Fontys

Technology Impact Cycle Tool

Courses Login
Depending on the technology you are assessing, some categories are more important than others. Here you can indicate how important you think this category is for this technology.
If this category is not applicable for this technology, please select 'Yes.'

(Only the gray shaded text is printed on the Quick Scan canvas)

There are fundamental issues with data. For example: - Data is always subjective; - Data collections are never complete; - Correlation and causation are tricky concepts; - Data collections are often biased; - Reality is way more complex than a million datapoints; Are you aware of these issues? How does the technology take these issues into account? We strongly recommend to do crash course four to properly understand the pitfalls and shortcomings of data (and it is fun!).

(This question is part of the Quick Scan)

Data and insights about data change over time. If you measure something, chances are that you will influence the thing you measure. Does the technology takes this into account and how? Are you aware of the risk of a self-fullfilling prophecy? Do you organize feedback on the data? Is the feedbackloop closed? How does the technology take this into account? How does the technology improve on the way it handles data?
When using data to predict future behaviour, think about how sustainable these insights are: (1) what are the long term legal permissions to process the data? (2) can the data and used algorithms (if any) be kept current? (3) is the data available for a reasonable period of time in the future, even when the source generator(s) are discontinued or sold to a third party?
If the technology profits from the use or sale of user data, do the users share in that profit? What options are considered for giving users the right to share profits on their own data? Do you think users are treated fairly? Why?

(Only the gray shaded text is printed on the Improvement Scan canvas)

If you think about the limitations of data. Things like subjectivity, incomplete datasets and so on. If you think about the way new insights are handled. If you think about the sustainability of the collection of data or the data that is collected from the users. If you think about all that, what would you (want to) improve? In the technology? In context? In use? The answers on questions 1-4 help you to gain insight into the potential impact of the use of data on this technology. The goal of these answers is not to provide you with a 'go' or ' no go' - decision. The goal is to make you think HOW you can improve the use of the technology. This can be by making changes to the technology or making changes to the context in which the technology is used, or making changes in the way the technology is used.

(This question is part of the Improvement Scan)

Are you satisfied with the quality of your answers? The depth? The level of insight? Did you have enough knowledge to give good answers? Give an honest assessment of the quality of your answers.

Frameworks

Ethical Data Assistant
(https://dataschool.nl/deda/)
5 Principles for Big Data Ethics
(https://towardsdatascience.com/5-principles-for-big-data-ethics-b5df1d105cd3)

Articles

Big Data can't bring objectivity to a subjective world (from TechCrunch)
(https://techcrunch.com/2016/11/18/big-data-cant-bring-objectivity-to-a-subjective-world/)
What's Up With Big Data Ethics? (article from Forbes Magazine)
(https://www.forbes.com/sites/oreillymedia/2014/03/28/whats-up-with-big-data-ethics/#f8833fa35913)

Books

Weapons of Math Destruction (Cathy O'Neill)
(https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815)
Everybody Lies
(https://www.bol.com/nl/p/everybody-lies/9200000054655546/)