Human oversight has become a key mechanism for the governance of artificial intelligence (“AI”). Human overseers are supposed to increase the accuracy and safety of AI systems, uphold human values, and build trust in the technology. Empirical research suggests, however, that humans are not reliable in fulfilling their oversight tasks. They may be lacking in competence or be harmfully incentivised. This creates a challenge for human oversight to be effective. In addressing this challenge, this article aims (...) to make three contributions. First, it surveys the emerging laws of oversight, most importantly the European Union’s Artificial Intelligence Act (“AIA”). It will be shown that while the AIA is concerned with the competence of human overseers, it does not provide much guidance on how to achieve effective oversight and leaves oversight obligations for AI developers underdefined. Second, this article presents a novel taxonomy of human oversight roles, differentiated along whether human intervention is constitutive to, or corrective of a decision made or supported by an AI. The taxonomy allows to propose suggestions for improving effectiveness tailored to the type of oversight in question. Third, drawing on scholarship within democratic theory, this article formulates six normative principles which institutionalise distrust in human oversight of AI. The institutionalisation of distrust has historically been practised in democratic governance. Applied for the first time to AI governance, the principles anticipate the fallibility of human overseers and seek to mitigate them at the level of institutional design. They aim to directly increase the trustworthiness of human oversight and to indirectly inspire well-placed trust in AI governance. (shrink)

This paper argues against the call to democratize artificial intelligence (AI). Several authors demand to reap purported benefits that rest in direct and broad participation: In the governance of AI, more people should be more involved in more decisions about AI—from development and design to deployment. This paper opposes this call. The paper presents five objections against broadening and deepening public participation in the governance of AI. The paper begins by reviewing the literature and carving out a set of (...) claims that are associated with the call to “democratize AI”. It then argues that such a democratization of AI (1) rests on weak grounds, because it does not answer to a demand of legitimization, (2) is redundant in that it overlaps with existing governance structures, (3) is resource intensive, which leads to injustices, (4) is morally myopic and thereby creates popular oversights and moral problems of its own, and finally, (5) is neither theoretically nor practically the right kind of response to the injustices that animate the call. The paper concludes by suggesting that AI should be democratized not by broadening and deepening participation but by increasing the democratic quality of the administrative and executive elements of collective decision making. In a slogan: The question is not so much whether AI should be democratized but how. (shrink)

This essay discusses engagement against state secrecy and engagement for secrecy, free from interference. By exploring divisions introduced by state secrecy (through exclusion, subjection and oppression), it identifies the distortions of equal participation in political communities. The author introduces the notion of pata-politics in order to describe the false relation to the secrecy effect. Furthermore, the text examines key issues of today’s intelligence studies (such as democratic intelligence oversight and the balance of powers doctrine), with special (...) emphasis on the possible limits of a liberal approach. Additionally, the author elaborates a metacritique of the framework in which the private sphere is one-sidedly described as a victim of wrong interference by state institutions. (shrink)

In recent years, artificial intelligence has been deployed by online platforms to prevent the upload of allegedly illegal content or to remove unwarranted expressions. These systems are trained to spot objectionable content and to remove it, block it, or filter it out before it is even uploaded. Artificial intelligence filters offer a robust approach to content moderation which is shaping the public sphere. This dramatic shift in norm setting and law enforcement is potentially game-changing for democracy. Artificial (...) class='Hi'>intelligence filters carry censorial power, which could bypass traditional checks and balances secured by law. Their opaque and dynamic nature creates barriers to oversight, and conceals critical value choices and tradeoffs. Currently, we lack adequate tools to hold them accountable. This paper seeks to address this gap by introducing an adversarial procedure— – Contesting Algorithms. It proposes to deliberately introduce friction into the dominant removal systems governed by artificial intelligence. Algorithmic content moderation often seeks to optimize a single goal, such as removing copyright-infringing materials or blocking hate speech, while other values in the public interest, such as fair use or free speech, are often neglected. Contesting algorithms introduce an adversarial design which reflects conflicting values, and thereby may offer a check on dominant removal systems. Facilitating an adversarial intervention may promote democratic principles by keeping society in the loop. An adversarial public artificial intelligence system could enhance dynamic transparency, facilitate an alternative public articulation of social values using machine learning systems, and restore societal power to deliberate and determine social tradeoffs. (shrink)

