My colleagues, Selena Griffith and Martin Bliemel, and I have launched a new website for our book. It has sections on:
Published: Visual Tools for Developing Cross-Disciplinary Collaboration, Innovation and Entrepreneurship Capacity
For the last couple of years I have been working on a book with Selena Griffith, Martin Bliemel and a long list of wonderfully creative and innovative authors from across the globe. Today it has been released for sale in digital and hard copy.
Visual Tools for Developing Cross-Disciplinary Collaboration, Innovation and Entrepreneurship Capacity identifies and documents pedagogical and practice-based visual approaches to scaffolding and developing these capacities in your classes, with your clients or in your teams. The editors have selected a diverse range of best practice case studies and theoretical frameworks from leading international educators and practitioners across a broad range of disciplines to illustrate how visual tools can be used to greatest effect.
Divided into four logically sequenced sections, the book will progressively build upon the array of visual tools you can employ in your practice. Initially starting with tools for collaboration it expands to include ways to overcome the challenges of cross-disciplinary collaboration. Building on this foundation you will then explore visual tools for stimulating and supporting Innovation in classrooms, with clients and customers, or your team. The third section introduces strategies for selecting visual tools to aid in Entrepreneurship and entrepreneurial activities. The final section provides you with case studies of fully integrated practice where teams have collaborated to innovate and bring the resultant outputs to market. Visual tools for Developing Cross-Disciplinary Collaboration, Innovation and Entrepreneurship Capacity is the perfect companion for an educator, facilitator or practitioner to help students, clients or teams maximize their potential through the use of visual tools. Read cover to cover or dip in as you need to.
You can order the book at this link.
Huge thanks to Vaughan Rees and Arianne Rourke for their series curation. And to all our Authors
Visual tools for developing student capacity for cross-disciplinary collaboration, innovation and entrepreneurship
Very happy that our book is finally being published – huge thanks to my wonderful co-conspirators, co-editors and co-authors – Selena Griffith and Martin Bliemel. 
Visual tools for developing student capacity for cross-disciplinary collaboration, innovation and entrepreneurship
Common Ground Research Networks, Champaign, IL 2018
“Visual tools for developing cross-disciplinary collaboration, innovation and entrepreneurship capacity identifies and documents pedagogical and practice-based visual approaches to scaffolding and developing these capacities in your classes, with your clients or in your teams.
Divided into four logically sequenced sections, it will progressively build upon an array of visual tools to aid your practice. Initially starting with collaboration it expands to include cross-disciplinary collaboration.
Building on this foundation you will then explore visual methods for Innovation, followed by Entrepreneurship. The final section provides case studies of fully integrated practice.
The perfect companion for an educator, facilitator or practitioner to help students, clients or teams maximize their potential through the use of visual tools.
Contributing authors include in international array of leading educators and practitioners from a diverse range of disciplines.”
Info sec, AI and ethics – some thoughts #codemesh
I’m heading off to speak at the CodeMesh Conference in London shortly and I’ve been thinking about the emerging boundaries between information security, AI and ethics. I will post some thoughts as they evolve.
Developers (and others) and ethical approaches
We need to help everyone, from coders through info sec professionals to senior organisational leaders, to understand that information security, AI and ethics are part of the everyday landscape for everyone now. It is no longer something that someone else does and it needs to become embedded into our everyday practices.
Nobody has all of the answers, and nobody even has all of the questions. But this intersection between information security, privacy, AI and ethics is becoming increasingly important as we start to think about the kind of future we are building. We need to think about to create the kind of future we want and not merely wander blindly into some kind of dystopian future.
In particular, ethics is an area that we do fairly well in academic research. Universities have well-established ethics processes and there is a high level of consciousness among researchers of its importance. But in business this is not even a secondary consideration. There is general theoretical agreement that everyone ought to take an ethical approach to their work, but it is not always welcome in practice. And yet business folks have a part to play in creating ethical workplaces. We all do.
In software development some of the practices that have been proposed – things like Privacy by Design or Security by Design – are interesting, yet I’ve not seen either in the wild. These are sensible approaches, and Privacy by Design is even part of GDPR so it might even work (eventually). Yet neither of these explicitly focuses on ethics.
And all of this is not much help when a developer is approached by a business person and is asked to develop something that might be ethically a bit shady. Look at the example of the developer for Volkswagen who went to prison for his role in creating software to deceive regulators around the world. There can be real world consequences for poor ethical decision making in the workplace.
