)
 Data Warehouse, metadata, ERP (90’s)
 Cybersecurity, analytics (2000 – present)

Selected Liaison Groups
 NIST (mostly 1:1 contacts, catalog of cited SPs and standards)
 IEEE P2675 Security for DevOps
 IEEE P1915.1 NFV and SDN Security, 5G (1:1 via AT&T)
 IEEE P7000-P7010 (S&P in robotics: algorithms, student data, safety & resilience, etc.)
 ISO 20546 20547 Big Data
 IEEE Product Safety Engineering Society
 IEEE Reliability Engineering
 IEEE Society for Social Implications of Technology
 HL7 FHIR Security Audit WG
 Cloud Native SAFE Computing (Kubernetes-centric)
 Academic cryptography experts

“Minority Report” (2002)
 The “PreCogs” have landed
 Proprietary predictive models already deployed in several
states for
 Law enforcement
 Child welfare
 “Pockets of poverty” identification
 Educational / teacher assessment
 Credit: Philip K. Dick (1956)

Ethical issues Already in Play
 Sustainability
 Environment
 Climate Change (*data center power consumption)
 Bias concerns in gender, race, free speech
 Social media technology responsibility
 As propaganda platforms
 Excessive use of cell phones by children: ADHD?
 Weakened critical thinking, F2F social skills (Sherry Turkle Reclaiming Conversation 2015)

IEEE P7000: Marquis Group Charter
“Scope: The standard establishes a process model by which engineers and technologists can address
ethical consideration throughout the various stages of system initiation, analysis and design.
Expected process requirements include management and engineering view of new IT product
development, computer ethics and IT system design, value-sensitive design, and, stakeholder
involvement in ethical IT system design. . .. The purpose of this standard is to enable the pragmatic
application of this type of Value-Based System Design methodology which demonstrates that
conceptual analysis of values and an extensive feasibility analysis can help to refine ethical system
requirements in systems and software life cycles.”

Related IEEE P70nn Groups
 IEEE P7000 Ethical Systems Design
 IEEE P7001 Transparency of Autonomous Systems
 IEEE P7002 Data Privacy Process
 IEEE P7003 Algorithmic Bias Considerations
 IEEE P7004 Standard for Child and Student Data Governance
 IEEE P7005 Standard for Transparent Employer Data Governance
 IEEE P7006 Standard for Personal AI Agent
 IEEE P7007 Ontological Standard for Ethically Driven Robotics and Automation Systems
 IEEE P7008 -Standard for Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems
 IEEE P7009 Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems
 IEEE P7010 Wellbeing Metrics Standard for Ethical Artificial Intelligence and Autonomous Systems
 IEEE P7011 SSIE Standard for Trustworthiness of News Media
 IEEE P7012 SSIE Machine Readable Personal Privacy Terms
 IEEE P7013 Facial Analysis

Key References
Focus: artificial intelligence
and autonomous systems.
Havens asks, “How will
machines know what we
value if we don’t know
ourselves?”

Recent Case Study Opportunities
“Faster, Higher, Farther chronicles a corporate
scandal that rivals those at Enron and Lehman
Brothers—one that will cost Volkswagen more
than $22 billion in fines and settlements.” –
Publisher

Case Study 2
“Equifax said that about 38,000 driver's
licenses and 3,200 passports details had
been uploaded to the portal that had was
hacked. (http://bit.ly/2jF3VTh) Equifax said
in September that hackers had stolen
personally identifiable information of U.S.,
British and Canadian consumers. The
company confirmed that information on
about 146.6 million names, 146.6 million
dates of birth, 145.5 million social security
numbers, 99 million address information
and 209,000 payment card number and
expiration date, were stolen in the cyber
security incident.” –Yahoo Finance

Case Study 3
It will be remembered as “a breach,” but the Facebook –
Cambridge Analytica incident was about supply chain big data.
Adjectives to
remember:
“Tiny” + “Big”

Case Study 4
Finding: Hispanic-owned and managed Airbnb properties, controlled for other
aspects, receive less revenue than other groups.
Response from Airbnb when contacted by reporters: We already provide tools
to help price listings.
Source: American Public Media Marketplace 8-May-2018
Related story: Dan Gorenstein, “Airbnb cracks down on bias – but at what cost?” Marketplace, 2018-09-08.