One of the biggest challenges facing modern societies is how to monitor one’s intelligence community while maintaining the necessary level of secrecy. Indeed, while some secrecy is needed for mission success, too much has allowed significant abuse. Moreover, extending this secrecy to democratic oversight actors only creates another layer of unobserved actors and removes the public scrutiny that keeps their power and decision-making in check. This article will therefore argue for a new type of oversight through (...) a specialised ethical whistleblowing framework. This includes, first, outlining what intelligence wrongdoings justify whistleblowing; second, whether whistleblowing is the correct remedy – something not necessarily clear with intelligence; and finally, what form the whistleblowing should take. This framework will examine the Snowden case to determine whether he was correct leaking intelligence data and whether the means were appropriate, and second, whether those involved in the Central Intelligence... (shrink)

Intolerance, abuse, calling of names because of differences of opinion about religion or politics or business, as well as because of differences of race, color, wealth or degree of culture are treason to the democratic way of life. Merely legal guarantees of the civil liberties of free belief, free expression, free assembly are of little avail if the give and take of ideas, facts, experiences, is choked by mutual suspicion, by abuse, by fear and hatred.Without some kind of (...) class='Hi'>oversight, the golem, not God, might emerge from machines … it is naïve to believe that government is competent, let alone in a position to control the development and deployment of robots, self-generating algorithms, and artificial intelligence.... (shrink)

Since the Enlightenment, there have been advocates for the rationalizing efficiency of enlightened sovereigns, bureaucrats, and technocrats. Today these enthusiasms are joined by calls for replacing or augmenting government with algorithms and artificial intelligence, a process already substantially under way. Bureaucracies are in effect algorithms created by technocrats that systematize governance, and their automation simply removes bureaucrats and paper. The growth of algorithmic governance can already be seen in the automation of social services, regulatory oversight, policing, the justice (...) system, and the military. However, there have also always been justified democratic and economic criticisms of autocracy, bureaucracy, technocracy, and algorithmic governance. Bureaucrats, technocrats, and algorithms embody biases that tend to serve the interests of elites, and all require transparency and democratic accountability, oversight individual citizens are ill equipped to exercise. As state apparatuses are increasingly automated, mechanisms for collective action and democratic oversight also need to be automated. Algorithms and cyborg citizenry will enable a posthuman democracy. Democratically accountable algorithmic governance, enabled by artificial intelligence and human enhancement, can automate bottom-up citizen surveillance, inform debate, aggregate decision making, and ensure the efficient working of the gradually withering state. As paid work disappears and we transition to a postcapitalist economy with a universal basic income, market mechanisms can be replaced with democratic planning. Indeed, only algorithmic governance can secure our future against accelerating threats from technological innovation. (shrink)

The maze and the masses -- Democracy as the rule of the dumb many? -- A selective genealogy of the epistemic argument for democracy -- First mechanism of democratic reason: inclusive deliberation -- Epistemic failures of deliberation -- Second mechanism of democratic reason: majority rule.

Democracy is valuable not only because it treats us equally but because it is intelligent. Democracies can make effective use of knowledge, engage in experimentation, utilise societal diversity, all the while motivating political leaders towards the common good. It is against the emergence of a new democratic scepticism, however, that this book defends the intelligence of democracy. Whether it be due to ignorant voters, irrational public debate, or disconnected politicians, a growing number now argue that democracies are destined (...) to make bad decisions. While these democratic sceptics see epistemic concerns as reasons for giving up on democracy, this book argues they are reasons to embrace it. Intelligent Democracy develops the first systemic account of democracy's epistemic value. Unlike approaches which focus on single institutions, this view conceptualises democracy as a network of actors which work together to produce epistemic benefits at the system level. While any one democratic body will be limited, this book analyses the divisions of labour within a broader deliberative system, and how best to combine inclusive elections, deliberative assemblies, random sortition, and the open public sphere. This systemic approach is shown to better explain democracy's epistemic value, while more adeptly responding to the challenges posed by the new democratic scepticism. It can demonstrate democracy's intelligence over regimes of epistocracy and political meritocracy which empower only the knowledgeable, but also overlooked proposals for granting greater powers to markets or polycentric governance. The result is a unique epistemic justification of democracy and a robust reply to democracy's critics. (shrink)

This paper examines the democratic ethics of artificially intelligent polls. Driven by machine learning, AI electoral polls have the potential to generate predictions with an unprecedented level of granularity. We argue that their predictive power is potentially desirable for electoral democracy. We do so by critically engaging with four objections: (1) the privacy objection, which focuses on the potential harm of the collection, storage, and publication of granular data about voting preferences; (2) the autonomy objection, which argues that polls (...) are an obstacle to independently formed judgments; (3) the tactical voting objection, which argues that voting strategically on the basis of polls is troublesome; and finally (4) the manipulation objection, according to which malicious actors could systematically bias predictions to alter voting behaviours. (shrink)