VW engineer sentenced to 40-month prison term in diesel case: [he] was a “pivotal figure” in designing the systems used to make Volkswagen diesels appear to comply with U.S. pollution standards, when instead they could emit up to 40 times the allowed levels of smog-forming compounds in normal driving. – Reuters 26 Aug 2017
It all seems to point to a need to develop ways for business people to run an ethical lens over their ideas way earlier than when they approach a developer.
One approach that has merit is something like the Ethics Canvas, which is inspired by notions like the Lean Canvas or the Business Model Canvas. A simple and easy to use tool such as this could provide business folks with a way to consider the ethical implications of things that they ask developers to do. I’ve started to use the Ethics Canvas at work in some projects, it will be interesting to see how it goes.
Header image: By Martin420 [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)%5D, from Wikimedia Commons
Future proof your career – some tips for women
Spoke at a women in technology conference recently on the topic of how to future proof your career. It might seem strange that I hardly mentioned technology at all in this talk. But the essentials for a long career are mostly outside of technology. Any intelligent person can pick up technology skills, but other essential skills include:
- Self analysis and self understanding
- NO to office housework
- Support networks
- Power of sponsorship
- Impostor syndrome
- And lastly, actual technology
Self analysis and self understanding
Self analysis and self understanding are the first thing to consider. If you can get a clear eyed understanding of your strengths and weaknesses this provides a good foundation for managing your career. This will also help with the development of the ability to take rejection, to ignore it, and to move on regardless. You will be rejected – sometimes because you are a woman, sometimes for other reasons. But if you have clear idea of your skills and the value that you provide then you can pick yourself up, regroup and get on with things.
Resilience counts – a career is a marathon not a sprint!
Another key skill to develop is not to take things personally at work. I always imagine that they are talking to the chair when someone appears to be having a go at me. Many times I have found out later that the person who was giving me a hard time was upset about something completely different. Of course there can be the case where someone really is out to get you – I’ve suffered from bullying at work before – the best thing to do in that case is to engineer an exit as soon as possible.
‘Wisdom is the principal thing; therefore get wisdom: and with all your getting get understanding.’ Proverbs 4:7
Take every opportunity to obtain a 360 degree understanding of yourself. And ensure that you seek feedback from diverse and reliable sources. Take time out to reflect on your work and assess it dispassionately. Learn to recognise your strengths and weaknesses, so that you can use your strengths and improve on your weaknesses. I often say that it’s not a weakness if you know about it and can compensate.
Say ‘NO’ to office housework
Office housework consists of the myriad of little tasks around the office that folks just assume that women will do. These range from filling the dishwasher, to tidying the bench tops, buying the birthday cakes and opening the mail. People will unconsciously expect women to do it.
- Stop doing it immediately (unless, of course, you really really want to do it)
- It takes time for which you are not remunerated
- It distracts you from your mission
If you consider how much of this you’ve probably done over your career it really adds up over the years. And our male colleagues have been blissfully ignoring all of this unremunerated labour and coasting past us.
Find your support network
Find a work support network. Often social friends will not understand your work context and you do need someone who understands. I’ve had conversions with family members as I try to explain some work thing and watch their eyes glaze over, or they simply do not have the mental map for my work. The main thing is that you need a cheer squad of folks who understand your work context and who can also provide contextually relevant advice.
Another sad fact is that sometimes colleagues at work will diverge as you progress in your career. This is especially true if you have risen up the ranks within a single organisation. In that case there might be envy or resentment, so you might need to socialise with different folks.
Discover the power of sponsorship
Sponsorship not mentorship. As noted in this article:
‘Mentors advise. Sponsors act.’
A sponsor is someone who will advocate on your behalf. Mentors are helpful when you need advice, but to really get ahead a sponsor can be more useful. It is helpful to have mentoring throughout your career, but a sponsor can help to make things happen and be real change agent for your career.
Nobody knows you feel like an impostor
Impostor syndrome is real, and almost everyone experiences it at some time. But the important thing to remember is that nobody knows how you feel so heed the advice to ‘fake it until you make it’ or rather ‘fake it until you become it’ as Amy Cuddy argues.
The crucial thing about imposter syndrome is that there is clear evidence that you are not an imposter, that is, someone has given you a particular role. You have the feeling that you are an imposter but there is evidence that you are not.
The Dunning–Kruger effect is a cognitive bias wherein persons of low ability suffer from illusory superiority, mistakenly assessing their cognitive ability as greater than it is. Therefore by having imposter syndrome at least you know you’re not suffering from the Dunning-Kruger effect.
New jobs will emerge as technology changes
The 21st century offers us great challenges and opportunities. Things like the digital revolution, AI, machine learning, Internet of Things, big data and data science, and Quantum computing – among others – are all going to change things beyond recognition. Always be looking to generalise your current skillset into the next big thing, and keep in mind Amara’s law:
‘We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.’
