James Brusseau

Today's AI recommendation algorithms produce a human dilemma between euphoria and freedom. To elaborate, four ways that recommenders reshape experience are delineated. First, the human experience of convenience is tuned to euphoric perfection. Second, a kind of personal authenticity becomes capturable with algorithms and data. Third, a conception of human freedom emerges, one that promotes unfamiliar interests for users instead of satisfying those that already exist. Finally, a new human dilemma is posed between two types of personal identity. On one side, there are recommendation algorithms that locate a user's core preferences, and then reinforce that identity with options designed to resemble those that have already proved satisfying. The result is an algorithmic production of euphoria and authenticity. On the other side, there are recommenders that provoke unfamiliar interests and curiosities. These proposals deny the existence of an authentic self and instead promote new preferences and experiences. The result is a human freedom of new personal identity.

Artificial creativity is presented as a counter to Benjamin’s conception of an “aura” in art. Where Benjamin sees authenticity as art’s critical element, generative artificial intelligence operates as pure inauthenticity. Two elements of purely inauthentic art are described: elusiveness and reflection. Elusiveness is the inability to find an origin-story for the created artwork, and reflection is the ability for perceivers to impose any origin that serves their own purposes. The paper subsequently argues that these elements widen the scope of artistic and creative potential. To illustrate, an example is developed around musical improvisation with an artificial intelligence partner. Finally, a question is raised about whether the inauthentic creativity of AI in art can be extended to human experience and our sense of our identities.

Today's AI recommendation algorithms produce a human dilemma between euphoria and freedom. To elaborate, four ways that recommenders reshape experience are delineated. First, the human experience of convenience is tuned to euphoric perfection. Second, a kind of personal authenticity becomes capturable with algorithms and data. Third, a conception of human freedom emerges, one that promotes unfamiliar interests for users instead of satisfying those that already exist. Finally, a new human dilemma is posed between two types of personal identity. On one side, there are recommendation algorithms that locate a user's core preferences, and then reinforce that identity with options designed to resemble those that have already proved satisfying. The result is an algorithmic production of euphoria and authenticity. On the other side, there are recommenders that provoke unfamiliar interests and curiosities. These proposals deny the existence of an authentic self and instead promote new preferences and experiences. The result is a human freedom of new personal identity.

Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, ethics is embedded. Subsequently, the paper illustrates the acceleration framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world ambiguities, the Telus experience indicates that acceleration AI ethics is a way of maximizing social responsibility through innovation, as opposed to sacrificing social responsibility for innovation, or sacrificing innovation for social responsibility.

Three directions for the AI avant-garde are sketched against the background of time. Posthumanism changes what we are, and belongs to the radical future. Transhumanism changes how we are, and corresponds with the radical past. Genhumanism changes who we are, and exists in the radical present. While developing the concepts, this essay intersects in two ways with theoretical debates about humanism in the face of technological advance. First, it describes how temporal divisions may cleanly differentiate post- and transhumanism. Second, the essay introduces generative humanism, which contributes to discussions about AI and society by delineating a novel humanistic response to contemporary technology. Finally, grounds are provided for a practical project, one where philosophers work with AI engineers in the area of genhumanism. Contemporary AI research into serendipity in recommendation engines provides natural support for the shared research.

The AI ethics of statistical fairness is an error, the approach should be abandoned, and the accumulated academic work deleted. The argument proceeds by identifying four recurring mistakes within statistical fairness. One conflates fairness with equality, which confines thinking to similars being treated similarly. The second and third errors derive from a perspectival ethical view which functions by negating others and their viewpoints. The final mistake constrains fairness to work within predefined social groups instead of allowing unconstrained fairness to subsequently define group composition. From the nature of these misconceptions, the larger argument follows. Because the errors are integral to how statistical fairness works, attempting to resolve the difficulties only deepens them. Consequently, the errors cannot be corrected without undermining the larger project, and statistical fairness collapses from within. While the collapse ends a failure in ethics, it also provokes distinct possibilities for fairness, data, and algorithms. Quickly indicating some of these directions is a secondary aim of the paper, and one that aligns with what fairness has consistently meant and done since Aristotle.

