See no evil? Ethics in an interventionist ICTD

This paper considers some of the ethical questions that arise in conducting interventionist ICTD research, and examines the ethical advice and guidance that is readily available to researchers. Recent years have seen a growing interest from technology researchers in applying their skills to address the needs and aspirations of people in developing regions. In contrast to much previous research in Information and Communication Technologies for Development (ICTD) which has sought to study and understand processes surrounding technologies, technology researchers are interested in finding ways to change the forms of these technologies in order to promote desirable social aims. These more interventionist research encounters raise distinctive ethical challenges. This paper explores the discussions that have been presented in the major ICTD journals and conferences and major development studies journals as well as examining codes of conduct from related fields of research. Exploration of this literature shows that the quantity, quality and detail of advice that directly addresses the challenges of interventionist ICTD is actually very limited. This paper argues that the there is an urgent need for the ICTD research community to investigate and debate this subject

The Complexities of Developing a Personal Code of Ethics for Learning Analytics Practitioners: Implications for Institutions and the Field

In this paper we explore the potential role, value and utility of a personal code of ethics (COE) for learning analytics practitioners, and in particular we consider whether such a COE might usefully mediate individual actions and choices in relation to a more abstract institutional COE. While several institutional COEs now exist, little attention has been paid to detailing the ethical responsibilities of individual practitioners. To investigate the problems associated with developing and implementing a personal COE, we drafted an LA Practitioner COE based on other professional codes, and invited feedback from a range of learning analytics stakeholders and practitioners: ethicists, students, researchers and technology executives. Three main themes emerged from their reflections: 1. A need to balance real world demands with abstract principles, 2. The limits to individual accountability within the learning analytics space, and 3. The continuing value of debate around an aspirational code of ethics within the field of learning analytics

PRECEPT:a framework for ethical digital forensics investigations

Purpose: Cyber-enabled crimes are on the increase, and law enforcement has had to expand many of their detecting activities into the digital domain. As such, the field of digital forensics has become far more sophisticated over the years and is now able to uncover even more evidence that can be used to support prosecution of cyber criminals in a court of law. Governments, too, have embraced the ability to track suspicious individuals in the online world. Forensics investigators are driven to gather data exhaustively, being under pressure to provide law enforcement with sufficient evidence to secure a conviction. Yet, there are concerns about the ethics and justice of untrammeled investigations on a number of levels. On an organizational level, unconstrained investigations could interfere with, and damage, the organization’s right to control the disclosure of their intellectual capital. On an individual level, those being investigated could easily have their legal privacy rights violated by forensics investigations. On a societal level, there might be a sense of injustice at the perceived inequality of current practice in this domain. This paper argues the need for a practical, ethically-grounded approach to digital forensic investigations, one that acknowledges and respects the privacy rights of individuals and the intellectual capital disclosure rights of organisations, as well as acknowledging the needs of law enforcement. We derive a set of ethical guidelines, then map these onto a forensics investigation framework. We subjected the framework to expert review in two stages, refining the framework after each stage. We conclude by proposing the refined ethically-grounded digital forensics investigation framework. Our treatise is primarily UK based, but the concepts presented here have international relevance and applicability.Design methodology: In this paper, the lens of justice theory is used to explore the tension that exists between the needs of digital forensic investigations into cybercrimes on the one hand, and, on the other, individuals’ rights to privacy and organizations’ rights to control intellectual capital disclosure.Findings: The investigation revealed a potential inequality between the practices of digital forensics investigators and the rights of other stakeholders. That being so, the need for a more ethically-informed approach to digital forensics investigations, as a remedy, is highlighted, and a framework proposed to provide this.Practical Implications: Our proposed ethically-informed framework for guiding digital forensics investigations suggest a way of re-establishing the equality of the stakeholders in this arena, and ensuring that the potential for a sense of injustice is reduced.Originality/value: Justice theory is used to highlight the difficulties in squaring the circle between the rights and expectations of all stakeholders in the digital forensics arena. The outcome is the forensics investigation guideline, PRECEpt: Privacy-Respecting EthiCal framEwork, which provides the basis for a re-aligning of the balance between the requirements and expectations of digital forensic investigators on the one hand, and individual and organizational expectations and rights, on the other

When users control the algorithms: Values expressed in practices on the twitter platform

Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the “Twitter algorithm” we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices

Guidelines for ethical nudging in password authentication

Nudging has been adopted by many disciplines in the last decade in order to achieve behavioural change. Information security is no exception. A number of attempts have been made to nudge end-users towards stronger passwords. Here we report on our deployment of an enriched nudge displayed to participants on the system enrolment page, when a password has to be chosen. The enriched nudge was successful in that participants chose significantly longer and stronger passwords. One thing that struck us as we designed and tested this nudge was that we were unable to find any nudge-specific ethical guidelines to inform our experimentation in this context. This led us to reflect on the ethical implications of nudge testing, specifically in the password authentication context. We mined the nudge literature and derived a number of core principles of ethical nudging. We tailored these to the password authentication context, and then show how they can be applied by assessing the ethics of our own nudge. We conclude with a set of preliminary guidelines derived from our study to inform other researchers planning to deploy nudge-related techniques in this context

PRECEPT: A Framework for Ethical Digital Forensics Investigations.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Cyber-enabled crimes are on the increase, and law enforcement has had to expand many of their detecting activities into the digital domain. As such, the field of digital forensics has become far more sophisticated over the years and is now able to uncover even more evidence that can be used to support prosecution of cyber criminals in a court of law. Governments, too, have embraced the ability to track suspicious individuals in the online world. Forensics investigators are driven to gather data exhaustively, being under pressure to provide law enforcement with sufficient evidence to secure a conviction. Yet, there are concerns about the ethics and justice of untrammeled investigations on a number of levels. On an organizational level, unconstrained investigations could interfere with, and damage, the organization’s right to control the disclosure of their intellectual capital. On an individual level, those being investigated could easily have their legal privacy rights violated by forensics investigations. On a societal level, there might be a sense of injustice at the perceived inequality of current practice in this domain. This paper argues the need for a practical, ethically-grounded approach to digital forensic investigations, one that acknowledges and respects the privacy rights of individuals and the intellectual capital disclosure rights of organisations, as well as acknowledging the needs of law enforcement. We derive a set of ethical guidelines, then map these onto a forensics investigation framework. We subjected the framework to expert review in two stages, refining the framework after each stage. We conclude by proposing the refined ethically-grounded digital forensics investigation framework. Our treatise is primarily UK based, but the concepts presented here have international relevance and applicability. In this paper, the lens of justice theory is used to explore the tension that exists between the needs of digital forensic investigations into cybercrimes on the one hand, and, on the other, individuals’ rights to privacy and organizations’ rights to control intellectual capital disclosure. The investigation revealed a potential inequality between the practices of digital forensics investigators and the rights of other stakeholders. That being so, the need for a more ethically-informed approach to digital forensics investigations, as a remedy, is highlighted, and a framework proposed to provide this. Our proposed ethically-informed framework for guiding digital forensics investigations suggest a way of re-establishing the equality of the stakeholders in this arena, and ensuring that the potential for a sense of injustice is reduced. Justice theory is used to highlight the difficulties in squaring the circle between the rights and expectations of all stakeholders in the digital forensics arena. The outcome is the forensics investigation guideline, PRECEpt: Privacy-Respecting EthiCal framEwork, which provides the basis for a re-aligning of the balance between the requirements and expectations of digital forensic investigators on the one hand, and individual and organizational expectations and rights, on the other

AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for "Good". This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of "AI for Social Good", more specifically "Data Science for Social Good". Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: "attacks" as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on 27-10-201