Case Study 5
A “charity” was used to subsidize
payments to Medicare patients in order
to boost drug sales. Multiple
manufacturers were involved.

Case Study 6
The US FTC Fair Credit Reporting Act requires that customers receive an explanation when credit will
not be extended by a lender.
Fact: Many lenders are using ML and algorithms to make such decisions in real time.

Case Study 7
“. . . Artificial intelligence. Mr. Zuckerberg’s vision, which
the committee members seemed to accept, was that
soon enough, Facebook’s A.I. programs would be able
to detect fake news, distinguishing it from more reliable
information on the platform. With midterms
approaching, along with the worrisome prospect that
fake news could once again influence our elections, we
wish we could say we share Mr. Zuckerberg’s optimism.
But in the near term we don’t find his vision plausible.
Decades from now, it may be possible to automate the
detection of fake news. But doing so would require a
number of major advances in A.I., taking us far beyond
what has so far been invented.”
https://www.nytimes.com/2018/10/20/opinion/sunday/ai-fake-news-disinformation-campaigns.html

Case Study 8
“The [Google DeepMind et al. team] research
acknowledges that current "deep learning" approaches to
AI have failed to achieve the ability to even approach
human cognitive skills. Without dumping all that's been
achieved with things such as "convolutional neural
networks," or CNNs, the shining success of machine
learning, they propose ways to impart broader reasoning
skills.”

Case Study 9
“. . . By 2015, the company realized its new system was
not rating candidates for software developer jobs and
other technical posts in a gender-neutral way. That is
because Amazon’s computer models were trained to vet
applicants by observing patterns in resumes submitted to
the company over a 10-year period. Most came from
men, a reflection of male dominance across the tech
industry.”

Case Study 10
Solving Poverty through Data Science
It’s
Magic!
https://www.marketplace.org/shows/marketplace-morning-report 2018-07-30

Related IEEE Associations
Related worries and worriers

IEEE Society on Social Implications
of Technology

IEEE Product Safety Engineering Society

IEEE Reliability Society
See free reliability analytics toolkit. Some
items are useful to Big Data DevOps)
https://kbros.co/2rugRij

Who is IEEE SA?
Why care what it does?
• Affordable, volunteer-driven, int’l
• IEEE SA members voting rights
• Collaboration with ISO, NIST
• Key standards include ethernet

But this is an ASQ Symposium!
 IEEE limitations:
 IEEE Active communities are small.
 Standards documents are not free, though participation for IEEE members is.
 Heavily weighted toward late career participants.
 Despite “Engineering” in title, often not “engineering.”

But IEEE has . . .
 IEEE Digital Library (with cross reference to ACM digital library)
 Multinational reach and engagement
 Reasonable internal advocacy and oversight
 Diversity
 Sometimes good awareness of NIST work
 Often best work in lesser-known conference publications (e.g., vs. IEEE Security)

State of Computing Profession Ethics
@ACM_Ethics
ACM Code of Ethics (Draft 3, 2018)
https://www.acm.org/about-acm/code-of-ethics

Highlights of ACM Ethics v3
 “minimize negative consequences of computing, including threats to health, safety, personal
security, and privacy.”
 When the interests of multiple groups conflict, the needs of the least advantaged should be given
increased attention and priority
 Computing professionals should promote environmental sustainability both locally and globally
(Conference theme!).
 “. . .the consequences of emergent systems and data aggregation should be carefully analyzed.
Those involved with pervasive or infrastructure systems should also consider Principle 3.7
(Standard of care when a system is integrated into the infrastructure of society).