Machine learning algorithms are increasingly used to support decision-making in the exercise of public authority. Here, we argue that an important consideration has been overlooked in previous discussions: whether the use of ML undermines the democratic legitimacy of public institutions. From the perspective of democratic legitimacy, it is not enough that ML contributes to efficiency and accuracy in the exercise of public authority, which has so far been the focus in the scholarly literature engaging with these developments. According (...) to one influential theory, exercises of administrative and judicial authority are democratically legitimate if and only if administrative and judicial decisions serve the ends of the democratic law maker, are based on reasons that align with these ends and are accessible to the public. These requirements are not satisfied by decisions determined through ML since such decisions are determined by statistical operations that are opaque in several respects. However, not all ML-based decision support systems pose the same risk, and we argue that a considered judgment on the democratic legitimacy of ML in exercises of public authority need take the complexity of the issue into account. This paper outlines considerations that help guide the assessment of whether a ML undermines democratic legitimacy when used to support public decisions. We argue that two main considerations are pertinent to such normative assessment. The first is the extent to which ML is practiced as intended and the extent to which it replaces decisions that were previously accessible and based on reasons. The second is that uses of ML in exercises of public authority should be embedded in an institutional infrastructure that secures reason giving and accessibility. (shrink)

The problem of controlling an artificial general intelligence has fascinated both scientists and science-fiction writers for centuries. Today that problem is becoming more important because the time when we may have a superhuman intelligence among us is within the foreseeable future. Current average estimates place that moment to before 2060. Some estimates place it as early as 2040, which is quite soon. The arrival of the first AGI might lead to a series of events that we have not (...) seen before: rapid development of an even more powerful AGI developed by the AGIs themselves. This has wide-ranging implications to the society and therefore it is something that must be studied well before it happens. In this paper we will discuss the problem of limiting the risks posed by the advent of AGIs. In a thought experiment, we propose an AGI which has enough human-like properties to act in a democratic society, while still retaining its essential artificial general intelligence properties. We discuss ways of arranging the co-existence of humans and such AGIs using a democratic system of coordination and coexistence. If considered a success, such a system could be used to manage a society consisting of both AGIs and humans. The democratic system where each member of the society is represented in the highest level of decision-making guarantees that even minorities would be able to have their voices heard. The unpredictability of the AGI era makes it necessary to consider the possibility that a population of autonomous AGIs could make us humans into a minority. (shrink)

Should artificial intelligences ever be included as co-authors of democratic decisions? According to the conventional view in democratic theory, the answer depends on the relationship between the political unit and the entity that is either affected or subjected to its decisions. The relational conditions for inclusion as stipulated by the all-affected and all-subjected principles determine the spatial extension of democratic inclusion. Thus, AI qualifies for democratic inclusion if and only if AI is either affected or subjected (...) to decisions by the political unit in relevant ways. This paper argues that the conventional view is too simple; that it neglects democratic reasons to recognize only agents and/or moral patients as participants in decision-making. The claim defended is that AAP and ASP implicitly affirm requirements for agency and patiency. In ASP, the entity included must be an agent understood either in terms of legal status, capacity to comply with the law or ability to recognize legitimate authority. In AAP, the entity included must be a patient, understood either in terms of capacity for sentience or consciousness. Thus, the idea here is to explore the potential democratic inclusion of artificial intelligences by an updated account of the relevant conditions of agency and patiency that are implicit in democratic theory. Although conceivable that AI is or will be either affected or subjected in relevant ways to decisions made by political units, it is far less clear that AI will ever be agents or patients in the sense required for democratic inclusion. (shrink)

This essay explains how, from the theoretical perspective of Basil Bernstein's three “conditions for democracy,” the current pedagogy of artificially intelligent personalized learning seems inadequate. Building on Bernstein's comprehensive work and more recent research concerned with personalized education, Natalia Kucirkova and Sandra Leaton Gray suggest three principles for advancing personalized education and artificial intelligence (AI). They argue that if AI is to reach its full potential in terms of promoting children's identity as democratic citizens, its pedagogy must go (...) beyond monitoring the technological progression of personalized provision of knowledge. It needs to pay more careful attention to the democratic impact of data-driven systems. Kucirkova and Leaton Gray propose a framework to distinguish the value of personalized learning in relation to pluralization and to guide educational researchers and practitioners in its application to socially just classrooms. (shrink)

The recently retired Homeland Security Advisory System constituted a main means by which the intensity of the terrorist threat was communicated to the United States' public. An examination of its inner workings and its social impact shows the System as part of a modality of government: an encapsulation of intelligence-led governmentality. Informed by the political philosophy of Cornelius Castoriadis, I contextualise this modality as a settling of fundamental tensions inherent in modern sociopolitical culture, those between the principle of social (...) and personal autonomy, and that of rational mastery of people and nature. These principles are strongly connected to democratic and oligarchic political organisation, respectively, and they give rise to different justifications of state authority. In turn, they pertain to the fundamental question of whether scientific expertise on politics is possible. (shrink)

Bioethics, Volume 36, Issue 2, Page 201-209, February 2022.

