We introduce a reciprocity criterion for coalition formation among goal-directed agents, which we call the indecomposable do-ut-des property. It refines an older reciprocity property, called the do-ut-des or give-to-get property by considering the fact that agents prefer to form coalitions whose components cannot be formed independently. A formal description of this property is provided as well as an analysis of algorithms and their complexity. We provide an algorithm to decide whether a coalition has the desired property, and we show (...) that the problem to verify whether a single coalition satisfies the property is tractable. Moreover, we provide an algorithm to search all the sub-coalitions of a given coalition satisfying the new property. Even if this problem is not computationally tractable, we show that in several cases, also the complexity of this problem may decrease considerably. (shrink)

We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis that (...) people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies. (shrink)

The strategies of action employed by a human subject in order to perceive simple 2-D forms on the basis of tactile sensory feedback have been modelled by an explicit computer algorithm. The modelling process has been constrained and informed by the capacity of human subjects both to consciously describe their own strategies, and to apply explicit strategies; thus, the strategies effectively employed by the human subject have been influenced by the modelling process itself. On this basis, good qualitative and semi-quantitative (...) agreement has been achieved between the trajectories produced by a human subject, and the traces produced by a computer algorithm. The advantage of this reciprocal modelling option, besides facilitating agreement between the algorithm and the empirically observed trajectories, is that the theoretical model provides an explanation, and not just a description, of the active perception of the human subject. (shrink)

Witnessing in merging biological, social and algorithmic realities is crucial to trust, as modelled in the YUTPA framework. Being witness and bearing witness is fundamental to human interaction. System participation in human communities of practice challenges the notion of witnessing and therefore the ability to build trust. Nevertheless, through trial and error, people in a variety of practices have found ways to establish the presence and develop trust in merging realities. This paper presents the results of 20 in-depth interviews with (...) professionals from a variety of disciplines and nations. The conclusion of cumulative analysis is that systems do not witness themselves, but their output deeply affects the mental maps that human beings make of each other, the world around them and their own self. Essential qualities human beings seek when being involved with other beings are defined by granularity and reciprocity in the design of time (duration of engagement, synchronizing performance, integrating rhythms and moments to signify), place (body sense, material interaction, emotional space and situated agency), relation (shared meaning, engagement, reputation and use) and action (tuning, reciprocity, negotiation and quality of deeds). By designing granular interaction in 4 dimensions, reciprocity in witnessing obtains significance and the basis for establishing trust in a variety of presences emerges while human agency acquires potential. (shrink)

In our contemporary moment, when machine learning algorithms are reshaping many aspects of society, the work of N. Katherine Hayles stands as a powerful corpus for understanding what is at stake in a new regime of computation. A renowned literary theorist whose work bridges the humanities and sciences among her many works, Hayles has detailed ways to think about embodiment in an age of virtuality, how code as performative practice is located, and the reciprocal relations among human bodies (...) and technics. This special issue follows the 2017 publication of her book Unthought: The Power of the Cognitive Nonconscious, in which Hayles traces the nonconscious cognition of biological life-forms and computational media. The articles in the special issue respond in different ways to Hayles’ oeuvre, mapping the specific contours of computational regimes and developing some of the ‘inflection points’ she advocates in the deep engagement with technical systems. (shrink)

This article explores the critical limitations of the now decades-long shift toward digital culture in the material and cultural constitution of biotechnology. It does this by telling the story of three contemporary efforts to reimagine the logic of life on the logic of the digital and the struggles attendant to building the infrastructures needed to actualize that re-imagination and make it profitable. In tracing these stories, it lifts out how biotechnologists, once caught in the spell of the digital sublime, are (...) having to confront the ways in which dreams of a data-driven and automated biology, far from eliciting an exalted future of health, wealth, and security, are simply reinforcing at the level of the organism a world already boxed in by algorithmic modes of reason and governed by imperatives of optimization. In the midst of these binds, this article points to a pathos which is beginning to stir at the bench as blue-collar technologists seek to activate an older feel for craftwork, and how this pathos signals a familiar but vital reciprocity between the maker and the made. It concludes that, in the end, breakdowns in the digital sublime make space for a more interesting biotechnical proposition, one in which the living thing being crafted has to be accommodated rather than rendered algorithmic: the cellular targets of biotechnical intervention must be coerced, cajoled, and enticed into playing along with our biotechnical imaginations. To act differently proves to be the real source of our trouble. The stories in this article draw on fieldwork conducted by the author through multiple experiments in collaborative anthropology. These experiments began in connection to a then-emerging brand of biotechnology called synthetic biology, were carried forward in view of a “post-engineering” approach to systems biology, and are currently (as of the writing of this essay) being conducted among biologists and technologists working to build “digital infrastructures” for the life sciences (i.e., digital tools for assembling, mediating, and synthesizing post-genomic biology). (shrink)

