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By Kate Carruthers in ideas —

Some thoughts on digital and data Ethics

Some thoughts on digital and data Ethics
ethics-centre-quote
The Ethics Centre

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

  1. Data is not neutral - that is all data is subject to bias
  2. 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
  3. 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
  4. Data is not inherently smart - it is our interpretation of data that adds value
  5. 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.

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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


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:

Some new ways to think about digital and data ethics

  • 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

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