S ́āntideva and the Transformation of Mind: The Interdependence of Ethics and Wisdom, with Reference to the Perfection of Generosity (P. Jeyapalasingham)

 

Problems in Population Ethics (J. Du Plessis)

 

L'Ennui au XVIIIe siècle en France. Les aléas d'un affect philosophique: de la crise existentielle aux crises politiques (A. Tétreault)

 

Annual Graduate Conference in Ethics and Public Affairs (uCarleton)

 

Resisting Commonsense and Taking Abortion Rights Seriously (P. Singh)

 

The Ethics of Persuasion: An Ethical Evaluation of Allocation and Acquisition Issues in Organ Transplantation (S. E. Oriaku)

 

Building Back Better: Opportunities and Risks - Call for papers

 

On Culture and Value - Conference

 

Exclusion and Legitimacy (A. Kazakov)

 

Phenomenology of the Cultural Other (H. Andishan)

 

On Forgetting with Forgiving (M. Dargaj)

 

Evaluating the use of pre-implentation genetic diagnosis through an institutional design approach (G. Calderini)

 

Contrat doctoral IHRIM et Oxford 2021–24: IA et éthique médicale

 Contrat doctoral 2021–24 : Questions éthiques de l’usage de l’intelligence artificielle en santé : apport d’un regard épistémologique

Doctoral Fellowship 2021–24: Ethical Questions Concerning the Use of Artificial Intelligence in Medicine and Health Management: An Epistemological Approach

 IHRIM ENS de Lyon / Maison Française d’Oxford

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Version FRANCAISE (for the ENGLISH version, see below)

Contrat proposé doctoral dans le cadre du projet CNRS 80 Prime 2021: “Ethical design for artificial intelligence models in patient management and treatment decisions” (ED-AIM)

Disciplines concernées: éthique médicale / éthique appliquée, philosophie des sciences, sciences informatiques.

Période : contrat de 3 ans (36 mois), septembre 2021–août 2024. Inscription à l’ED 487 (philosophie) à l’Université de Lyon, avec rattachement au laboratoire IHRIM (UMR 5317, CNRS, ENS de Lyon). Obligation d’effectuer des missions prolongées à la Maison Française d’Oxford (UMIFRE 11 et USR 3129 CNRS, Oxford) d’une durée totale de minimum 12 mois sur la période.

Profil : Master 2 ou équivalent en éthique appliquée/éthique médicale/philosophie des sciences ; bonnes connaissances en informatique. La thèse peut être rédigée en français ou en anglais. La capacité de communiquer et collaborer dans les deux langues est nécessaire. Les étudiants non français sont également invités à postuler.

Encadrement : (co-dir.) Mogens LAERKE (MFO UMIFRE 11 Oxford / IHRIM ENS de Lyon) et Thomas GUYET (Inria-IRISA).

Contexte : ED-AIM est une collaboration interdisciplinaire CNRS entre la Maison Française d’Oxford (USR 3129 CNRS), l’Institut de recherche en informatique et systèmes aléatoires (UMR 6074 IRISA, Rennes 1), l’Institut d’histoire des représentations et des idées dans les modernités (IHRIM, UMR 5317, ENS de Lyon) et l’Institute of Biomedical Engineering de l’Université d’Oxford.

Dossier : Lettre de motivation (1 page); CV (2 pages max.); Projet de these (3 pages max). L’ensemble du dossier est à soumettre comme un pdf unique aux deux adresses mogens.laerke@cnrs.fr et thomas.guyet@irisa.fr. Date limite : 1^er Juillet 2021. Les candidat(e)s sélectionné(e)s seront invité(e)s à un entretien (en ligne) pendant la première moitié de juillet.

Sujet : Ces dernières années, l'apprentissage automatique, un sous-domaine de l'intelligence artificielle (IA), a suscité un intérêt considérable dans les milieux de la santé en raison de son potentiel pour aider à améliorer l'efficacité et la sécurité à tous les niveaux de nos systèmes de santé, du diagnostic à l'organisation des soins. Nos sociétés tentent d'anticiper les changements que l’IA pourrait provoquer (Conseil européen, 2020) et de mettre en place les mesures qui nous assurent qu'elles suivent des principes, des valeurs morales, des codes professionnels et des normes sociales. Idéalement, ces concepts éthiques doivent être intégrés dès la conception des outils (ethical design). À cet égard, les modèles d'apprentissage automatique présentent des faiblesses bien connues qui font actuellement l'objet d'études dans le domaine de l'informatique. Premièrement, les modèles d'apprentissage automatique peuvent être soumis à différents types de biais. Deuxièmement, les modèles d'intelligence artificielle sont pour la plupart des modèles en boîte noire (Wang et al. 2020). Les utilisateurs de ces systèmes perdent leur capacité à contester les décisions automatiques. L'utilisation des boîtes noires soulève d'importants problèmes éthiques. Par exemple, dans quelle mesure les modèles fondés sur l’IA incarnent-ils des valeurs qui entraînent des conséquences imprévues et des résultats injustes ou discriminatoires (Keskinbora 2019 ; Chen et al. 2019) ? Devons-nous accepter les décisions prises sur la base de modèles de type boîte noire dont les résultats ne peuvent être clairement interprétés et expliqués ? Dans quelle mesure le patient et les professionnels de la santé peuvent-ils et doivent-ils faire confiance à ces modèles ? Comment l’utilisation de l’IA affecte-t-elle la confiance de patients des traitements qui leur sont proposés et des professionnels de la santé qui les proposent ?  En abordant ces questions spécifiquement par rapport à l’IA, le doctorant devrait apporter un regard à la fois éthique et épistémologique sur le développement de la science et des techniques dans le domaine de la santé.

