October 17, 2017

Archives for August 2017

Getting serious about research ethics in computer science

Digital technology mediates our public and private lives. That makes computer science a powerful discipline, but it also means that ethical considerations are essential in the development of these technologies. Not all new developments may be welcomed by users, such as a patent application by Facebook that enables the company to identify their users’ emotions through cameras on their devices. A critical approach to developing digital technologies, guided by philosophical and ethical principles, will allow interventions that improve society in meaningful ways.

The Center for Information Technology Policy recently organized a conference to discuss research ethics in different computer science communities, such as machine learning, security, and Internet measurement.  This blog post is the first in a series that summarizes and builds on the panel discussions at the conference.

Prof. Arvind Narayanan points out that computer science sub-communities have traditionally developed their own community standards about what is considered to be ethical. See for example responsible vulnerability disclosure standards in information security, or the Menlo Report for the Internet measurement discipline. This allows norms and standards to be tailored to the needs of sub-disciplines. However, the increasing responsibilities of researchers and sub-communities, arising from the increasing power and reach of computer science, are sometimes met with confusion. There is a tendency to see ethical considerations as a “policy issue” to be dealt with by others.

Prof. Melissa Lane of the University Center for Human Values points out that while ethics is rooted in understanding community standards and norms, these do not exhaust it, as some researchers in computer science and other fields can sometimes be tempted to think.  Rather, the academic study of ethics provides the tools to critically reflect on these norms and challenge existing and new practices. A meaningful computer science research ethics therefore does not just translate existing norms into functional requirements, but explores how values are enabled, operationalized, or stifled through technology. A careful analysis of a particular context may even uncover new values that were previously taken for granted or not even considered to be a norm. Think, for example, of “disattendability” — the idea of going about your business without anyone tracking you or paying attention to you. We usually take this for granted in the physical world, but on the Internet, ad trackers, among others, actively violate this norm on an ongoing basis. By understanding the effects of design choices and methodologies, ethics guides technology designers to choose the most appropriate approach among the available alternatives.

Ethics is known for its somewhat conflicting theories, such as consequentialism (“Ends justify the Means”) and deontology (“Act in such a way that you treat humanity […] never merely as a means to an end, but always at the same time as an end”). Prof. Susan Brison cautions against an approach that simply takes an ethical theory and applies it to a technology. She raised the question whether computer science research and data science may require new types of ethics, or evolved theories. Digital data is changing the underlying properties of information, whereby our traditional ways of thinking are being challenged in important ways. For example, micro-targeting of bespoke political messages to individuals circumvents the ability to let ‘good speech’ drown out ‘bad speech’, which is a foundational idea for the concept of freedom of speech.

In my research, I’ve found that ethical guidelines can be incomplete, inaccessible, or conflicting, and existing legal statutes from previous technological eras may not be directly applicable to current technology. This has resulted in computer science communities being somewhat confused about their ethical and legal responsibilities. The upcoming posts in this series will explore some of the ethical standards in machine learning, security, algorithmic transparency, and Internet measurement. We welcome any feedback to move this discussion forward at a crucial time for the ethics of computer science.

See the introduction to the conference here.