Abductive reasoning, reasoning for inferring explanations for observations, is often mentioned in scientific, design-related and artistic contexts, but its understanding varies across these domains. This paper reviews how abductive reasoning is discussed in epistemology, science and design, and then analyses how various computational systems use abductive reasoning. Our analysis shows that neither theoretical accounts nor computational implementations of abductive reasoning adequately address generating creative hypotheses. Theoretical frameworks do not provide a straightforward model for generating creative abductive hypotheses, and computational systems largely implement syllogistic forms of abductive reasoning. We break down abduc-tive computational systems into components and conclude by identifying specific directions for future research that could advance the state of creative abductive reasoning in computational systems.

Algorithmic bias has been the subject of much recent controversy. To clarify what is at stake and to make progress resolving the controversy, a better understanding of the concepts involved would be helpful. The discussion here focuses on the disputed claim that algorithms themselves cannot be biased. To clarify this claim we need to know what kind of thing 'algorithms themselves' are, and to disambiguate the several meanings of 'bias' at play. This further involves showing how bias of moral import can result from statistical biases, and drawing connections to previous conceptual work about political artifacts and oppressive things. Data bias has been identified in domains like hiring, policing and medicine. Examples where algorithms themselves have been pinpointed as the locus of bias include recommender systems that influence media consumption, academic search engines that influence citation patterns, and the 2020 UK algorithmically-moderated A-level grades. Recognition that algorithms are a kind of thing that can be biased is key to making decisions about responsibility for harm, and preventing algorithmically mediated discrimination.

The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to symbol grounding. In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism.

This paper develops a critical theory of artificial intelligence, within a historical constellation where computational systems increasingly generate cultural content that destabilises traditional distinctions between human and machine production. Through this analysis, I introduce the concept of the algorithmic condition, a cultural moment when machine-generated work not only becomes indistinguishable from human creation but actively reshapes our understanding of ideas of authenticity. This transformation, I argue, moves beyond false consciousness towards what I call post-consciousness, where the boundaries between individual and synthetic consciousness become porous. Drawing on critical theory and extending recent work on computational ideology, I develop three key theoretical contributions, first, the concept of the Inversion to describe a new computational turn in algorithmic society; second, automimetric production as a framework for understanding emerging practices of automated value creation; and third, constellational analysis as a methodological approach for mapping the complex interplay of technical systems, cultural forms and political economic structures. Through these contributions, I argue that we need new critical methods capable of addressing both the technical specificity of AI systems and their role in restructuring forms of life under computational capitalism. The paper concludes by suggesting that critical reflexivity is needed to engage with the algorithmic condition without being subsumed by it and that it represents a growing challenge for contemporary critical theory.

The concept of code-driven law, i.e. of "legal norms or policies that have been articulated in computer code" by some actors with normative competence, has been convincingly elaborated by Hildebrandt [1]. Its introduction has the merit to refocus the discussion on the role of artificial devices in the legal activity, rather than on ontological positions expressed under code-is-law or law-is-code banners, which are present, with various interpretations and changing fortunes, in the literature and practice of contemporary regulatory technologies, and technology-oriented legal scholarship (see the overview in [2]). According to Hildebrandt, code-driven law should be distinguished from data-driven law, i.e. computational decision-making derived from statistical or other inductive methods, and from text-driven law, i.e. the legal activity performed by humans by means of sources of norms such as statutory and case law. A crucial difference between these forms of "law" is that the linguistic artifacts used in text-driven law are characterized by open-textured concepts (e.g.

Before this, fragmented computer networks struggled to communicate seamlessly. The introduction of the Transmission Control Protocol/Internet Protocol (TCP/IP) enabled consistent data transfer and became the standard for digital communication. However, this node-centric approach, which relies heavily on Internet Protocol (IP) addresses, has also created significant security vulnerabilities and privacy concerns due to its focus on network nodes rather than the data itself. In today's digital landscape, the centralized aggregation and storage of sensitive user data -- including IP addresses -- by service providers pose substantial security risks. These centralized repositories are prime targets for cyberattacks, potentially compromising user privacy and exposing sensitive information. Additionally, the reliance on IP-based system modeling has amplified these risks, necessitating a shift toward a more secure and resilient design approach. This paper proposes a novel data-centric design methodology that moves away from traditional node-focused models. By prioritizing data as the central entity and incorporating multimodal frameworks encompassing objects, events, concepts, and actions, this approach enhances data security and flexibility. The new informatics domain model reimagines data's role in system design, emphasizing its importance throughout its entire lifecycle to foster innovation, security, and seamless data interoperability.