This paper builds upon a critically clarified statement of the triune brain concept to set out the conflict systems neurobehavioral model. The model defines the reciprocal algorithms of behavior from evolved brain structure. The algorithms are driven by subjectively experienced behavioral tension as the self‐preservational programming, common to our ancestral vertebrates, frequently tugs and pulls against the affectional program‐ming of our mammalian legacy. The yoking of the dual algorithmic dynamic accounts for the emergence of moral and spiritual (...) consciousness as manifested in the universal norm of reciprocity and in the work of such thinkersas Martin Buber and Paul Tillich. (shrink)

Paul MacLean, founder and long-time chief ofthe Laboratory of Brain Evolution and Behavior,National Institutes of Health, is a pioneeringfigure in the emergent field of evolutionaryneuroscience. His influence has been widelyfelt in the development of biologicalpsychiatry and has led to a considerableliterature on evolutionary approaches toclinical issues. MacLean's work is alsoenjoying a resurgence of interest in academicareas of neuroscience and evolutionarypsychology which have previously shown littleinterest or knowledge of his extensive work. This chapter builds on MacLean's work to bringtogether new insights (...) into the neuralarchitecture of human development, hierarchy,conflict behavior, and reciprocity in the formof the Conflict Systems Neurobehavioral Model. Hamilton 's rule of kinship altruism orinclusive fitness is proposed to be the gene'seye complement to MacLean's evolutionaryneuroscience and the CSN Model derivedtherefrom. Hierarchy, conflict behavior andreciprocity are also central issues in healthydevelopment as well as in clinical syndromes ofdepression, mania, and other socialmaladjustments. The emerging insights permitthe integration of the concept of inclusivefitness underpinning evolutionary psychologywith MacLean's perspective on evolutionaryneuroscience as well as the definition of newchallenges for mental health and socialstability. The policy implications areindicated. (shrink)

The article investigates the interplay of moral rules in computer simulation. The investigation is based on two situations which are well-known to game theory: the prisoner''s dilemma and the game of Chicken. The prisoner''s dilemma can be taken to represent contractual situations, the game of Chicken represents a competitive situation on the one hand and the provision for a common good on the other. Unlike the rules usually used in game theory, each player knows the other''s strategy. In that way, (...) ever higher levels of reflection are reached reciprocally. Such strategies can be interpreted as moral rules.Artificial morality is related to the discipline of Artificial Life. As in artificial life, the use of genetic algorithms suggests itself. Rules of behaviour split and reunite as chromosome strings do. (shrink)

In the dissertation we study the complexity of generalized quantifiers in natural language. Our perspective is interdisciplinary: we combine philosophical insights with theoretical computer science, experimental cognitive science and linguistic theories. -/- In Chapter 1 we argue for identifying a part of meaning, the so-called referential meaning (model-checking), with algorithms. Moreover, we discuss the influence of computational complexity theory on cognitive tasks. We give some arguments to treat as cognitively tractable only those problems which can be computed in polynomial (...) time. Additionally, we suggest that plausible semantic theories of the everyday fragment of natural language can be formulated in the existential fragment of second-order logic. -/- In Chapter 2 we give an overview of the basic notions of generalized quantifier theory, computability theory, and descriptive complexity theory. -/- In Chapter 3 we prove that PTIME quantifiers are closed under iteration, cumulation and resumption. Next, we discuss the NP-completeness of branching quantifiers. Finally, we show that some Ramsey quantifiers define NP-complete classes of finite models while others stay in PTIME. We also give a sufficient condition for a Ramsey quantifier to be computable in polynomial time. -/- In Chapter 4 we investigate the computational complexity of polyadic lifts expressing various readings of reciprocal sentences with quantified antecedents. We show a dichotomy between these readings: the strong reciprocal reading can create NP-complete constructions, while the weak and the intermediate reciprocal readings do not. Additionally, we argue that this difference should be acknowledged in the Strong Meaning hypothesis. -/- In Chapter 5 we study the definability and complexity of the type-shifting approach to collective quantification in natural language. We show that under reasonable complexity assumptions it is not general enough to cover the semantics of all collective quantifiers in natural language. The type-shifting approach cannot lead outside second-order logic and arguably some collective quantifiers are not expressible in second-order logic. As a result, we argue that algebraic (many-sorted) formalisms dealing with collectivity are more plausible than the type-shifting approach. Moreover, we suggest that some collective quantifiers might not be realized in everyday language due to their high computational complexity. Additionally, we introduce the so-called second-order generalized quantifiers to the study of collective semantics. -/- In Chapter 6 we study the statement known as Hintikka's thesis: that the semantics of sentences like ``Most boys and most girls hate each other'' is not expressible by linear formulae and one needs to use branching quantification. We discuss possible readings of such sentences and come to the conclusion that they are expressible by linear formulae, as opposed to what Hintikka states. Next, we propose empirical evidence confirming our theoretical predictions that these sentences are sometimes interpreted by people as having the conjunctional reading. -/- In Chapter 7 we discuss a computational semantics for monadic quantifiers in natural language. We recall that it can be expressed in terms of finite-state and push-down automata. Then we present and criticize the neurological research building on this model. The discussion leads to a new experimental set-up which provides empirical evidence confirming the complexity predictions of the computational model. We show that the differences in reaction time needed for comprehension of sentences with monadic quantifiers are consistent with the complexity differences predicted by the model. -/- In Chapter 8 we discuss some general open questions and possible directions for future research, e.g., using different measures of complexity, involving game-theory and so on. -/- In general, our research explores, from different perspectives, the advantages of identifying meaning with algorithms and applying computational complexity analysis to semantic issues. It shows the fruitfulness of such an abstract computational approach for linguistics and cognitive science. (shrink)