In this essay, I attempt to interpret the educational philosophy of John Dewey in a way that accomplishes two goals. The first of these is to avoid any reference to Dewey as a propagator of a particular scientific method or to any of the individualist and cognitivist ideas that is sometimes associated with him. Secondly, I want to overcome the tendency to interpret Dewey as a naturalist by looking at his concept of intelligence. It is argued that ‘intelligent experience’ (...) is the basic concept of education. I suggest how this concept should be understood. I propose to look at it as an interplay between the faculties of imagination and judgment. (shrink)

Artificial Intelligence (AI), especially of the machine learning (ML) variety, is used by health care organizations to assist with a number of tasks, including diagnosing patients and optimizing operational workflows. AI products already proliferate the health care market, with usage increasing as the technology matures. Although AI may potentially revolutionize health care, the use of AI in health settings also leads to risks ranging from violating patient privacy to implementing a biased algorithm. This chapter begins with a broad overview (...) of health care AI and how it is currently used. We then adopt a “lifecycle” approach to discussing issues with health care AI. We start by discussing the legal and ethical issues pertaining to how data to build AI are gathered in health care settings, focusing on privacy. Next, we turn to issues in algorithm development, especially algorithmic bias. We then discuss AI deployment to treat patients, focusing on informed consent. Finally, we will discuss existing oversight mechanisms for health AI in the United States: liability and regulation. (shrink)

Artificial Intelligence (AI) technologies offer new ways of conducting decision-making tasks that influence the daily lives of citizens, such as coordinating traffic, energy distributions, and crowd flows. They can sort, rank, and prioritize the distribution of fines or public funds and resources. Many of the changes that AI technologies promise to bring to such tasks pertain to decisions that are collectively binding. When these technologies become part of critical infrastructures, such as energy networks, citizens are affected by these decisions (...) whether they like it or not, and they usually do not have much say in them. The democratic challenge for those working on AI technologies with collectively binding effects is both to _develop_ and _deploy_ technologies in such a way that the democratic legitimacy of the relevant decisions is safeguarded. In this paper, we develop a conceptual framework to help policymakers, project managers, innovators, and technologists to assess and develop approaches to democratize AI. This framework embraces a broad sociotechnical perspective that highlights the interactions between technology and the complexities and contingencies of the context in which these technologies are embedded. We start from the problem-based and practice-oriented approach to democracy theory as developed by political theorist Mark Warren. We build on this approach to describe practices that can enhance or challenge democracy in political systems and extend it to integrate a sociotechnical perspective and make the role of technology explicit. We then examine how AI technologies can play a role in these practices to improve or inhibit the democratic nature of political systems. We focus in particular on AI-supported political systems in the energy domain. (shrink)

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily focused (...) on how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)

Ongoing hostilities between evolution and intelligent design adherents reveal deeper epistemological and ethical crises in American life. First, when adjudicating sociopolitical differences among people, how much epistemological “diversity” can be embraced before the very canons of judgment become suspect? Pragmatist notions of inquiry, warranted assertability, and pluralism can help strike a better balance. Second, the related crisis of factionalized “communities” might be addressed, along Deweyan lines, by the construction of a philosophical “total attitude” redolent of democratic ideals, more broadly (...) conceived. This attitude could grow out of reconstructed educational methods that train imaginative and interactive habits of inquiry and communication. (shrink)

The call to make artificial intelligence (AI) more democratic, or to “democratize AI,” is sometimes framed as a promising response for mitigating algorithmic injustice or making AI more aligned with social justice. However, the notion of “democratizing AI” is elusive, as the phrase has been associated with multiple meanings and practices, and the extent to which it may help mitigate algorithmic injustice is still underexplored. In this paper, based on a socio-technical understanding of algorithmic injustice, I examine three (...) notable notions of democratizing AI and their associated measures—democratizing AI use, democratizing AI development, and democratizing AI governance—regarding their respective prospects and limits in response to algorithmic injustice. My examinations reveal that while some versions of democratizing AI bear the prospect of mitigating the concern of algorithmic injustice, others are somewhat limited and might even function to perpetuate unjust power hierarchies. This analysis thus urges a more fine-grained discussion on how to democratize AI and suggests that closer scrutiny of the power dynamics embedded in the socio-technical structure can help guide such explorations. (shrink)

