David M. Douglas

Doxing is the intentional public release onto the Internet of personal information about an individual by a third party, often with the intent to humiliate, threaten, intimidate, or punish the identified individual. In this paper I present a conceptual analysis of the practice of doxing and how it differs from other forms of privacy violation. I distinguish between three types of doxing: deanonymizing doxing, where personal information establishing the identity of a formerly anonymous individual is released; targeting doxing, that discloses personal information that reveals specific details of an individual’s circumstances that are usually private, obscure, or obfuscated; and delegitimizing doxing, which reveals intimate personal information that damages the credibility of that individual. I also describe how doxing differs from blackmail and defamation. I argue that doxing may be justified in cases where it reveals wrongdoing (such as deception), but only if the information released is necessary to reveal that such wrongdoing has occurred and if it is in the public interest to reveal such wrongdoing. Revealing additional information, such as that which allows an individual to be targeted for harassment and intimidation, is unjustified. I illustrate my discussion with the examples of the alleged identification of the creator of Bitcoin, Satoshi Nakamoto, by Newsweek magazine, the identification of the notorious Reddit user Violentacrez by the blog Gawker, and the harassment of game developer Zoe Quinn in the ‘GamerGate’ Internet campaign.

As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems. We examine the practices and experiences of researchers and engineers from Australia’s national scientific research agency (CSIRO), who are involved in designing and developing AI systems for many application areas. Semi-structured interviews were used to examine how the practices of the participants relate to and align with a set of high-level AI ethics principles proposed by the Australian Government. The principles comprise: (1) privacy protection and security, (2) reliability and safety, (3) transparency and explainability, (4) fairness, (5) contestability, (6) accountability, (7) human-centred values, (8) human, social and environmental well-being. Discussions on the gained insights from the interviews include various tensions and trade-offs between the principles, and provide suggestions for implementing each high-level principle. We also present suggestions aiming to enhance associated support mechanisms.

We present an overview of the literature on trust in AI and AI trustworthiness and argue for the need to distinguish these concepts more clearly and to gather more empirically evidence on what contributes to people’s trusting behaviours. We discuss that trust in AI involves not only reliance on the system itself, but also trust in the developers of the AI system. AI ethics principles such as explainability and transparency are often assumed to promote user trust, but empirical evidence of how such features actually affect how users perceive the system’s trustworthiness is not as abundance or not that clear. AI systems should be recognised as socio-technical systems, where the people involved in designing, developing, deploying, and using the system are as important as the system for determining whether it is trustworthy. Without recognising these nuances, ’trust in AI’ and ’trustworthy AI’ risk becoming nebulous terms for any desirable feature for AI systems.

The term ’ethical risk’ often appears in discussions about the responsible development and deployment of artificial intelligence (AI). However, ethical risk remains inconsistently defined in this context, obscuring what distinguishes it from other forms of risk, such as social, reputational or legal risk, for example. In this paper we present a definition of ethical risk for AI as being any risk associated with an AI that may cause stakeholders to fail one or more of their ethical responsibilities towards other stakeholders. To support our definition, we describe how stakeholders have role responsibilities that follow from their relationship with the AI, and that these responsibilities are towards other stakeholders associated with the AI. We discuss how stakeholders may differ in their ability to make decisions about an AI, their exposure to risk, and whether they or others may benefit from these risks. Stakeholders without the ability to make decisions about the risks associated with an AI and how it is used are dependent on other stakeholders with this ability. This relationship places those who depend on decision-making stakeholders at ethical risk of being dominated by them. The decision-making stakeholder is ethically responsible for the risks their decisions about the AI impose on those affected by them. We illustrate our account of ethical risk for AI with two examples: AI-designed attachments for surgical robots that are optimised for treating specific patients, and self-driving ’robotaxis’ that carry passengers on public roads.

Science is being transformed by the increasing capabilities of automation technologies and artificial intelligence (AI). Integrating AI and machine learning (ML) into scientific practice requires changing established research methods while maintaining a scientific understanding of research findings. Researchers are at the forefront of this change, but there is currently little understanding of how they are experiencing these upheavals in scientific practice. In this paper, we examine how researchers working in several research fields (automation engineering, computational design, conservation decision-making, materials science, and synthetic biology) perceive AI/ML technologies used in their work, such as laboratory automation, automated design of experiments, computational design, and computer experiments. We find that researchers emphasised the need for AI/ML technologies to have practical benefits (such as efficiency and improved safety) to justify their use. Researchers were also hesitant to automate data analysis, and the importance of explainability differed between researchers working with laboratory automation and those using AI/ML directly in their research. This difference is due to the different role AI/ML plays in different research fields: laboratory automation performs processes already defined by the researcher and the actions are visible or recorded, while in AI/ML applications the decisions that produced the result may be obscure to the researcher. Understanding the role AI/ML plays in scientific practice is important for ensuring that scientific knowledge continues to grow.