We explain metacognition as a management of cognitive resources that does not necessitate algorithmic strategies or metarepresentation. When pragmatic, world-directed actions cannot reduce the distance to the goal, agents engage in epistemic action directed at cognition. Such actions often are physical and involve other people, and so are open to observation. Taking a dynamic systems approach to development, we suggest that implicit and perceptual metacognition emerges from dyadic reciprocal interaction. Early intersubjectivity allows infants to internalize and construct rudimentary strategies (...) for monitoring and control of their own and others’ cognitions by emotion and attention. The functions of initiating, maintaining, and achieving turns make proto-conversation a productive platform for developing metacognition. It enables caregiver and infant to create shared routines for epistemic actions that permit training of metacognitive skills. The adult is of double epistemic use to the infant – as a teacher that comments on and corrects the infant’s efforts, and as the infant’s cognitive resource in its own right. (shrink)

The system called F is essentially a sub-theory of Frege Arithmetic without the ad infinitum assumption that there is always a next number. In a series of papers (Systems for a Foundation of Arithmetic, True” Arithmetic Can Prove Its Own Consistency and Proving Quadratic Reciprocity) it was shown that F proves a large number of basic arithmetic truths, such as the Euclidean Algorithm, Unique Prime Factorization (i.e. the Fundamental Law of Arithmetic), and Quadratic Reciprocity, indeed a sizable amount of arithmetic. (...) In particular, F proves some (but not all) of the Peano Axioms; that is, F proves the axioms of a sub-theory - call it FPA - of second-order Peano-Arithmetic. This short technical note will demonstrate that the converse also holds, in the following sense. F has the same language as second-order Peano Arithmetic except that, in addition, it has a two-place predicate symbol “Μ”. Then it is possible to provide a definition, indeed a reasonable definition, for “Μ” such that FPA proves all the axioms of F. So F and FPA effectively have the same proof-theoretic strength. In particular FPA, which lacks the Successor Axiom stating that every natural number has a successor, is able to prove the Euclidean Algorithm, Unique Prime Factorization, and Quadratic Reciprocity, indeed (again) a sizable amount of arithmetic. (shrink)

What could a social-justice oriented, feminist data studies look like? The current datalogical turn foregrounds the digital datafication of everyday life, increasing algorithmic processing and data as an emergent regime of power/knowledge. Scholars celebrate the politics of big data knowledge production for its omnipotent objectivity or dismiss it outright as data fundamentalism that may lead to methodological genocide. In this feminist and postcolonial intervention into gender-, race- and geography-blind ‘big data’ ideologies, I call for ethical, anti-oppressive digital data-driven research in (...) the social sciences and humanities. I argue that a reflexive data scholarship can emerge from the reintegration of feminist and postcolonial science studies and ethics of care ideals. Although it is not a panacea for all ails of data mining, I offer a road map for an alternative data-analysis practice that is more power-sensitive and accountable. By incorporating a people-centric and context-aware perspective that acknowledges relationships of dependency, reflects on temptations, and scrutinises benefits and harm, an ‘asymmetrically reciprocal’ (Young, 1997) research encounter may be achieved. I bring this perspective to bear on experiences of a two-year research project with eighty-four young Londoners on digital identities and living in a highly diverse city. I align awareness of uneven relations of power and knowledge with the messy relation of dependency between human and non-human actors in data analysis. This framework productively recognises that digital data cannot be expected to speak for itself, that data do not emerge from a vacuum, and that isolated data patterns cannot be the end-goal of a situated and reflexive research endeavor. Data-driven research, in turn, shows the urgency for renewed feminist ethical reflection on how digital mediation impacts upon responsibility, intersectional power relations, human subjectivity and the autonomy of research participants over their own data. (shrink)