As Paul Roehrig, chief strategy officer for Cognizant Digital Business, notes:
‘People skills are more and more important in an era where we have powerful and pervasive technology,’ he says. ‘It sounds counterintuitive, but to beat the bot, you need to be more human.’
21st Century skills include:
- Problem solving
- Creativity
- Analytic thinking
- Collaboration
- Communication
- Ethics, action, and accountability
Above all…
You can have multiple careers
Don’t panic!
Remember to breathe
Data is the engine of the fourth industrial revolution
“Data is the new oil and we are in the midst of the fourth industrial revolution that is driven by the internet of things. The old fossil fuelled industrial revolutions are in their dying days and we are seeing the birth of a new era that will reshape everything that we know.”
– Kate Carruthers
Last week at the 2018 Stanford University Women in Data Science Conference in UTS Sydney I spoke on a panel along with Theresa Anderson, Ethel Karskens, Nicole Dyson , Aurelie Jacquet, Joanne Cooper, and Angela Chin.
As first speaker I got to set the scene for the remaining speakers, here is a summary of my remarks.
My remarks
One thought to start with. I am currently the Chief Data & Analytics Officer at UNSW Sydney, and this job did not exist when I left school, and this job did not exist when I graduated from university. So, do not worry about educating kids for the jobs of the future when we probably cannot even imagine what those jobs will be. We’re at an exciting point now where people can’t be trained for the jobs they’ll have in the future because they don’t exist yet. Of all the things that I have studied, history, philosophy and anthropology have been among the most useful. And they have actually been a good grounding for an unknown future.
Data is the new oil and we are in the midst of the fourth industrial revolution that is driven by the internet of things. The old fossil fuelled industrial revolutions are in their dying days and we are seeing the birth of a new era that will reshape everything that we know. We’ve had industrial revolutions before, this is just the next one.
Data scientists are the currently the new high priests, but not for long, as algorithms take over from them. Data engineers are the new coal miners, preparing the data ready for use in new applications which are only now emerging.
This is the next stage of the digital revolution. It includes VR/AR and the internet of things. Everything will change. Things that were impossible will become possible.
Things that will change
Among the things that will change are:
- Education is on the brink of changes, and it will make the way that we were educated so divergent from the modern world. Technologies such as VR and AR will drive change in the place and nature of education and the role of educators are shifting from chalk and talk to technology and facilitation of discovery.
- Science and Engineering will bring us new technologies such as quantum computing and CRISPR-Cas9 genome editing technology that will revolutionise everyday life.
- Medicine is at the start of a new world of genomic medicine powered by data, AI and machine learning.
- Home life with intelligent devices like Google Home and Alexa are reshaping how we manage our homes.
- Autonomous vehicles are becoming a reality faster than I had imagined a few years back.
- Jobs will change – some jobs will go, for example truck drivers, and new jobs will emerge as things like autonomous vehicles become the norm.
All of this is powered by data and enabled by the internet of things.
The people who are educated in data will be well placed in this new economy. Data science and cybersecurity grads will be well placed, and we are already seeing this in the graduate outcomes.
Challenges
We still face big challenges with things such as privacy and identity management. There is still no one ring to rule all in identity. The threats to privacy and data security are increasing. With biometric data being stored by companies in the cloud, our identities with our unchangeable features such as voice and finger prints, are now more at risk than ever.
Also, we face threats such as the increasing corporatisation and creation of proprietary goods from our data. As folks say if you’re not paying then you are the product.
And this means that old fashioned things like ethics will become increasingly important in both education and in business.
I’m particularly (and increasingly) interested in digital ethics. I think that we will need to develop customary practices that embed ethics into software development. Ideas like privacy by design and security by design will need to become commonplace.
There are huge opportunities offered by this new industrial revolution. There will be winner and losers. And the higher education sector has an important role in both inventing this future and in preparing young people to be a part of it. It is certainly an interesting time to be alive.
Thank you.
Image: By DARPA (Defense Advanced Research Projects Agency (DARPA)) [Public domain], via Wikimedia Commons
Data For Public Good
I’ve been meaning to share this discussion for ages: from the Constellation Research Conference last year on Data For Public Good.
Data data data everywhere but what to do with the deluge?
It was a wideranging discussion about how to extract the signal from the noise and ponder how data can be used for the public good. The panel discussed the power of data for health care, public sector, education, and society, and how organistions can tap in to the power of data and do good. It is clear that there is no guarantee that data will do good without our help.
