FloridiTaddeo2016DataEthics

Luciano Floridi and Mariarosaria Taddeo, "What is Data Ethics?"

Bibliographic info

⇒ Floridi L, Taddeo M. 2016What is data ethics?Phil.Trans.R.Soc.A374:20160360.http://dx.doi.org/10.1098/rsta.2016.0360

Commentary

⇒ In general, what about this text makes it particularly interesting or thought-provoking? What do you see as weaknesses?
The text by Floridi and Taddeo is a short, but deep analysis of the concept of Data Ethics. I believe that it can be used perfectly to introduce students to this topic, if it is completely new to them (this was the case for me). At first I thought, why don't they start writing from a definition. But, the authors chose to first highlight the relevance of data science and how it touches ethical challenges. In that way the text builds toward the definition of Data Science, and it creates a better understanding of the context around the terminology.
What is very good is the explanation of the LoAd (Level of Abstraction Data). The authors elaborate on the fact that people should not solely focus on information or computer ethics, because they miss the point. The point being that hardware doesn't cause the ethical problems, but it is what the hardware does with the software and the data that represents the source of the new ethical challenges. LoAd focusses on these different moral dimensions of data and therefore data ethics is a better term.
Since the text is a rather short straight-forward introduction, I did not find weaknesses.

Excerpts & Key Quotes

The definition of Data Ethics (Page 3_:

"In the light of this change of LoA, data ethics can be defined as the branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values)."

Comment:

As I addressed above, the authors chose to first address the relevance of the LoAd. From that development they create this definition of Data Ethics. From this definition the data science can be separated over three different axes of research: the ethics of data, the ethics of algorithms, and the ethics of practices. Since the spectrum of data ethics has many different aspects, I believe this distinction in the definition can be useful for further research and for creating an overview of the different challenges within data ethics.

Social Preferability (Page 2):

"Social acceptability or, even better,social preferability must be the guiding principles for any data science project witheven a remote impact on human life, to ensure that opportunities will not be missed."

Comment:

This citation supports one side of the debate, how data science ensure the respect of human rights and at the same time shaping open, pluralistic and tolerant information. Social preferability refers to one side of the debate, that argues that overlooking ethical issues may cause a social rejection of a data science project. Ethical issues must not be neglected. The other side of the debate argues that the protection of individual rights must not harm the chances of data science. In this introduction the writes do not take a stance in this debate, but it is relevant to illustrate both views.

trust and transparency (Page 3)

"Trust and transparency are also crucial topics in the ethics of data, in connection with an acknowledged lack of public awareness of the benefits, opportunities, risks and challenges associated with data science."

Comment:

This citation is not too complicated, however the issues that arise by acknowledging this citation do cause a lot of debates. For instance, how far does transparency reach? Maybe only specialists should gain access to the technology of data science, or you could argue that everyone should have the possibility to dig in the technology behind data science, algorithms, and other technologies. Having access to this information could increase the trust of the general public in data science and as a consequence the development of new data science could get more public support. I believe that it is always important to think about the transparency towards a bigger audience, because the issues around data ethics all influence us, and it would be ethical to inform the public. Therefore, it is appropriate that Floridi and Toddeo name these topics in the introduction to Data Ethics.