The title of this paper is inspired by the book edited by Domenico Fiormonte entitled Canoni liquidi (Liquid Canons). Of course, the adjective “liquid” refers to Zygmunt Bauman’s term at which my critique is also indirectly aimed. The title of Fiormonte’s book seems to suggest equivalence between textual “mobility” and “liquidity.” Yet the “liquefying” of (literary) canons and the emergence of new intrinsically kinetic or fluid forms of mobile textuality requires a critical assessment that does not prematurely celebrate the funeral (...) of the text as we know it but pays close attention to what seems to be waiting for us beyond the text, as suggested in Francesco Fiorentino’s recent edited volume. This means paying attention to what in my recent book, Il futuro della letteratura (The Future of Literature), I call digital incunabula: objects/textual tools, devices programmed in a twofold, reciprocal sense, whose linguistic properties and rules interact and interfere with the algorithmic procedures of artificial languages. In this programmed and programmable interaction and intermediation one can perceive the horizon of incomprehensibility of posthuman language, providing a new aporetic dimension to Gadamer’s assertion that “the being that can be understood is language.“ This assertion is by now the most significant issue in the horizon of literary (digital) arts. (shrink)

We present an approach to subjective computing for the design of future robots that exhibit more adaptive and flexible behavior in terms of subjective intelligence. Instead of encapsulating subjectivity into higher order states, we show by means of a relational approach how subjective intelligence can be implemented in terms of the reciprocity of autonomous self-referentiality and direct world-coupling. Subjectivity concerns the relational arrangement of an agent’s cognitive space. This theoretical concept is narrowed down to the problem of coaching a reinforcement (...) learning agent by means of binary feedback. Algorithms are presented that implement subjective computing. The relational characteristic of subjectivity is further confirmed by a questionnaire on human perception of robot’s behavior. The results imply that subjective intelligence cannot be externally observed. In sum, we conclude that subjective intelligence in relational terms is fully tractable and therefore implementable in artificial agents. (shrink)

Complex, multigenic biological traits are shaped by the emergent interaction of proteins being the main functional units at the molecular scale. Based on a phenomenological approach, algorithms for quantifying two different aspects of emergence were introduced (Wegner and Hao in Progr Biophys Mol Biol 161:54–61, 2021) describing: (i) pairwise reciprocal interactions of proteins mutually modifying their contribution to a complex trait (denoted as weak emergence), and (ii) formation of a new, complex trait by a set of n ‘constitutive’ (...) proteins at concentrations exceeding individual threshold values (strong emergence). The latter algorithm is modified here to take account of protein redundancy with respect to a complex trait (‘full redundancy’). Irreducibility is considered a necessary and sufficient criterion for strong biological emergence; if one constitutive protein is missing, or its concentration drops below the threshold the trait is lost. A definition based on ‘unpredictability’ is dismissed, because this criterion is irrelevant for the evolution of a complex trait, and apparent unpredictability may rather reflect our basic deficits in understanding unless we can provide an unequivocal proof for it. The phenomenological approach advocated here allows to identify hidden rules according to which strongly emergent traits may be organized. This is of high value for understanding the evolution of complex traits which seems to require the saltational advent of all constitutive proteins ‘in one turn’ to arrive at a functional trait providing for an improved fitness of the organism. Rather than being a purely random process, it may be guided by fundamental structural principles. (shrink)

Mindfulness and compassion meditation are thought to cultivate prosocial behavior. However, the lack of diverse representation within both scientific and participant populations in contemplative neuroscience may limit generalizability and translation of prior findings. To address these issues, we propose a research framework calledIntersectional Neurosciencewhich adapts research procedures to be more inclusive of under-represented groups. Intersectional Neuroscience builds inclusive processes into research design using two main approaches: 1) community engagement with diverse participants, and 2) individualized multivariate neuroscience methods to accommodate neural (...) diversity. We tested the feasibility of this framework in partnership with a diverse U.S. meditation center (East Bay Meditation Center, Oakland, CA). Using focus group and community feedback, we adapted functional magnetic resonance imaging (fMRI) screening and recruitment procedures to be inclusive of participants from various under-represented groups, including racial and ethnic minorities, gender and sexual minorities, people with disabilities, neuropsychiatric disorders, and/or lower income. Using person-centered screening and study materials, we recruited and scanned 15 diverse meditators (80% racial/ethnic minorities, 53% gender and sexual minorities). The participants completed the EMBODY task – which applies individualized machine learning algorithms to fMRI data – to identify mental states during breath-focused meditation, a basic skill that stabilizes attention to support interoception and compassion. All 15 meditators’ unique brain patterns were recognized by machine learning algorithms significantly above chance levels. These individualized brain patterns were used to decode the internal focus of attention throughout a 10-min breath-focused meditation period, specific to each meditator. These data were used to compile individual-level attention profiles during meditation, such as the percentage time attending to the breath, mind wandering, or engaging in self-referential processing. This study provides feasibility of employing an intersectional neuroscience approach to include diverse participants and develop individualized neural metrics of meditation practice. Through inclusion of more under-represented groups while developing reciprocal partnerships, intersectional neuroscience turns the research process into an embodied form of social action. (shrink)