Moderator: Doug Henschen
Chief Data Officer at UNSW Australia: Kate Carruthers
Director at NDSSL, Biocomplexity Institute of Virginia Tech: Madhav Marathe
Principal Digital Architect, ASRC/Federal Communications Commission: Andrew Nebus
Executive Exchange – Data For Public Good from Constellation Research on Vimeo.
Some thoughts on digital and data Ethics
‘We ask ethical questions whenever we think about how we should act. Being ethical is a part of what defines us as human beings.’
The Ethics Centre, Sydney
Humans have been thinking about the moral principles that govern our behaviour or the way in which we conduct ourselves for aeons. We are moving at lightspeed towards a new and exciting future that is built on algorithms, data, and digital technologies. Ethics is an area of increasing importance since we are barreling forward with the proliferation of data through digital and IoT and there seems to be little opportunity to slow things down.
I’ve been thinking about digital and data ethics since I joined Steve Wilson, David Bray, John Taschek, and R “Ray” Wang on a Digital Ethics for the Future panel with in 2016.
5 propositions about data
- Data is not neutral – that is all data is subject to bias
- There is no such thing as raw data – that is, by the simple mechanism of selecting data, you have exercised judgment as to which data to include or exclude
- The signal to noise ratio has changed – we now have so much data that there is more noise than signal and it gets difficult to ascertain what is the signal
- Data is not inherently smart – it is our interpretation of data that adds value
- The more data we have the less anonymity – thus it becomes increasingly difficult to avoid identification
Why this is important
There have been numerous examples of data breaches for example the Australian Red Cross and the nation of Sweden. Every data breach is the result of some defect in the design, development or deployment of the technology. These breaches could be prevented by means of including some ethical frameworks into the design, build and deployment phases.
By the way, the World’s Biggest Data Breaches visualisation tool provides an excellent and mesmerising way to explore data breaches.
It is also interesting to recall the ease with which Microsoft’s Tay Twitter bot was trained to become rather nasty very quickly. Thus demonstrating the need to be sure of the training data one uses and to ponder the potential consequences of design and deployment decisions: Twitter taught Microsoft’s AI chatbot to be a racist asshole in less than a day.
And there is the recent example of bathroom soap dispensers having been designed to recognise white hands not coloured ones. This is obvious bias from the design and development team, and an example of why diversity in teams is critical. The fact the average developer is white male means that it is likely that every design has as its default setting as a white male.
The issues of bias – both unconscious and conscious – are enormous.
Data is increasing at a vast rate, as demonstrated by this chart from the IDC Data Age 2025 study, and this means that we need to develop ethical frameworks to support the acquisition, management and analysis of large datasets .
Some existing approaches
Universities have a long history in managing ethics, but even they are struggling with the implications of the complex data sets and algorithms that they are dealing with.
Over the years the ICT industry has developed a number of codes of ethics and codes for professional practice, yet many developers and data scientists are mostly unaware of these. Some examples of these codes of practice include:
But realistically, if developers have not even heard of these codes then how can they possibly influence the design of solutions that avoid bias and other ethical issues?
Some newer approaches
“Privacy is an inherent human right, and a requirement for maintaining the human condition with dignity and respect.’
There are the beginnings of some new approaches, such as the Accenture: 12 guidelines for developing data ethics codes. And recent initiatives such as the OWASP Security by Design Principles and the Privacy by Design might well provide a good starting point for thinking about how we can embed good practice into the design and building of data sets and algorithms.
There is some good discussion of these issues in Floridi, Taddeo What is Data Ethics? (2016), and as they note, we need to examine ethics in terms of the following categories:
- data – including how we generate, record and share data, including issues of consent and unintended uses of the data
- algorithms – how we interpret data via artificial intelligence, machine learning and robots
- practices – devising responsible innovation and professional codes to guide this emerging science
There have been developments in the area of community based approaches to improving digital and data ethics, chiefly in the area of machine learning and AI. Here are some examples of groups working in this area:
- IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems
- Fairness, Accountability, and Transparency in Machine Learning
- Algorithmic Justice League
Some new ways to think about digital and data ethics
‘Complexity is a defining feature of the digital era, and we are not adjusting our governance structures to manage it.’
– Kent Aitken, Prime Ministers Fellow, Public Policy Forum Canada, 2017
We need to be clear that technology has no ethics. It is people who demonstrate ethics. And technology inherits the biases of its makers. We need to develop ethical frameworks and governance practices that enable us to develop solutions that are better from an ethical perspective.
I believe that if we start from the principles of Privacy by Design and Security by Design that we have a fairly firm practical basis for the future.
One thing is certain at an institutional level, information security , privacy and data governance will need more work to form a solid foundation to enable better data ethics.