Forthcoming in Collaborative Intelligence: How Humans and AI are Transforming our World, Arathi Sethumadhavan and Mira Lane (eds.), Seth Lazar, Australian National University Man, a child in understanding of himself, has placed in his hands physical tools of incalculable power. He plays with them like a child, and whether they work harm or good is largely a matter of accident. The instrumentality becomes a master and works fatally as if possessed of a will of its own-- not because it has a will but because man has not. Introduction As rapid advances in Artificial Intelligence and the rise of some of history's most potent corporations meet the diminished neoliberal state, people are increasingly subject to power exercised by means of automated systems. Machine learning, big data, and related computational technologies now underpin vital government services from criminal justice to tax auditing, public health to social services, immigration to defence (Citron, 2008; Calo and Citron, 2020; Engstrom et al., 2020). Google and Amazon connect consumers and producers in new algorithmic markets (Nadler and Cicilline, 2020). Google's search algorithm--and possibly in the near future OpenAI's GPT-4 or another large language model--determines, for many, how they find out about everything from how to vote to where to get vaccinated. Meta, Twitter, TikTok, Google and others algorithmically decide whose speech is amplified, reduced, or restricted (Vaidhyanathan, 2011; Pasquale, 2015; Gillespie, 2018; Suzor, 2019). And a new wave of products based on rapid advances in Large Language Models (LLMs) have the potential to further transform our economic and political lives. Automatic Authorities are automated computational systems used to exercise power over us by substantially determining what we may know, what we may have, and what our options will be. This chapter is based on, and substantially revises, my'Power and AI: Nature and Justification', in the Oxford Handbook of AI Governance (Justin Bullock et al., eds). My thanks to the publisher for their permission to use this material. But what normative lessons should we draw from these analyses? Power is everywhere, and is not necessarily bad.

We consider the question of what properties a Machine Ethics system should have. This question is complicated by the existence of ethical dilemmas with no agreed upon solution. We provide an example to motivate why we do not believe falling back on the elicitation of values from stakeholders is sufficient to guarantee correctness of such systems. We go on to define two broad categories of ethical property that have arisen in our own work and present a challenge to the community to approach this question in a more systematic way.

Tone is a crucial component of the prosody of Shanghainese, a Wu Chinese variety spoken primarily in urban Shanghai. Tone sandhi, which applies to all multi-syllabic words in Shanghainese, then, is key to natural-sounding speech. Unfortunately, recent work on Shanghainese TTS (text-to-speech) such as Apple's VoiceOver has shown poor performance with tone sandhi, especially LD (left-dominant sandhi). Here I show that word segmentation during text preprocessing can improve the quality of tone sandhi production in TTS models. Syllables within the same word are annotated with a special symbol, which serves as a proxy for prosodic information of the domain of LD. Contrary to the common practice of using prosodic annotation mainly for static pauses, this paper demonstrates that prosodic annotation can also be applied to dynamic tonal phenomena. I anticipate this project to be a starting point for bringing formal linguistic accounts of Shanghainese into computational projects. Too long have we been using the Mandarin models to approximate Shanghainese, but it is a different language with its own linguistic features, and its digitisation and revitalisation should be treated as such.

In this paper we define a probabilistic computational model which generalizes many noisy neural network models, including the recent work of Maass and Sontag [5]. We identify weak ergodicjty as the mechanism responsible for restriction of the computational power of probabilistic models to definite languages, independent of the characteristics of the noise: whether it is discrete or analog, or if it depends on the input or not, and independent of whether the variables are discrete or continuous. We give examples of weakly ergodic models including noisy computational systems with noise depending on the current state and inputs, aggregate models, and computational systems which update in continuous time.