This book presents consciousness models from Eastern and Western perspectives that accommodate current scientific research in the natural sciences and humanities, from neurological experiments through philosophical enquiries to spiritual approaches. It offers up to date research from key disciplines in consciousness studies ranging from neurology, quantum mechanics, algorithmic science, mathematics, and astrophysics to literary studies, philosophy, and (comparative) theology. The volume examines the dichotomy between Western and Eastern perceptions of consciousness – where consciousness is perceived as brain activity by Western (...) scientists, and as a divine presence by various religions, especially in the East. The essays contextualize each other and reciprocally illuminate the potential and limits of the respective approaches. The texts aim at a transdisciplinary and transcultural exchange of ideas in consciousness studies and address a readership from interested lay-readers to experts of the field. The volume is of interest to researchers of consciousness studies. (shrink)

Functionalism, which views consciousness as the product of the processing of stimuli by the brain, is perhaps the dominant view among researchers in the cognitive sciences and associated fields. However, as a workable scientific model of consciousness, it has been marred by a singular lack of tangible success, except at the broadest levels of explanation. This paper argues that this is not an accident, and that in its standard construal it is simply too unwieldy to assume the burden of full-fledged (...) theory. In its place, a reduced functionalism is introduced by applying the principle of parsimony successively to the elements of standard functionalism until only a minimal framework remains. This simpler account states that consciousness is a function of instantaneous causal relations between processing elements rather the putative algorithm such relations are instantiating. It is then argued as a corollary that the only such relations that matter are those in which reciprocal influences are at play. Thus, purely afferent and efferent causal relations are pruned from consideration. The theory resulting from the addition of this corollary is shown to have good correspondence with a number of recent neurophysiologically-motivated approaches to consciousness, including those that stress the importance of reentry, those that view synchrony as a key independent variable, and those that highlight the importance of the accessibility of conscious contents to multiple processing modules. In addition, the theory is shown to be consistent with recent results in the literature on masking, and those in the literature on binocular rivalry. The paper concludes by arguing that the theoretical and empirical difficulties inherent in consciousness research imply that the principle of parsimony must occupy a more central role in consciousness research than it would in ordinary scientific discourse. (shrink)

Against the backdrop of an aging world population increasingly affected by a diverse range of abilities and disabilities as well as the rise of ubiquitous computing and digital app cultures, this paper questions how mobile technologies mediate between heterogeneous environments and sensing beings. To approach the current technological manufacturing of the senses, two lines of thought are of importance: First, there is a need to critically reflect upon the concept of assistive technologies as artifacts providing tangible solutions for a specific (...) disability. Second, the conventional distinction between user and environment requires a differentiated consideration. This contribution will first review James Gibson’s concept of “affordances” and modify this approach by introducing theories and methods of Science and Technology Studies and Actor-Network Theory. Then, we present two case studies where we explore the relations between recent “assistive” app technologies and human sensory perception. As hearing and seeing are key in this regard, we concentrate on two specific media technologies: ReSound LINX2, a hearing aid which allows for direct connect with iPhone, iPad, or iPod Touch, and Camassia, an IOS app for sonic wayfinding for blind people. We emphasize the significance of dis-/abling practices for manufacturing novel forms of hearing and seeing and drawing on sources like promotional materials by manufacturers, ads, or user testimonials and reviews. Our analysis is interested in the reciprocal relationships between users and their socio-technical and media environments. By and large, this contribution will provide crucial insights into the contemporary entanglement of algorithm-driven technologies, daily practices, and sensing subjects: the production of techno-sensory arrangements. (shrink)

The aim of the paper is to identify and categorize frequent patterns describing interactions between users in social networks. We analyze a social network with relationships between users that evolve in time already identified. In our research, we discover patterns based on frequent interactions between groups of users. The patterns are described by the characteristics of these interactions, such as their reciprocity, or the relative difference between estimations of global influences of the users participating in the discussions. The modification of (...) the apriori algorithms is applied as one of the methods for pattern identification. The analyzed social network is built using the data set containing data from the Polish blog website salon24, which concerns mostly socio-political issues. (shrink)

Since the emergence of digital design techniques in combination with so-called responsive materials, the concept of organic forms in architecture seems to be gaining a new quality. The resemblance to an organism should no longer apply only superficially but be inscribed in the materiality as well as in the history of origin and functioning. This article addresses these new transformative effects between architecture and biology. They are presented primarily in relation to the structural architecture of the 1960s and the computational (...) architectural systems since the 1990s. One focus of architecture is on dynamic forms that adapt themselves to their environment by means of flexible materials and generative algorithms. Here, architecture as technically animated matter no longer involuntarily competes with creative nature but is seen as part of a reciprocal relationship. This reciprocal relationship is specified by recourse to various architectural models. The models’ approaches suggest that organic-looking forms are generated by simulated biological processes. The article examines this claim of the models from the perspective of the history of architecture and design. It shows how, since the mid-twentieth century, a renewal of architectural design practice has been sought by reformulating morphological questions at the intersection of biological and cybernetic discourses. (shrink)