The article critically analyzes how contemporary image practices involving generative artificial intelligence are entangled with processes of democratization. We demonstrate and discuss how generative artificial intelligence images raise questions of democratization and citizenship in terms of access, skills, validation, truths, and diversity. First, the article establishes a theoretical framework, which includes theory on democratization and aesthetics and lays the foundations for the analytical concepts of ‘formative’ and ‘generative’ visual citizenship. Next, we argue for the use of explorative and (...) collaborative methods to investigate contemporary image practice, before analyzing the central part of our investigation, which takes the form of four collaborative workshops conducted in 2023 with external partners in different domains (the art scene, art therapy, education, and the news media). After analyzing insights from these workshops, the article significantly nuances how visual citizenship is at work in different manners depending on the different concrete image practices using generative artificial intelligence. Finally, we conclude that an aesthetic perspective offers valuable insights into foundational aspects of belonging to contemporary visual communities. (shrink)

The emergence of the “4th Industrial Revolution,” i.e. the convergence of artificial intelligence, the Internet of Things, advanced materials, and bioengineering technologies, could accelerate socioeconomic insecurities and anxieties or provide beneficial alternatives to the status quo. In the post-Covid-19 era, the entities that are best positioned to capitalize on these innovations are large firms, which use digital platforms and big data to orchestrate vast ecosystems of users and extract market share across industry sectors. Nonetheless, these technologies also have the (...) potential to democratize ownership, broaden political-economic participation, and reduce environmental harms. We articulate the potential sociotechnical pathways in this high-stakes crossroads by analyzing cellular agriculture, an exemplary 4th Industrial Revolution technology that synergizes computer science, biopharma, tissue engineering, and food science to grow cultured meat, dairy, and egg products from cultured cells and/or genetically modified yeast. Our exploration of this space involved multi-sited ethnographic research in both the cellular agriculture community and alternative economic organizations devoted to open source licensing, member-owned cooperatives, social financing, and platform business models. Upon discussing how these latter approaches could potentially facilitate alternative sociotechnical pathways in cellular agriculture, we reflect upon the broader implications of this work with respect to the 4th Industrial Revolution and the enduring need for public policy reform. (shrink)

Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks (...) against human advisors, I argue that time-tested democratic procedures, such as deliberation and aggregation by voting, provide safeguards that are effective against human and machine advisor shortcomings alike. Additional protective measures may include custom training for advisor LLMs or boosting representatives’ competencies in query formulation. Implementation of adversarial proceedings in which LLM advisors would debate each other and provide dissenting opinions is likely to yield further epistemic benefits. Overall, promising interventions that would mitigate the LLM risks appear feasible. Machine advisors could thus empower human decision-makers to make more autonomous, higher-quality decisions. On this basis, I defend the hypothesis that LLMs’ careful integration into policymaking could augment democracy’s ability to address today’s complex social problems. (shrink)

The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to partial data collection, rare updates, and significant resource demands. To address these issues, the article suggests that specific data management and Machine Learning techniques, such as natural language processing and sentiment analysis, (...) can improve the measurement and practice of IPD. (shrink)

The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity (like vaccine against COVID-19). In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all (...) the possibilities of supplying, and it is difficult for them to make the right decisions. Such citizen unawareness can be exploited by unscrupulous politicians to achieve personal targets. Therefore, it is necessary to organize public control to motivate politicians to use all available opportunities in supplying. The goal of the paper is to build such a digital mechanism of public control of the politicians by citizens, which would best assess and stimulate the activities of the authorities to improve the supply of a vital commodity. In the age of artificial intelligence, such digital public control in the face of uncertainty can be based on digital machine learning. In addition, it is necessary to take into account and model the activities of politicians associated with the presence of their own targets that do not coincide with public ones. Such politicians can use the learning of citizens for their own targets. The objective of the article is to build an optimal digital mechanism of public control in a two-level model of a democratic social system—a digital society. At its top level, there is the Citizen, who gives an assessment for the Politico located at the lower level. In turn, the Politico can influence the supplying of a vital commodity. Political stability is guaranteed if the Citizen regularly approves of the Politico’s actions to increase this supply. However, the Politico may not use the opportunities available to him to offer a commodity to achieve a personal target. To avoid this, the Politico’s control mechanism is proposed. It includes the procedure for digital learning of the Citizen, as well as a procedure for assessing the Politico activity. Sufficient conditions have been found for the synthesis of such the Politico’s control mechanism, at which stochastic possibilities of increasing the supply of a vital commodity are used. The example of such the Politico’s control mechanism is considered on the case of supply of the COVID-19 vaccine in England. (shrink)