Highlights:Joint ACM IEEE
Software Engr Code of Ethics
https://www.computer.org/web/education/code-of-ethics
 Software engineers shall act consistently with the public interest.
 Approve software only if they have a well-founded belief that it is safe, meets specifications, passes appropriate tests, and
does not diminish quality of life, diminish privacy or harm the environment. The ultimate effect of the work should be to
the public good.
 Be fair and avoid deception in all statements, particularly public ones, concerning software or related documents,
methods and tools.
 Consider issues of physical disabilities, allocation of resources, economic disadvantage and other factors that can diminish
access to the benefits of software.
 Identify, document, and report significant issues of social concern, of which they are aware, in software or related
documents, to the employer or the client.
 Strive for high quality, acceptable cost and a reasonable schedule, ensuring significant tradeoffs are clear to and
accepted by the employer and the client, and are available for consideration by the user and the public.
 Identify, define and address ethical, economic, cultural, legal and environmental issues related to work projects

Hidden: Human Computer Interaction
 NBDPWG System Communicator
 Usability for web and mobile content
 Substitutes for old school manuals
 “Privacy text” for disclosures, policy, practices
 Central to much of the click-based economy
 “User” feedback, recommendations
 Recommendation engines

Professional Pride, Public Disillusionment
Broader acceptance within IT & Evidence-based Practices
 Growth of data science inside many professions (R, Python)
 Extraordinary explosion of OSS tooling
 Big Data, ML, Real Time
 Watson, AlphaGo, Alexa “AI” (Gee Whiz factor)
Public Perspective
 “2017 was the year we fell out of love with algorithms.”
 Cambridge Analytica, Equifax

Natural Language Tooling
 Hyperlinks to artifacts
 Chatbots
 Live agent
 Speech to text support
 Text mining
 Enterprise search (workflow-enabled artifacts)
 Some of the indexed artifacts may approach big data status
 SaaS Text Analytics

Dependency Management
 Big Data configuration management
 Across organizations
 Needed for critical infrastructure
 See NIST critical sector efforts
 Dependencies may not be human-intelligible
“’Once ze rocket goes up, who cares where
it come down. That’s not my department,’
says Wernher von Braun.” – Tom Lehrer

Traceability & Requirements Engineering
 What is an ethical requirement?
Possible: big data ethical fabric (transparency, usage)
 Can you audit a requirement? What is a quality requirement?
 What is requirement traceability?

Special Populations
 Disadvantaged
 By regulation (e.g., 8A, SBIR, disability)
 By “common sense” (“fairness” and “equity”)
 By economic / sector (“underserved”)
 Internet Bandwidth inequity
 Children
 “Criminals” / Malware Designers

Algorithms
 “Why am I locked out while she is permitted?”
 “Why isn’t my FICO score changing?”
 “How can I know when I have explained our algorithm?”
 “Is there an ‘explain-ability’ metric?”
 What is different about machine-to-machine algorithms?
 “Can an algorithm be abusive?”
 “Is ‘bias’ the new breach?” https://kbros.co/2I2sxDO

“Bias is the New Breach”
“Researchers from MIT and Stanford University
tested three commercially released facial-analysis
programs from major technology companies and
will present findings that the software contains
clear skin-type and gender biases. Facial
recognition programs are good at recognizing
white males but fail embarrassingly with females
especially the darker the skin tone. The news
broke last week but will be presented in full at the
upcoming Conference on Fairness, Accountability,
and Transparency.“
https://www.cio.com/article/3256272/artificial-intelligence/in-the-ai-revolution-bias-is-the-new-
breach-how-cios-must-manage-risk.html

Algorithmic Bias Risk Management
 1. Recognize, socialize groups protected by statute (e.g.,
Equal Credit Opportunity Act)
 2. Creatively consider other affected subpopulations
 Sight impaired – other disabilities
 Children, elderly
 Unusual household settings (elder care, multi-family
housing)
 Part time and workers
 Novice vs. Experienced users
 What counterfactuals are simply not being measured?