This book traces and explores the evolution of taste from a design perspective: what it is, how it works and what it does. Karin Tehve examines taste primarily through its recursive relationship to media. This ongoing process changes the relationship between designers and the public, and our understanding of the relationship of individuals to their social contexts. Through an analysis of taste, design is understood to be an active constituent of social life, not as autonomous from it. This book reclaims (...) a term long dismissed from interior design and unveils taste's role as a powerful social and political agent within systems of aesthetics, affecting both its producers and consumers. Each chapter discusses a taste concept or definition, analyzes its reciprocal relationship with media, and explores its implications for interior design. Illustrated with 70 images, taste's relationship to media is viewed through a variety of different lenses, including books, photography, magazines, internet, social media and algorithms. Written for students and scholars of interior design and related design fields, this book will be a helpful resource for all those interested in the question of taste, and is an invitation to produce and consume all media critically. (shrink)

Traditional art history equates the production of the memes of visual art exclusively with individual consciousness. An evolutionary art history looks also at the patterns into which art-memes flow patterns that display striking synchronicities of cultural motivation, both within the arts themselves and between the arts and sciences parallel patterns of collective memetic behaviour. We are all familiar with short-wave collective behaviour: football fans, evangelist meetings, political demonstrations, rioting crowds and panicking troops all display it. This paper is concerned with (...) long-wave collective behaviour, as evidenced in the migration of Western elite culture from the open to the closed meme in the arts and sciences of the Renaissance period, or again in the reciprocal cultural migration from closed to open memes going on in the arts and sciences at the present time. How do we explain this swarming of the memes, such that artistic and scientific communication, despite their explicitly different forms, seem invisibly joined at the hip? Why do our memes rock'n'roll topologically between open and closed states? The paper suggests that memetic swarming the synchronization of our memes in alternating open and closed states may be an expression of the sensitivity of our consciousness to the changing dynamics of our own relationship with the planetary biosphere through ecological evolution. If so, the ultimate driver of memetic swarming may be an algorithm as commonplace and as universal as the figureground opposition: linking our evolution with our memes by way of a simple principle of reciprocity, such that consciousness will tend to output closed memes in periods of open evolution, and, reciprocally, closed memes in periods of open evolution. (shrink)

This article proposes a new bi-directional way of understanding the convergence of biology and computing. It argues for a reciprocal interaction in which biology and computing have shaped and are currently reshaping each other. In so doing, we qualify both the view of a natural marriage and of a digital shaping of biology, which are common in the literature written by scientists, STS, and communication scholars. The DNA database is at the center of this interaction. We argue that DNA (...) databases are spaces of convergence for computing and biology that change in form, meaning, and function from the 1960s to the 2000s. The first part of the article shows how, in the 1980s, DNA sequencing shifted from passively incorporating computers to be increasingly modeled in digital coding and decoding. Information retrieval algorithms, reciprocally, were altered according to the peculiarities of DNA in the first sequence-storage databases. The second part of the article investigates the impact of these reciprocal interactions and globalization on the organization of research centers, ways of conducting big science, and scientific values. Through convergence and new technologies such as data mining, biology and computing were transformed technologically, institutionally, and culturally into a new bio-data enterprise called genomics. (shrink)

Altruism and cynicism are two fundamental algorithms of moral decision-making. This derives from the evolution of cooperative behavior and reciprocal altruism and the need to avoid being taken advantage of. Rushton (1986) developed a self-report scale to measure altruism, however no scale to measure cynicism has been developed for use in ethics research. Following a discussion of reciprocal altruism and cynicism, this article presents an 11-item self-report scale to measure cynicism, developed and validated using a sample of (...) 271 customer-service and sales personnel. (shrink)

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. (...) In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

Algorithms wield increasing control over our lives—over which jobs we get, whether we're granted loans, what information we're exposed to online, and so on. Algorithms can, and often do, wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has gone largely neglected. I investigate three questions about algorithmic neutrality: What is it? Is it possible? And when we have it in mind, what can we learn about algorithmic (...) bias? (shrink)

In Algorithms & Autonomy, Rubel, Castro, and Pham (hereafter RCP), argue that the concept of autonomy is especially central to understanding important moral problems about algorithms. In particular, autonomy plays a role in analyzing the version of social contract theory that they endorse. I argue that although RCP are largely correct in their diagnosis of what is wrong with the algorithms they consider, those diagnoses can be appropriated by moral theories RCP see as in competition with their (...) autonomy based theory. Most notably, proponents of consequentialism and virtue ethics can appropriate RCPs insights. This is good news because RCP’s social contract theory is vulnerable to a well known class of counterexamples. The most significant contribution of RCP, if I am right, is in their identification, presentation, and evaluation of concrete cases involving algorithms and not in the more controversial claims about theoretical ethics that RCP themselves see as central to what they are doing. (shrink)