References
- Causeit Data Ethics
- Digital Ethics Lab – Oxford Internet Institute – University of Oxford
- Georgetown University, Kennedy Institute of Ethics, Ethics Lab
- The BIG Data Ethics Cheat Sheet, Hackermoon
- Guidelines on Ethical Research – British Sociological Association
- What is data ethics? Luciano Floridi, Mariarosaria Taddeo. Phil. Trans. R. Soc. A 2016 374 20160360; DOI: 10.1098/rsta.2016.0360. Published 14 November 2016
- Digital Enlightenment Forum: Digital Ethics. Workshop Report. (2016, March 1). Retrieved August 16, 2017, from https://digitalenlightenment.org/sites/default/files/users/14/Digital%20Ethics%20Workshop%20Report%20v2.pdf
- A deep study on the concept of digital ethics. Maggiolini, Piercarlo. (2014). Revista de Administração de Empresas, 54(5), 585-591. https://dx.doi.org/10.1590/S0034-759020140511
- Digital Ethics. Capurro, R. (2009, December 1). Retrieved August 18, 2017, from http://www.capurro.de/korea.html
- The Eternal Value of Privacy. Schneier, B. (2006, May 18). Retrieved August 18, 2017, from https://www.schneier.com/essays/archives/2006/05/the_eternal_value_of.html
- Governance in the digital age. Aitken, K. (2017, May 23). Public Policy Forum . Retrieved August 18, 2017, from https://medium.com/@PPForum/governance-in-the-digital-age-a7edce03637d
Data governance and cybersecurity
The connection between data governance and cybersecurity might not be immediately apparent. But if one considers the ‘5 knows of cyber’, it becomes obvious that cybersecurity is all about data, and data is all about information, and we want information to be secure.
I use the ‘5 knows’ as the foundation of our data governance framework, because it really helps people to understand why data governance is 
important and how it can help them. And if people can understand the why then they can move towards controlling their data more effectively. And once we move towards managing our data then we can start to manage information.
Cybersecurity is very much a team sport, it is a collaboration between teams – Data & Information Governance, Cybersecurity, Risk Management, IT Operations, and the business units. There is no way any single group can manage security, especially with the emerging threat landscape.
But the fundamentals of data governance are an essential starting point for the collaboration:
- policies, standards, procedures and guidelines for data governance
- governance groups to coordinate activities
- data classification
- data handling guidelines
- system classification
- an information security management system
Data Governance: 5 Tips for getting started
I’ve been reflecting on the past year and one big focus area was data governance. Rolling out a data governance program along with an Information Security Management System (ISMS) is a big job for a large and complex organisation, and it is a multi-year project. We are in year two of the data governance program and over the past few years there have been a number of lessons learned.
It is all very easy to throw up one’s hands and say that it is all too hard, that data wants to be free, or that governing data is impossible. Yet to enable new ways of analysing data (and dare I say it, big data), and for effective cyber security management, we must work out how to do data governance.
Tips for getting a data governance program started
- Clarify your mandate. Get your policies and procedures sorted out early. An official policy clarifies your mandate for running the data governance program and can assist in obtaining buy-in. My starting point was a definition:
“Data governance is the organization and implementation of policies, procedures, structure, roles, and responsibilities which outline an enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets.”
Source: John Ladley, Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program, 2012
- Setup an effective governance structure. This seems like an obvious thing, but many organisations struggle with this. Getting the right structure setup and the right people involved is critical to success. I have setup a Data Governance Steering Committee (DGSC), which has oversight of the entire program, with cross-organisational executive involvement, and it has been very important in obtaining credibility. The DGSC is supported by another Committee, which takes a more hands-on day to day role in deciding how we manage data across the organisation. We also work closely with IT, Privacy, Procurement, and Legal to ensure that they are involved in the data governance program.
- Make a start. Typically in a large organisation it can be daunting to consider data governance and to know where to start. Find an area of the organisation that has some willing people and just get started. This lets you demonstrate success and leverage that success to get the next area of the organisation involved.
- Take inspiration from other organisations. Don’t feel the need to invent data governance from scratch. Talk to other practitioners – they’re usually delighted to find a fellow traveler. Find groups where data and information governance folks hang out, like The Data Governance Institute: The DGI, Information Governance ANZ or the Data Management Association Australia (DAMA). The kind folks at DG @ Stanford University were particularly helpful to me in the early days. Of course check out my Data Governance website at UNSW Sydney, where we have shared our policies, processes and practices.
- Ignore the vendors. there are a plethora of vendors who say they have solutions for data governance. Ignore them. It is not about the tools, it is about practice and culture.