In this contribution we will explore some of the implications of the vision of Ambient Intelligence (AmI) for law and legal philosophy. AmI creates an environment that monitors and anticipates human behaviour with the aim of customised adaptation of the environment to a person’s inferred preferences. Such an environment depends on distributed human and non-human intelligence that raises a host of unsettling questions around causality, subjectivity, agency and (criminal) liability. After discussing the vision of AmI we will present (...) relevant research in the field of philosophy of technology, inspired by the post-phenomenological position taken by Don Ihde and the constructivist realism of Bruno Latour. We will posit the need to conceptualise technological normativity in comparison with legal normativity, claiming that this is necessary to develop democratic accountability for the implications of emerging technologies like AmI. Lastly we will investigate to what extent technological devices and infrastructures can and should be used to achieve compliance with the criminal law, and we will discuss some of the implications of non-human distributed intelligence for criminal liability. (shrink)

In an era defined by events that continuously shake Fukuyama’s thesis according to which liberal democracy constitutes the end of History, there is need for a democratic ideal that puts the role of civic action at the heart of its justification. In this article, I argue that John Dewey’s democratic ideal understood as a matter of civic co-creation, where democratic pursuits are continually redefined by citizens through solving communal problems - not set by history, once and for (...) all - provides a valuable response to this need. To this end, this article reconsiders Deweyan democracy by:(1) presenting it as a transformational process, in opposition to liberal democracy; (2) discussing Dewey’s conception of active citizenship as requiring more than mere political participation; (3) articulating Dewey’s democratic ideal as a form of applied social intelligence; (4) making explicit the pedagogical consequences of Deweyan democracy; and (5) interpreting it as a form of peaceful conflict resolution aiming at balance in inter-personal relationships. (shrink)

Cognitive technology is an umbrella term sometimes used to designate the realm of technologies that assist, augment or simulate cognitive processes or that can be used for the achievement of cognitive aims. This technological macro-domain encompasses both devices that directly interface the human brain as well as external systems that use artificial intelligence to simulate or assist (aspects of) human cognition. As they hold the promise of assisting and augmenting human cognitive capabilities both individually and collectively, cognitive technologies could (...) produce, in the next decades, a significant effect on human cultural evolution. At the same time, due to their dual-use potential, they are vulnerable to being coopted by State and non-State actors for non-benign purposes (e.g. cyberterrorism, cyberwarfare and mass surveillance) or in manners that violate democratic values and principles. Therefore, it is the responsibility of technology governance bodies to align the future of cognitive technology with democratic principles such as individual freedom, avoidance of centralized, equality of opportunity and open development. This paper provides a preliminary description of an approach to the democratization of cognitive technologies based on six normative ethical principles: avoidance of centralized control, openness, transparency, inclusiveness, user-centeredness and convergence. This approach is designed to universalize and evenly distribute the potential benefits of cognitive technology and mitigate the risk that such emerging technological trend could be coopted by State or non-State actors in ways that are inconsistent with the principles of liberal democracy or detrimental to individuals and groups. (shrink)

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, (...) process, and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

National Artificial Intelligence (AI) strategies articulate imaginaries of the integration of AI into society and envision the governing of AI research, development and applications accordingly. To integrate these central aspects of national AI strategies under one coherent perspective, this paper presented an analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality. The first part of the paper analyses how the guiding vision of a human-centric AI not only adheres to ethical and legal (...) principles consistent with Germany’s liberal democratic constitutional system but also addresses the risks and promises inherent to the ordoliberal problematization of freedom. Second, it is scrutinized how the strategy cultivates the fear of not achieving technological sovereignty in the AI sector. Thereby, it frames the global AI race as a race of competing (national) approaches to governing AI and articulates an ordoliberal approach to governing AI (the ‘third way’), according to which government has to operate between the twin dangers of governing too much and not governing enough. Third, the paper analyses how this ordoliberal proportionality of governing structures Germany’s Science Technology & Innovation Policy. It is shown that the corresponding risk-based approach of regulating AI constitutes a security apparatus as it produces an assessment of fears: weighting the fear of the failure to innovate with the fear of the ramifications of innovation. Finally, two lines of critical engagement based on this analysis are conducted. (shrink)

The rapid development of programs based on the principles of machine learning (ML) and artificial intelligence (AI) signals significant changes in the components of education, namely in the provider, the tool of transmission, and the recipient of knowledge. Historical data analysis regarding the key functions of education serves as the basis for identifying fundamental innovations introduced through AI and ML. The impact of writing, printing, and the Internet has significantly altered the tool for knowledge transmission, influencing the volume of (...) information and the number of knowledge recipients. The implementation of AI and ML transforms not only the tool but also the provider of knowledge itself, which can become impersonal thanks to the corresponding computer programs. With the historically justified increase in the volume of knowledge possessed by humanity, there is a transformation observed in education systems. This is especially true for democratic societies, where the emphasis is increasingly shifting from providing a large amount of knowledge to developing critical thinking. It has been researched that programs based on AI and ML, applying linguistic models, are capable of effectively systematizing knowledge. This lays the foundation for personalizing the entire education process for a specific knowledge recipient, without burdening the provider. However, there have been cases when such imitation misleads scientists, who perceive it as attempts at communication between programs that have an equivalence to the human level. The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs. However, intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge. (shrink)