The tendency to reciprocate – to return good for good and evil for evil – is a potent force in human life, and the concept of reciprocity is closely connected to fundamental notions of ‘justice’, ‘obligation’ or ‘duty’, ‘gratitude’ and ‘equality’. In _Reciprocity_, first published in 1986,_ _Lawrence Becker presents a sustained argument about reciprocity, beginning with the strategy for developing a moral theory of the virtues. He considers the concept of reciprocity in detail, contending that it is a basic (...) virtue that provides the basis for parental authority, obligations to future generations, and obedience to law. Throughout the first two parts of the book, Becker intersperses short pieces of his own narrative fiction to enrich reflection on the philosophical arguments. The final part is devoted to extensive bibliographical essays, ranging over anthropology, psychology, political theory and law, as well as the relevant ethics and political philosophy. (shrink)

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways (...) in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency. (shrink)

Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in (...) which such trajectories can be subject to evaluation in relation to their temporal dynamics, robustness, and representation. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as (...) biased. While researchers have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system’s outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in terms of the democratic ideal of ‘public reason’. I argue that situating demands for algorithmic accountability within this justificatory framework enables us (...) to better articulate their purpose and assess the adequacy of efforts toward them. (shrink)

Should decision-making algorithms be held to higher standards of transparency than human beings? The way we answer this question directly impacts what we demand from explainable algorithms, how we govern them via regulatory proposals, and how explainable algorithms may help resolve the social problems associated with decision making supported by artificial intelligence. Some argue that algorithms and humans should be held to the same standards of transparency and that a double standard of transparency is hardly justified. (...) We give two arguments to the contrary and specify two kinds of situations for which higher standards of transparency are required from algorithmic decisions as compared to humans. Our arguments have direct implications on the demands from explainable algorithms in decision-making contexts such as automated transportation. (shrink)

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decision-making processes. Using U.S. mortgage lending as an example use (...) case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker. (shrink)

For most of human history, human knowledge was considered to be something that was stored and captured by words. This began to change when Galileo said that the book of nature is written in the language of mathematics. Today, Dan Dennett and many others argue that all genuine scientific knowledge is in the form of mathematical algorithms. However, recently discovered neurocomputational algorithms can be used to justify the claim that there is genuine knowledge which is non-algorithmic. The fact (...) that these algorithms use prototype deployment, rather than mathematics or logic, gives us good reason to believe that there is a kind of knowledge that we derive from stories that is different from our knowledge of algorithms. Even though we would need algorithms to build a system that can make sense out of stories, we do not need to use algorithms when we ourselves embody a system that learns from stories. The success of the Galilean perspective in the physical sciences has often resulted in an attempt to mathematize the humanities. I am arguing that the dynamic neurocomputational perspective can give us a better understanding of how we get knowledge and wisdom from the stories told by disciplines such as Literature, History, Anthropology and Theology. This new neurological data can be used to justify the traditional pedagogy of these disciplines, which originally stressed the telling of stories rather than the learning of algorithms. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can (...) have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

Nudge is a popular public policy tool that harnesses well-known biases in human judgement to subtly guide people’s decisions, often to improve their choices or to achieve some socially desirable outcome. Thanks to recent developments in artificial intelligence (AI) methods new possibilities emerge of how and when our decisions can be nudged. On the one hand, algorithmically personalized nudges have the potential to vastly improve human daily lives. On the other hand, blindly outsourcing the development and implementation of nudges to (...) “black box” AI systems means that the ultimate reasons for why such nudges work, that is, the underlying human cognitive processes that they harness, will often be unknown. In this paper, we unpack this concern by considering a series of examples and case studies that demonstrate how AI systems can learn to harness biases in human judgment to reach a specified goal. Drawing on an analogy in a philosophical debate concerning the methodology of economics, we call for the need of an interdisciplinary oversight of AI systems that are tasked and deployed to nudge human behaviours. (shrink)

In recent years, biologists and philosophers of science have argued that evolutionary theory should incorporate more seriously the idea of ‘reciprocal causation.’ This notion refers to feedback loops whereby organisms change their experiences of the environment or alter the physical properties of their surroundings. In these loops, in particular niche constructing activities are central, since they may alter selection pressures acting on organisms, and thus affect their evolutionary trajectories. This paper discusses long-standing problems that emerge when studying such (...) class='Hi'>reciprocal causal processes between organisms and environments. By comparing past approaches to reciprocal causation from the early twentieth century with contemporary ones in niche construction theory, we identify two central reoccurring problems: All of these approaches have not been able to provide a conceptual framework that allows maintaining meaningful boundaries between organisms and environments, instead of merging the two, and integrating experiential and physical kinds of reciprocal causation. By building on case studies of niche construction research, we provide a model that is able to solve these two problems. It allows distinguishing between mutually interacting organisms and environments in complex scenarios, as well as integrating various forms of experiential and physical niche construction. (shrink)