L’encadrement multi-disciplinaire de la thèse propose un environnement avec une expertise dans les domaines de l’IA et des applications concrètes à des données de santé. Les projets peuvent avoir des aspects applicatifs pour questionner des outils fondés sur de l’IA (outils existants ou à adapter selon une question de recherche), ou encore des expérimentations impliquant des cas d’usages réels en santé.

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ENGLISH VERSION (pour la version française, voir plus haut)

Doctoral Fellowship offered within the framework of the project CNRS 80 Prime 2021: “Ethical Design of Artificial Intelligence Models in Patient Management and Treatment Plans” (ED-AIM)

Disciplinary background: medical ethics / applied ethics / philosophy of science / computer science.

Period: 3-year contract (36 months), September 2021 – August 2024. Admission at the Doctoral School pf Philosophy at the University of Lyon (ED 487) with an affiliation at the CNRS institute IHRIM (UMR 5316, ENS de Lyon). Obligation to undertake prolonged research stays at the Maison Française d’Oxford (UMIFRE 11 and USR 3129 CNRS, Oxford) of total duration of minimum 12 months.

Profile: Master 2, MPhil or equivalent in applied ethics/medical ethics/philosophy of science. Some competence in computer science is required. The thesis can be written in English or in French, but a capacity to communicate and collaborate in both languages is necessary. Non-French students are welcome to apply.

Supervision: co-direction by Mogens LÆRKE (MFO UMIFRE 11 Oxford / IHRIM ENS de Lyon) and Thomas GUYET (Inria-IRISA).

Context: ED-AIM is an inter-disciplinary collaboration of the CNRS between the Maison Française d’Oxford (USR 3129 CNRS), the Institut de recherche en informatique et systèmes aléatoires (UMR 6074 IRISA, Rennes 1), the Institut d’histoire des représentations et des idées dans les modernités (IHRIM, UMR 5317, ENS de Lyon), and the Institute of Biomedical Engineering at Oxford University.

Application: Covering Letter explaining the interests and motivations of the applicant (1 page); CV (2 pages max); Thesis Project (3 pages max). The complete dossier should be sent AS A SINGLE PDF FILE to both of the following two addresses:  mogens.laerke@cnrs.fr et thomas.guyet@irisa.fr. Deadline: 1 July 2021. Shortlisted candidates will be invited to an online interview during the first half of July.

Topic: In recent years, machine learning, a subfield of artificial intelligence (herea.er AI), has generated considerable interest in health care circles due to its potential to help improving efficiency and safety at all levels of our health care systems, from diagnostics to the organisation of care. Our societies attempt to anticipate the changes that IA may bring (Conseil Européen, 2020) and put safeguards into place to ensure that it abides by our moral principles, principles of professional conduct, and social norms. Ideally, ethical notions should be integrated into these tools at their very inception (ethical design). Machine learning models do, however, have well-known weaknesses that are currently being investigated in the field of computer science. First, machine learning model can be subject to different kinds of bias. Second, artificial intelligence models are mostly black-box models (Wang et al. 2020). Users of such systems lose their ability to contest automatic decisions. The use of black-box models raises important ethical problems.  For example, to what extent do such models embody values inasmuch as they entail actions and choices and can inadvertently contain design or implementation errors that result in unforeseen consequences and unfair outcomes (Keskinbora 2019; Chen et al. 2019)? Should we embrace decisions made on the basis of black-box models whose outputs cannot be clearly interpreted and explained? Or, to what extent can and should patient and practitioners trust these models? How does the use of IA affect patients’ trust in the treatments offered and in the health workers offering them? By approaching these question specifically in relation to IA, the doctoral candidate should bring both an ethical and an epistemological perspective on these issues within the development of science and technology in health management.

The multi-disciplinary context and supervision of the thesis offers support in the domains of IA and its concrete application in relation to health management. The project can imply the application and use IA tools (either already existing tools, or tools that can be appropriately adapted to the research question) or experiments or survey that concern the actual use of such tools in health management.  