Linkage to Privacy, Surveillance, Distrust
Ask your quality engineer to respond to this question:
“Algorithms are bad because they . . . “
 Use data without our knowledge
 Are based on incorrect or misleading knowledge about us
 Are not accountable to individual citizens
 Are used by governments to spy on citizens
 Support drone warfare
 Are built by specialists who do what they are told without asking questions
 Represent a trend to automate jobs out of existence
 Are built by big companies with no public accountability

“When we fell out of love with algorithms.”

Audience, Alerts, Audits: Monitoring
 Who is the audience for a product or service? (Out of regular coffee in our meeting room)
 Who should be alerted, and for what, and how often?
 Even if they have opted out?
 What should be audited?
 What thresholds are appropriate for cost, timetable, risk?

Decisions vs. Decision Support:
Application Areas
Human-Computer Interactions in Decision-making

Undermining Specialists*
“The threat the electronic health records
and machine learning post for physicians’
clinical judgment – and their well-being.” – NYT
2018-05-16
“’Food poisoning’ was diagnosed because
the strangulated hernia in the groin was
overlooked, or patients were sent to the
catheterization lab for chest pain because
no one saw the shingles rash on the left
chest.”
*Or adversely changing specialist behavior.

“Rote Decision-Making”
“The authors, both emergency room physicians at
Brigham and Women’s Hospital in Boston, do a fine job
of sorting through most of the serious problems in
American medicine today, including the costs, over-
testing, overprescribing, overlitigation and general
depersonalization. All are caused at least in part, they
argue, by the increasing use of algorithms in medical
care.” -NYT 2018-04-01

Facial Recognition for Law Enforcement
 “AMZ touts its Rekognition facial recognition system as ‘simple and easy to
use,’ encouraging customers to ‘detect, analyze, and compare faces for a
wide variety of user verification, people counting, and public safety use
cases.’ And yet, in a study released Thursday by the American Civil Liberties
Union, the technology managed to confuse photos of 28 members of
Congress with publicly available mug shots. Given that Amazon actively
markets Rekognition to law enforcement agencies across the US, that’s
simply not good enough. The ACLU study also illustrated the racial bias that
plagues facial recognition today. ‘Nearly 40 percent of Rekognition’s false
matches in our test were of people of color, even though they make up only
20 percent of Congress,’ wrote ACLU attorney Jacob Snow. ‘People of color
are already disproportionately harmed by police practices, and it’s easy to
see how Rekognition could exacerbate that.’“ -Wired 2018-07-26

“Family” Impacts
“Charges of faulty forecasts have accompanied the
emergence of predictive analytics into public policy.
And when it comes to criminal justice, where
analytics are now entrenched as a tool for judges
and parole boards, even larger complaints have
arisen about the secrecy surrounding the workings
of the algorithms themselves — most of which are
developed, marketed and closely guarded by private
firms. That’s a chief objection lodged against two
Florida companies: Eckerd Connects, a nonprofit,
and its for-profit partner, MindShare Technology.” –
NYT “Can an algorithm tell when kids are in danger?” 2018-01-02

Lawsuit over Teacher Evaluation Algorithm
 Value-added measures for teacher evaluation, called the Education Value-
Added Assessment System, or EVAAS, in Houston, is a statistical method
that uses a student’s performance on prior standardized tests to predict
academic growth in the current year. This methodology—derided as
deeply flawed, unfair and incomprehensible—was used to make decisions
about teacher evaluation, bonuses and termination. It uses a secret
computer program based on an inexplicable algorithm (above).
 In May 2014, seven Houston teachers and the Houston Federation of
Teachers brought an unprecedented federal lawsuit to end the policy,
saying it reduced education to a test score, didn’t help improve teaching or
learning, and ruined teachers’ careers when they were incorrectly
terminated. Neither HISD nor its contractor allowed teachers access to the
data or computer algorithms so that they could test or challenge the
legitimacy of the scores, creating a ‘black box.’” http://kbros.co/2EvxjU9