With the increasing use of algorithms in high-stakes areas such as criminal justice and health has come a significant concern about the fairness of prediction-based decision procedures. In this article I argue that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome’s account of fairness as the proportional satisfaction of claims. On this interpretation these criteria do not disagree on what it means for an algorithm to be _fair_. Rather they (...) express different understandings of what grounds a claim to a good being allocated by an algorithmic decision procedure. I then argue that an important implication of the Broomean interpretation is that it strengthens the case for outcome-based criteria. Finally, I consider how a version of the levelling-down objection to performance parity criteria arises within the Broomean account. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that (...) do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

사회 경제적 혜택의 분배 규범으로 롤즈가 제시하는 차등의 원칙의 근거를 검토한다. 통상 불확실 상황에서 합리적 선택 전략들 중 하나로 거론되는 최소치 극대화(maximin) 기준이 그 근거라고 간주되지만, 롤즈는 상호성(reciprocity)을 차등의 원칙의 선택 근거로 제시한다. 논문은 롤즈적 상호성의 내용, 관련된 이상들, 상호성으로부터 차등의 원칙으로 나아가는 추론을 검토한다. 논의 결과는 상호성에 관련된 이상들인 평등한 출발점이자 비교의 수준점, 도덕적으로 자의적 요소들에 의한 분배의 규제, 효율성, 자유롭고 평등한 개인들의 합의가 이질적이고 고유한 근거와 방향을 가진 이상들이기에 그것들 각각의 내용과 상대적 비중을 어떻게 보는지에 따라 차등의 원칙과 (...) 다른 분배 규범이 도출될 가능성이 있다는 것이다. (shrink)

It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only demonstrate (...) how the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature: epistemic fragmentation. Epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources. This, in turn, can impede the identification and conceptualisation of emerging harms in these environments. We trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare, and learn from shared experiences. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. (...) Using these case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)

Algorithmic communications pose several challenges to democracy. The three phenomena of filtering, hypernudging, and microtargeting can have the effect of polarizing an electorate and thus undermine the deliberative potential of a democratic society. Algorithms can spread fake news throughout the society, undermining the epistemic potential that broad participation in democracy is meant to offer. They can pose a threat to political equality in that some people may have the means to make use of algorithmic communications and the sophistication to (...) be immune from attempts at manipulation, while other people are vulnerable to manipulation by those who use these means. My concern here is with the danger that algorithmic communications can pose to political equality, which arises because most citizens must make decisions about what and who to support in democratic politics with only a sparse budget of time, money, and energy. Algorithmic communications such as hypernudging and microtargeting can be a threat to democratic participation when persons are operating in environments that do not conduce to political sophistication. This constitutes a deepening of political inequality. The political sophistication necessary to counter this vulnerability is rooted for many in economic life and it can and ought to be enhanced by changing the terms of economic life. (shrink)

This paper provides strong evidence challenging the self-interest assumption that dominates the behavioral sciences and much evolutionary thinking. The evidence indicates that many people have a tendency to voluntarily cooperate, if treated fairly, and to punish noncooperators. We call this behavioral propensity “strong reciprocity” and show empirically that it can lead to almost universal cooperation in circumstances in which purely self-interested behavior would cause a complete breakdown of cooperation. In addition, we show that people are willing to punish those who (...) behaved unfairly towards a third person or who defected in a Prisoner’s Dilemma game with a third person. This suggests that strong reciprocity is a powerful device for the enforcement of social norms involving, for example, food sharing or collective action. Strong reciprocity cannot be rationalized as an adaptive trait by the leading evolutionary theories of human cooperation (in other words, kin selection, reciprocal altruism, indirect reciprocity, and costly signaling theory). However, multilevel selection theories of cultural evolution are consistent with strong reciprocity. (shrink)

Economists and biologists have proposed a distinction between two mechanisms – “strong” and “weak” reciprocity – that may explain the evolution of human sociality. Weak reciprocity theorists emphasize the benefits of long-term cooperation and the use of low-cost strategies to deter free-riders. Strong reciprocity theorists, in contrast, claim that cooperation in social dilemma games can be sustained by costly punishment mechanisms, even in one-shot and finitely repeated games. To support this claim, they have generated a large body of evidence concerning (...) the willingness of experimental subjects to punish uncooperative free-riders at a cost to themselves. In this article, I distinguish between a “narrow” and a “wide” reading of the experimental evidence. Under the narrow reading, punishment experiments are just useful devices to measure psychological propensities in controlled laboratory conditions. Under the wide reading, they replicate a mechanism that supports cooperation also in “real-world” situations outside the laboratory. I argue that the wide interpretation must be tested using a combination of laboratory data and evidence about cooperation “in the wild.” In spite of some often-repeated claims, there is no evidence that cooperation in the small egalitarian societies studied by anthropologists is enforced by means of costly punishment. Moreover, studies by economic and social historians show that social dilemmas in the wild are typically solved by institutions that coordinate punishment, reduce its cost, and extend the horizon of cooperation. The lack of field evidence for costly punishment suggests important constraints about what forms of cooperation can or cannot be sustained by means of decentralised policing. (shrink)