The use of artificial intelligence in healthcare contexts is highly controversial for the (bio)ethical conundrums it creates. One of the main problems arising from its implementation is the lack of transparency of machine learning algorithms, which is thought to impede the patient’s autonomous choice regarding their medical decisions. If the patient is unable to clearly understand why and how an AI algorithm reached certain medical decision, their autonomy is being hovered. However, there are alternatives to prevent the negative impact (...) of AI opacity in shared (healthcare professional–patient) decision-making processes, and benefit from the high accuracy of such systems. Despite these ethical issues, this paper advocates for its usage and also defends the positive role that it could have in enhancing patients’ autonomy. I advocate for a system where the implementation of AI is only partial, and humans will remain in control of oversighting algorithm interpretation of medical images and patients’ data. (shrink)

This article links three rarely considered dimensions related to the implementation of artificial intelligence (AI)-based technologies in the form of predictive policing and discusses them in relation to liberal democratic societies. The three dimensions are the theoretical embedding and the workings of AI within anomic conditions (1), potential normative disorders emerging from them in the form of thinking errors and discriminatory practices (2) as well as the consequences of these disorders on the psychosocial, and emotional level (3). Against (...) this background, AI-induced anomie is conceptualized as a field of tension that refers to a systematic deterioration of democratic norms that are supposed to create ‘normative orders’, but which, when implemented through AI-supported measures, can reproduce existing discriminations, and establish new kinds of discriminatory relations. In future, these AI-based measures have the potential to lead to opposing normative disorders by emerging in the form of false social norms to an equally false Second Nature. They deprive persons involved of the possibility of individual appropriation of social norms and the specific emotional development associated with it. (shrink)

In an era defined by the global surge in the adoption of AI-enabled technologies within public administration, the promises of efficiency and progress are being overshadowed by instances of deepening social inequality, particularly among vulnerable populations. To address this issue, we argue that democratizing AI is a pivotal step toward fostering trust, equity, and fairness within our societies. This article navigates the existing debates surrounding AI democratization but also endeavors to revive and adapt the historical social justice framework, maximum feasible (...) participation, for contemporary participatory applications in deploying AI-enabled technologies in public administration. In our exploration of the multifaceted dimensions of AI’s impact on public administration, we provide a roadmap that can lead beyond rhetoric to practical solutions in the integration of AI in public administration. (shrink)

BackgroundIt has been argued that ethics review committees—e.g., Research Ethics Committees, Institutional Review Boards, etc.— have weaknesses in reviewing big data and artificial intelligence research. For instance, they may, due to the novelty of the area, lack the relevant expertise for judging collective risks and benefits of such research, or they may exempt it from review in instances involving de-identified data.Main bodyFocusing on the example of medical research databases we highlight here ethical issues around de-identified data sharing which motivate (...) the need for review where oversight by ethics committees is weak. Though some argue for ethics committee reform to overcome these weaknesses, it is unclear whether or when that will happen. Hence, we argue that ethical review can be done by data access committees, since they have de facto purview of big data and artificial intelligence projects, relevant technical expertise and governance knowledge, and already take on some functions of ethical review. That said, like ethics committees, they may have functional weaknesses in their review capabilities. To strengthen that function, data access committees must think clearly about the kinds of ethical expertise, both professional and lay, that they draw upon to support their work.ConclusionData access committees can undertake ethical review of medical research databases provided they enhance that review function through professional and lay ethical expertise. (shrink)

There is an abundance of enthusiasm and optimism about how governments at all levels can make use of big data, algorithms and artificial intelligence. There is also growing concern about the risks that come with these new systems. This article makes the case for greater government transparency and accountability about uses of big data through a Government of Canada qualitative research case study. Adapting a method from critical cartographers, I employ counter-mapping to map government big data practices and internal (...) discussions of risk and challenge. I do so by drawing on interviews and freedom of information requests. The analysis reveals that there are more concerns and risks than often publicly discussed and that there are significant areas of silence that need greater attention. The article underlines the need for our democratic systems to respond to our new datafied contexts by ensuring that our institutions make changes to better protect citizen rights, uphold democratic principles and ensure means for citizen intervention. (shrink)