Wells Fargo Credit Denial “Glitch”
Mark Underwood @knowlengr | Views my own | Creative Commons | *Thru 2018-08 | v1.4
CNN: “Hundreds of people
had their homes foreclosed
on after software used by
Wells Fargo incorrectly
denied them mortgage
modifications.” 2018-08-05
https://money.cnn.com/2018/08/04/news/companies/wells-fargo-mortgage-modification/index.html

. . . All this is not easy to “fix”
Risk mitigation for data science implementations is relatively immature.

Unintended Use Cases or Ethical Lapse?
• Algorithm corrected for color bias, but can
now be used for profiling
• “Red Teaming” or “Abuse User Stories” can
help
• Unintended use cases call for a safety vs. a
pure “assurance” framework

“Lite” AI Security/Reliability Frameworks
https://motherboard.vice.com/en_us/article/bjbxbz/researchers-tricked-ai-into-doing-free-computations-it-wasnt-trained-to-do
“Google researchers demonstrated that a
neural network could be tricked into
performing free computations for an
attacker. They worry that this could one
day be used to turn our smartphones into
botnets by exposing them to images.”

XAI: Explain, Interpret, Narrate,
Translate
The elusive holy grail of Transparency

Challenges of Interpretability
“Adversarial ML literature
suggests that ML models are
very easy to fool and even
linear models work in counter-
intuitive ways.” (Selvaraju et al, 2016)
• Reproducability
• Training sets including results of
other analytics (e.g., FICO)
• Provenance (think IoT)
• Opaque statistical issues

Transparency
 What does it mean to be “transparent” about ethics?
 What connection to IEEE /ACM / ASQ professional ethics?
 ASQ: “Be truthful and transparent in all professional interactions and activities.” https://asq.org/about-asq/code-of-ethics
 ACM: “The entire computing profession benefits when the ethical decision making process is accountable to and
transparent to all stakeholders. Open discussions about ethical issues promotes this accountability and transparency.”
 ACM “A computing professional should be transparent and provide full disclosure of all pertinent system limitations and
potential problems. Making deliberately false or misleading claims, fabricating or falsifying data, and other dishonest
conduct are violations of the Code.”
 ACM “Computing professionals should establish transparent policies and procedures that allow individuals to give
informed consent to automatic data collection, review their personal data, correct inaccuracies, and, where appropriate,
remove data.”
 ACM “Organizational procedures and attitudes oriented toward quality, transparency, and the welfare of society reduce
harm to the public and raise awareness of the influence of technology in our lives. Therefore, leaders should encourage
full participation of all computing professionals in meeting social responsibilities and discourage tendencies to do
otherwise.”

Transparency & Professional Ethics
 What connection to IEEE /ACM /ASQ professional ethics?
 ASQ: “. . . Fairness . . . Hold paramount the safety, health, and welfare of individuals, the public, and the environment.”
 ACM: “The entire computing profession benefits when the ethical decision making process is accountable to and
transparent to all stakeholders. Open discussions about ethical issues promotes this accountability and transparency.”
 ACM “A computing professional should be transparent and provide full disclosure of all pertinent system limitations and
potential problems. Making deliberately false or misleading claims, fabricating or falsifying data, and other dishonest
conduct are violations of the Code.”
 ACM “Computing professionals should establish transparent policies and procedures that allow individuals to give
informed consent to automatic data collection, review their personal data, correct inaccuracies, and, where appropriate,
remove data.”
 ACM “Organizational procedures and attitudes oriented toward quality, transparency, and the welfare of society reduce
harm to the public and raise awareness of the influence of technology in our lives. Therefore, leaders should encourage
full participation of all computing professionals in meeting social responsibilities and discourage tendencies to do
otherwise.”