One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social effects. AI problems are solved by more AI, not less. Permissions and restrictions governing AI emerge from a decentralized process, instead of a unified authority. The work of ethics is embedded in AI development and application, instead of functioning from outside. Together, these attitudes and practices remake ethics as provoking rather than restraining artificial intelligence.

Recent AI ethics has focused on applying abstract principles downward to practice. This paper moves in the other direction. Ethical insights are generated from the lived experiences of AI-designers working on tangible human problems, and then cycled upward to influence theoretical debates surrounding these questions: 1) Should AI as trustworthy be sought through explainability, or accurate performance? 2) Should AI be considered trustworthy at all, or is reliability a preferable aim? 3) Should AI ethics be oriented toward establishing protections for users, or toward catalyzing innovation? Specific answers are less significant than the larger demonstration that AI ethics is currently unbalanced toward theoretical principles, and will benefit from increased exposure to grounded practices and dilemmas.

Principles of fairness and solidarity in AI ethics regularly overlap, creating obscurity in practice: acting in accordance with one can appear indistinguishable from deciding according to the rules of the other. However, there exist irregular cases where the two concepts split, and so reveal their disparate meanings and uses. This paper explores two cases in AI medical ethics – one that is irregular and the other more conventional – to fully distinguish fairness and solidarity. Then the distinction is applied to the frequently cited COMPAS versus ProPublica dispute in judicial ethics. The application provides a broader model for settling contemporary and topical debates about fairness and solidarity. It also implies a deeper and disorienting truth about AI ethics principles and their justification.

In 1990, Gilles Deleuze publishedPostscript on the Societies of Control, an introduction to the potentially suffocating reality of the nascent control society. This thirty-year update details how Deleuze’s conception has developed from a broad speculative vision into specific economic mechanisms clustering around personal information, big data, predictive analytics, and marketing. The central claim is that today’s advancing control society coerces without prohibitions, and through incentives that are not grim but enjoyable, even euphoric because they compel individuals to obey their own personal information. The article concludes by delineating two strategies for living that are as unexplored as control society itself because they are revealed and then enabled by the particular method of oppression that is control.

Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology’s human-centering. When summed, the scores convert into objective investment guidance. The strategy of incorporating ethics into financial decisions will be recognizable to participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks are inadequate for AI-intensive companies. To fully account for contemporary technology, the following categories of evaluation will be developed and featured as vital investing criteria: autonomy, dignity, privacy, performance. With these priorities established, the larger goal is a model for humanitarian investing in AI-intensive companies that is intellectually robust, manageable for analysts, useful for portfolio managers, and credible for investors.

Big data and predictive analytics applied to economic life is forcing individuals to choose between authenticity and freedom. The fact of the choice cuts philosophy away from the traditional understanding of the two values as entwined. This essay describes why the split is happening, how new conceptions of authenticity and freedom are rising, and the human experience of the dilemma between them. Also, this essay participates in recent philosophical intersections with Shoshana Zuboff’s work on surveillance capitalism, but the investigation connects on the individual, ethical level as opposed to the more prevalent social and political interaction.

This dissertation has two aims: to elaborate Gilles Deleuze's ontology and to set Deleuze's philosophy in correspondence with literature. Each chapter elaborates Deleuze's ontology from a specific perspective. In the first chapter, I filter Deleuze's thought through the question of personal identity. Subsequent chapters exercise Deleuze's ontology through questions of Nietzsche's 'eternal return' and of spatiality and alienation. Besides developing Deleuze's thought in each chapter's particular form, I apply the resulting concepts to a variety of literary texts including Steve Erickson's Days Between Stations, Shakespeare's Titus Andronicus and Isabelle Eberhardt's writings.