Much scholarly attention has recently been devoted to ways in which artificial intelligence (AI) might weaken formal political democracy, but little attention has been devoted to the effect of AI on “cultural democracy”—that is, democratic control over the forms of life, aesthetic values, and conceptions of the good that circulate in a society. This work is the first to consider in detail the dangers that AI-driven cultural recommendations pose to cultural democracy. This Article argues that AI threatens to (...) weaken cultural democracy by undermining individuals’ direct and spontaneous engagement with a diverse range of cultural materials. It further contends that United States law, in its present form, is ill equipped to address these challenges, and suggests several strategies for better regulating culture-mediating AI. Finally, it argues that while such regulations might run afoul of contemporary First Amendment doctrine, the most normatively attractive interpretation of the First Amendment not only allows but encourages such interventions. (shrink)

In the United States, the Presidential Commission for the Study of Bioethical Issues has proposed deliberative democracy as an approach for dealing with ethical issues surrounding synthetic biology. Deliberative democracy might similarly help us as we update the regulation of human subjects research. This paper considers how the values that deliberative democratic engagement aims to realize can be realized in a human subjects research context. Deliberative democracy is characterized by an ongoing exchange of ideas between participants, and an effort (...) to justify decisions that bind participants by appeal to reasons that the participants can understand and share. Even when unanimous agreement is not reached, the active participation of everyone along with the requirement that reasons be made accessible enhances the legitimacy of the ultimate outcome. Importantly, deliberative democratic structures avoid strict hierarchies and place participants, as much as possible, in the position of equals. Human subjects research has some features that may make deliberative democratic principles seem initially unappealing. For one, there are asymmetries in knowledge between expert researchers conducting the research and participants in the research process. For another, statistical validity is made easiest by research paradigms that produce standardized, quantitative data, which can be difficult to achieve if research participants are given the power to deliberatively reshape the research design as it progresses. These and other problems have tended to produce a human subjects research process where subjects do not actively participate in shaping research, but rather consent to a predefined set of interventions designed by expert researchers and vetted by Institutional Review Boards (IRBs). In this paper, I suggest some ways in which human subjects research could do more to realize deliberative democratic values, and, in particular, how a revised Common Rule might help to realize these values. First, research participants could be treated not as passive subjects but instead involved in research design, ethical review, and the ongoing conduct and dissemination of research. Such participation might involve, for instance, including people who have served as research subjects on IRBs, or replacing IRB oversight for certain forms of research exempted from IRB oversight under a revised Common Rule with oversight by a body of community members or research subjects. It might also involve having the oversight of research that uses more participatory models be more participatory and less hierarchical in nature. I also raise questions about the exemption of research on public benefit programs from any research-level oversight and from consent requirements. While IRBs are likely not the correct overseers, there may be good reason to view such research with a critical eye, because of its potential for long-range impacts on the lives of participants. By giving research subjects a greater voice in research that aims at fine-tuning public benefit programs on which subjects rely, a deliberative oversight process has the potential to recast research participation as a form of active democratic participation and to address a “democratic deficit” in public health. Numerous proposals regarding health care have called for greater participation by laypeople and a more nonhierarchical approach to setting health priorities. Involving lay research subjects in the conduct of public benefits research and other forms of public health research may help to further these goals. (shrink)

In a recent article, Carolyn Hendricks and Lyn Carson begin to remedy the deficit of literature on deliberative democracy consultancy, or the provision of deliberation goods and services for a fee, by observing that the competitive, entrepreneurial and business-driven nature of this growing deliberative industry might threaten those conditions for generating an open and participatory process of democratic governance. Building on their important contribution to the literature, the present paper provides a parallel assessment based on John Dewey's notions of (...) public spirit and socialized intelligence. While the growth of the deliberative consultancy industry might appear dangerous to the prospects for an engaged citizenry and open government, the specter, I argue, is more apparent than real, the result of two implicit dualisms, one between citizens and experts and the other between the individual and the community. Some might claim that Dewey's critique of the epistemological industry also applies to the deliberation industry. However, this is not the case. Deliberative consultancy pertains to the business enterprise behind deliberative democracy, not the academic enterprise of producing knowledge about deliberative democracy. Dewey's notion of public spirit defuses the tension between citizen and expert, or deliberators and consultants, by understanding them as engaged in collaborative partnerships. Moreover, Dewey's idea of socialized intelligence reunites the individual and her community, or the deliberative entrepreneur and the larger community of inquirers. In this way, the threat, in Jon Elster's terminology, that the market poses to the forum does not manifest in the relationship between deliberative consultants and deliberating publics so long as a few ethical norms guide the practice of deliberative consultancy. (shrink)