Transparency General Challenges
 Some data, algorithms are intellectual property
 Some training data includes PII
 Predictive analytical models are often “point in time”
 “Transparent” according to whose definition?
 Should algorithms have “opt-in?” Can they?
 Training set big data variety reidentification risks
 What quality spectra exist for transparency? A quality BoK for transparency?

Explainability / Interpretability
“[We need to] find ways of making techniques like
deep learning more understandable to their creators
and accountable to their users. Otherwise it will be
hard to predict when failures might occur—and it’s
inevitable they will. That’s one reason Nvidia’s car is
still experimental.”

“Fairness Flow”:
But will you share your ethics guidance?
https://www.cnet.com/news/facebook-starts-building-ai-with-an-ethical-compass/
“Bin Yu, a professor at UC Berkeley, says
the tools from Facebook and Microsoft
seem like a step in the right direction,
but may not be enough. She suggests
that big companies should have outside
experts audit their algorithms in order
to prove they are not biased. ‘Someone
else has to investigate Facebook's
algorithms—they can't be a secret to
everyone,” Yu says.’”
-Technology Review 2018-05-25

Decision Support for Bias Detection
“Things like transparency, intelligibility, and explanation are new enough
to the field that few of us have sufficient experience to know everything
we should look for and all the ways that bias might lurk in our models,”
says Rich Caruna, a senior researcher at Microsoft who is working on
the bias-detection dashboard.”
Technology Review, Will Knight 2018-05-25

Insights from More Mature Settings
 AI Analytics for distributed military coalitions
 “. . . Research has recently started to address such concerns and
prominent directions include explainable AI [4], quantification of
input influence in machine learning algorithms [5], ethics
embedding in decision support systems [6], “interruptability” for
machine learning systems [7], and data transparency [8]. “
 “. . . devices that manage themselves and generate their own
management policies, discussing the similarities between such
systems and Skynet.”
S. Calo, D. Verma, E. Bertino, J. Ingham, and G. Cirincione, "How to prevent skynet
from forming (a perspective from Policy-Based autonomic device management),"
in 2018 IEEE 38th International Conference on Distributed Computing Systems
(ICDCS), Jul. 2018, pp. 1369-1376. [Online]. Available:
http://dx.doi.org/10.1109/ICDCS.2018.00137

Enterprise Level Risk
 Impact on reputation
 Litigation
 Unintentionally reveal sources, methods, data / interrupted data streams (e.g, web)
 Loss of consumer confidence, impact on public safety
 Misapplication of internally developed models
 Financial losses from data science #fail
 “. . . as long as our training is in the form of someone lecturing about the basics of gender or
racial bias in society, that training is not likely to be effective”.
Dr. Hanie Sedghi, Research Scientist, Google Brain

Corporate Initiatives
 Environmental Social Governance
 What does quality mean in enterprise sustainability?
 Where if there is only lip service to sustainability or quality?
 Transparency within employee groups, departments, subsidiaries (See P7005)
 Computing decisions that affect carbon footprint (green data centers, etc.)

ISO 26000
 “ISO 26000 is the international standard
developed to help organizations effectively
assess and address those social responsibilities
that are relevant and significant to their mission
and vision; operations and processes; customers,
employees, communities, and other
stakeholders; and environmental impact.”

Related Work
 NIST 800-53 Rev 5 and others, NIST Cloud Security
 Building, Auto Automation ISO 29481, 16739, 12006
 https://www.buildingsmart.org/about/what-is-openbim/ifc-introduction
 Uptane
 Ethics and Societal Considerations ISO 26000, IEEE P700x
 DevOps Security IEEE P2675
 Microsegmentation and NFV IEEE P1915.1
 Safety orientation
 Infrastructure as code
 E.g., security tooling is code, playbooks are code