Today’s ethics of privacy is largely dedicated to defending personal information from big data technologies. This essay goes in the other direction; it considers the struggle to be lost, and explores two strategies for living after privacy is gone. First, total exposure embraces privacy’s decline, and then contributes to the process with transparency. All personal information is shared without reservation. The resulting ethics is explored through a big data version of Robert Nozick’s Experience Machine thought experiment. Second, transient existence responds to privacy’s loss by ceaselessly generating new personal identities, which translates into constantly producing temporarily unviolated private information. The ethics is explored through Gilles Deleuze’s metaphysics of difference applied in linguistic terms to the formation of the self. Comparing the exposure and transience alternatives leads to the conclusion that today’s big data reality splits the traditional ethical link between authenticity and freedom. Exposure provides authenticity, but negates human freedom. Transience provides freedom, but disdains authenticity.

The difference between Derrida and Deleuze has been debated in terms of their understandings and uses of the historical distinction between Being and beings. Daniel W. Smith intersects with the question when discussing transcendence and immanence. Clair Colebrook intersects when discussing materialism. Paul Patton intersects when distinguishing the unconditioned and conditioned. This essay moves along with their ideas, and contributes to the discussion by re-inscribing the debate in terms of nouns and verbs. The conclusion suggests that the noun/verb prism yields a view of the question about Being and beings that fits most easily into Smith’s conception of the relation between Derrida and Deleuze. Thematically, the essay is framed by a line from Derrida’s eulogy for Deleuze, and by a question. The line is Derrida recollecting Deleuze commenting that, "It’s painful for me to see you spending so much time on the College International de Philosophie. I would rather you wrote..." The question addressed to the prolific Derrida and Deleuze is: “How much writing is enough?” Why do individuals with limited time who have already written numerous and thick volumes of philosophy choose to go ahead and write more?

According to Facebook’s Mark Zuckerberg, big data reality means, “The days of having a different image for your co-workers and for others are coming to an end, which is good because having multiple identities represents a lack of integrity.” Two sets of questions follow. One centers on technology and asks how big data mechanisms collapse our various selves (work-self, family-self, romantic-self) into one personality. The second question set shifts from technology to ethics by asking whether we want the kind of integrity that Zuckerberg lauds, and that big data technology enables. The negative response is explored by sketching three ethical conceptions of selfhood that recommend personal identity be understood as dis-integrating. The success of the strategies partially depends upon an undermining use of big data platforms.

Decadence in philosophy means evaluating truth claims exclusively in terms of provocation, in terms of how vigorously they generate subsequent thought. The best truth/book/essay/video doesn’t settle questions, but produces still more thought, writing, production. Decadence privileges the history of thinking over the history of truth. Thought’s history runs from base servility (the best thinking eliminates the need for itself by culminating in universal truth, Platonism), to dialectical servility (the ceaseless interplay of interpretation as a verb, and as a noun, Nietzscheanism), to decadence, where thought overthrows truth’s independent value and incorporates assertions into its own expression and acceleration. Decadence is defined as truth serving thought, and practiced when the only reason we have truths is to generate more thinking. While the definition structures well historically, the claim in Decadence of the French Nietzsche is not that there are serial epochs – Platonism followed by Nietzscheanism followed by Decadence – but that an esoteric vein of decadence runs through philosophy’s history.

Compartmentalizing our distinct personal identities is increasingly difficult in big data reality. Pictures of the person we were on past vacations resurface in employers’ Google searches; LinkedIn which exhibits our income level is increasingly used as a dating web site. Whether on vacation, at work, or seeking romance, our digital selves stream together. One result is that a perennial ethical question about personal identity has spilled out of philosophy departments and into the real world. Ought we possess one, unified identity that coherently integrates the various aspects of our lives, or, incarnate deeply distinct selves suited to different occasions and contexts? At bottom, are we one, or many? The question is not only palpable today, but also urgent because if a decision is not made by us, the forces of big data and surveillance capitalism will make it for us by compelling unity. Speaking in favor of the big data tendency, Facebook’s Mark Zuckerberg promotes the ethics of an integrated identity, a single version of selfhood maintained across diverse contexts and human relationships. This essay goes in the other direction by sketching two ethical frameworks arranged to defend our compartmentalized identities, which amounts to promoting the dis-integration of our selves. One framework connects with natural law, the other with language, and both aim to create a sense of selfhood that breaks away from its own past, and from the unifying powers of big data technology.