We paraphrase Descartes' famous dictum in the area of AI ethics where the "I doubt and therefore I am" is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt ourselves. The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study.

Agents act to bring about a state of the world that is more compatible with their personal or institutional values. To formalise this intuition, the paper proposes an action framework based on the STRIPS formalisation. Technically, the contribution expresses actions in terms of Value-based Formal Reasoning (VFR), which provides a set of propositions derived from an Agent's value profile and the Agent's assessment of propositions with respect to the profile. Conceptually, the contribution provides a computational framework for a form of consequentialist ethics which is satisficing, pluralistic, act-based, and preferential.

Responsible AI must be able to make decisions that consider human values and can be justified by human morals. Operationalising normative ethical principles inferred from philosophy supports responsible reasoning. We survey computer science literature and develop a taxonomy of 23 normative ethical principles which can be operationalised in AI. We describe how each principle has previously been operationalised, highlighting key themes that AI practitioners seeking to implement ethical principles should be aware of. We envision that this taxonomy will facilitate the development of methodologies to incorporate normative ethical principles in responsible AI systems.

In recent years, computer scientists have increasingly com e to recognize that artificial intelligence (AI) systems have the potential to create harmful consequences. Especially within machine learning, there have been numerous efforts to formally characterize various not ions of fairness and develop algorithms to satisfy these criteria. However, most of this research has proceede d without any nuanced discussion of ethical foundations. Partly as a response, there have been several r ecent calls to think more broadly about the ethical implications of AI (Barabas et al., 2018; Hu and Chen, 2018b; Torresen, 2018; Green, 2019). Among the most prominent approaches to ethics within philos ophy is a highly influential position known as consequentialism. Roughly speaking, the consequentialist believes that out comes are all that matter, and that people should therefore endeavour to act so as to produce the best consequences, based on an impart ial perspective as to what is best . Although there are numerous difficulties with consequentia lism in practice (see §4), it nevertheless provides a clear and principled foundation from which to critiq ue proposals which fall short of its ideals. In this paper, we analyze the literature on fairness within mac hine learning, and show how it largely depends on assumptions which the consequentialist perspective rev eals immediately to be problematic. In particular, we make the following contributions: - We provide an accessible overview of the main ideas of conseq uentialism ( §3), as well as a discussion of its difficulties ( §4), with a special emphasis on computational limitations. 1 - We review the dominant ideas about fairness in the machine le arning literature ( §5), and provide the first critique of these ideas explicitly from the perspectiv e of consequentialism ( §6). - We conclude with a broader discussion of the ethical issues r aised by learning and randomization, highlighting future direction for both AI and consequentia lism ( §7).

The creation of ethics modules that can be inserted into a variety of classes may help ensure that ethics as a subject is not marginalized and enable professors with little experience in philosophy or with fewer resources to incorporate ethics into their more technical classes. This post will outline some of the topics that professors have decided to cover in this field, as well as suggestions for types of assignments that may be useful. We hope that readers will consider ways to add these into their classes, and we welcome comments with further suggestions of topics or assignments. With regards to ethics, some of the key topics that professors have taught about include: deontology, consequentialism, utilitarianism, virtue ethics, moral responsibility, cultural relativism, social contract, feminist ethics, justice consequentialism, the distinction between ethics and law, and the relationship between principles, standards, and rules. Using these frameworks, professors can discuss a variety of topics, including: privacy, algorithmic bias, misinformation, intellectual property, surveillance, inequality, data collection, AI governance, free speech, transparency, security, anonymity, systemic risk, labor, net neutrality, accessibility, value-sensitive design, codes of ethics, predictive policing, virtual reality, ethics in industry, machine learning, clinical versus actuarial reasoning, issue spotting, and basic social science concepts.