Selected Software Engineering References
Bo Brinkman, Catherine Flick, Don Gotterbarn, Keith Miller, Kate Vazansky, and Marty J. Wolf. 2017.
Listening to professional voices: draft 2 of the ACM code of ethics and professional
conduct. Commun. ACM 60, 5 (April 2017), 105-111. DOI: https://doi.org/10.1145/3072528

Stepping on the quality scales
Beyond ISO 9001

Quality Engineer as Camera Lens
“So how big is the difference between a lens that costs a few hundred dollars, and one costing over
a thousand dollars more? What kinds of gains does your money buy? Are the quality improvements
substantial enough to be noticed by the untrained eye?” (Richard Baguley, Wired 2014-06-13)
https://www.wired.com/2014/06/hi-lo-dslr-lenses/
If a quality engineer more fully pursues her goals, would an
enterprise’s moral compass be more finely tuned?

Quality Touchpoints
 Requirements development
 Requirements adherence
 Measurement frameworks
 Traceability / integrity
 Multiple overlapping frameworks (social, environmental, psychological, enterprise, regulatory. . . )

Current Challenges
 Stop-to-test paradigm often fails
 Streaming data quality models are ahead of current quality teaching / practice
 AI – for – quality
 AI measurement
 AI test generation
 AI data / sensor simulation, scalability
 Quality of XAI by Audience / Enterprise

Agile development & quality engineering
 “[Studies] indicate that there is a significant
correlation between the inclusion of ethical
tools in the process of planning in Agile
methodologies and the achievement of
improved performance in three quality
parameters: schedule, product functionality
and cost. “

Selected Quality References
H. Abdulhalim, Y. Lurie, and S. Mark, "Ethics as a quality driver in agile software projects," Journal of
Service Science and Management, vol. 11, no. 1, pp. 13-25, 2018. [Online]. Available:
http://dx.doi.org/10.4236/jssm.2018.111002

Use Cases
 Network Protection
 Systems Health & Management (AWS metrics, billing, performance)
 Education
 Cargo Shipping
 Aviation (safety)
 UAV, UGV regulation
 Regulated Government Privacy (FERPA, HIPAA, COPPA, GDPR, PCI etc.)
 Healthcare Consent Models
 HL7 FHIR Security and Privacy link

A Final Rationale
“What, me quality
engineer worry?”

• Co-Chair NIST Big Data Public WG Security & Privacy subgroup https://bigdatawg.nist.gov/
• Chair Ontology / Taxonomy subgroup for IEEE P7000. Occasional participant in IEEE Standards
WGs P7007, P7003, P7002, P7004, P7010
• IEEE Standard P1915.1 Standard for Software Defined Networking and Network Function
Virtualization Security (member)
• IEEE Standard P2675 WG Security for DevOps (member)
• Current: Finance, large enterprise: supply chain risk, complex playbooks, many InfoSec tools,
workflow automation, big data logging; risks include fraud and regulatory #fail
• Authored chapter “Big Data Complex Event Processing for Internet of Things Provenance:
Benefits for Audit, Forensics, and Safety” in Cyber-Assurance for IoT (Wiley, 2017)
https://kbros.co/2GNVHBv
• @knowlengr dark@computer.org knowlengr.com https://linkedin.com/in/knowlengr
About Me

Background Material
NBDPWG Appendix A, Cloud Native SAFE

ACM Computing Classification
Security & Privacy Topics
 Database and storage security
 Data anonymization and sanitation
 Management and querying of encrypted data
 Information accountability and usage control
 Database activity monitoring
 Software and application security
 Software security engineering
 Web application security
 Social network security and privacy
 Domain-specific security and privacy architectures
 Software reverse engineering
 Human and societal aspects of security and privacy
 Economics of security and privacy
 Social aspects of security and privacy
 Privacy protections
 Usability in security and privacy

CRISP-DM Process Model

Cloud Native Foundation
Safe Access For Everyone (SAFE)
 https://github.com/cn-security/safe

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Editor's Notes