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Big Data and Ethics

The acquisition and use of aggregated information are not a new phenomenon. Data collection and analysis have always been common for centuries and have played an important role in the development of the organization. Data science gives huge opportunities to enhance private and public life, as well as the environment. Sadly, such opportunities are also gathered to significant ethical challenges. The heavy use of increasingly more data—often personal, if not sensitive (big data)—and the grow in reliance on algorithms to analyze them in order to make choices and decisions, as well as the increasing reduction of human involvement or even oversight over many automatic processes, pose pressing issues of fairness, responsibility and respect of human rights, among others. However, these ethical challenges can be highlighted successfully. Fostering the development and applications of data science while making sure to respect the rights of humans and of the values shaping open, pluralistic and tolerant information societies is a great chance of which we can and must take advantage of. Data science prompted a further change in the LoA (Level of Abstraction) at which ethical analysis can be developed most fruitfully. We have come to understand that it is not a specific technology (computers, tablets, mobile phones, online platforms, cloud computing), but what any digital technology manipulates that represents the correct focus of our ethical strategies. The change from information ethics to data ethics is more semantic than conceptual, but it does highlight the need to focus on what is being covered as the true invariant of our concerns. This is why labels such as “robo-ethics” or “machine ethics” miss the point, anachronistically stepping back to when “computer ethics” seemed to provide the right perspective. It is not the hardware that causes ethical problems, it is what the hardware does with the software and the data that represents the source of our new difficulties. LoA concentrates on the different moral dimensions of data. In doing so, it highlights the fact that, ethical problems such as privacy, anonymity, transparency, trust and responsibility concern data collection, curation, analysis and use, and therefore they are better understood at that level. In the light of this of LoA shifting, data ethics can be describeed as the branch of ethics that studies and evaluates moral problems related to data, algorithms corresponding practices, to formulate and support morally good solutions. In short, the ethical challenges posed by data science can be mapped within the conceptual space delineated by three axes of research: the ethics of data, the ethics of algorithms and the ethics of practices. First, ethics of data focuses on ethical problems posed by the gathering and analysis of large datasets and on problems ranging from the usage of big data. Second, ethics of algorithms, it addresses the problems posed by the increasing complexity and autonomy of algorithms broadly understood, especially in the case of machine learning applications. In this situation, some crucial challenges include moral responsibility and accountability of both data scientists and designers with respect to unexpected and undesired consequences as well as missed opportunities. Finally, the ethics of practices addresses the pressing questions about the responsibilities and liabilities of people and organizations in charge of data processes, strategies and policies, including data scientists, with the goal to define an ethical framework to make professional codes about responsible innovation, development and usage, which may ensure ethical practices fostering both the progress of data science and the protection of the rights of individuals and groups. Three issues are central in this line of analysis: consent, user privacy and secondary usage. While they are different lines of research, the ethics of data, algorithms and practices are obviously intertwined, and this is why it may be better to speak in terms of three axes defining a conceptual space within which ethical problems are like points identified by three values. Most of them do not lie on a single axis. For instance, analyses concentrating on data privacy will also address issues concerning consent and professional responsibilities. Likewise, ethical auditing of algorithms often implies analyses of the responsibilities of their designers, developers, users and adopters. Data ethics must address the whole conceptual space and hence all three axes of research together, even if with different priorities and focus. For this reason, data ethics needs to be made from the start as a macroethics, that is, as an overall ‘geometry’ of the ethical space that avoids narrow and ad hoc approaches but rather addresses the diverse set of ethical implications of data science within a consistent, holistic and inclusive framework.