Références / References

Geis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E., Jaremko, J. L., ... & Kohli, M. (2019). Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Canadian Association of Radiologists Journal 70(4): 329-334.         

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9): 389-399.

European Council, European framework on ethical aspects of artificial intelligence, robotics and related technologies, 2020.

Wang, F., Kaushal, R., & Khullar, D. (2020). Should health care demand interpretable artificial intelligence or accept black box medicine? Annals of Internal Medicine,172(1) : 59-61.

Keskinbora, K. H. (2019), Medical ethics considerations on artificial intelligence, Journal of Clinical Neuroscience 64 : 277-282.

Chen, I. Y., P. Szolovits, and M. Ghassemi (2019), Can AI Help Reduce Disparities in General Medical and Mental Health Care? AMA J Ethics 21(2) : 167-179.

Santideva and the Transformation of Mind: The Interdependance of Ethics and Wisdom (P. Jeyapalasingham)

 

Appel de textes | Call for papers : L'Éthique et la vie bonne | On Ethics and Good Life

 

Les Limites de la rationalité humaine : la confiance face à la différence (D. Marinier)

PhD studentship: The ethics and epistemology of explanatory AI (Technische Universiteit Delft)

PhD studentship: The ethics and epistemology of explanatory AI

JOB DESCRIPTION

Artificial Intelligence is pervading every aspect of our life: it is present in decisions made in healthcare, transportation, finances, education, and governmental level, just to mention a few players. With it, explanation for high-stakes decision making (XAI) is at the center of current debates, receiving much (and deserved) attention from industry and academy alike. AI is essentially a sociotechnical system, where a decision maker interacts with various sources of information and decision-support tools, a process whose quality should be assessed in terms of the final, aggregated outcome —the quality of the decision— rather than assessing only the quality of the decision-support tool in isolation (e.g., in terms of its predictive accuracy and standalone precision). It is therefore important to develop tools that explain their predictions in meaningful terms, a property rarely matched by AI systems available in the market today. The explanation problem for a
decision-support system can be understood as a trade-off between what algorithms can safely ignore and what meaningful information should absolutely be included to make an informed decision. Thus, the problem of XAI is intertwined with epistemic and normative problems, such as trustworthiness (what sort of knowledge we have and what can safely be ignored), comprehensibility (human meaningfulness of the explanations), and accountability (humans keeping the ultimate responsibility for the decision). In this context, several epistemological, ethical, and legal questions emerge, such as: what is the logic for a successful XAI? how can we ensure reliability and trust? which ethical concerns emerge in the context of (un)successful explanations? Is current XAI complying with regulations? The successful candidate will work in the intersections of epistemological and ethical issues, with a strong emphasis on the normative aspects of explanatory AI. Different ethical frameworks and principles will be studied to understand the implications of XAI and the role of different stakeholders in high-stake decision makings (developers, users, individuals affected by system’s actions, legal experts, etc.). Case studies will also be focus of this project, as they will engage relevant stakeholders to capture adherence to social, legal, ethical norms, and, ultimately, human responsibility.

This PhD project will be developed within the EU Horizon2020 SoBigData++. SoBigData++ strives to deliver a distributed, Pan-European, multi-disciplinary research infrastructure for big social data analytics, coupled with the consolidation of a cross-disciplinary European research community, aimed at using social mining and big data to understand the complexity of our contemporary, globally-interconnected society. SoBigData++ is set to advance on such ambitious tasks thanks to SoBigData, the predecessor project that started this construction in 2015. Becoming an advanced community, SoBigData++ will strengthen its tools and services to empower researchers and innovators through a platform for the design and execution of large-scale social mining experiments. It will be open to users with diverse background, accessible on project cloud (aligned with EOSC) and also exploiting supercomputing facilities. Pushing the FAIR principles further, SoBigData++ will render social mining experiments more easily designed, adjusted and repeatable by domain experts that are not data scientists. SoBigData++ will move forward from a starting community of pioneers to a wide and diverse scientific movement, capable of empowering the next generation of responsible social data scientists, engaged in the grand societal challenges laid out in its exploratories: Societal Debates and Online Misinformation, Sustainable Cities for Citizens, Demography, Economics & Finance 2.0, Migration Studies, Sport Data Science, Social Impact of Artificial Intelligence and Explainable Machine Learning. SoBigData++ will advance from the awareness of ethical and legal challenges to concrete tools that operationalise ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency and pluralism. SoBigData++ will deliver an accelerator of data-driven innovation that facilitates the collaboration with industry to develop joint pilot projects, and will consolidate an RI ready for the ESFRI Roadmap and sustained by a SoBigData Association.

REQUIREMENTS

• Master’s degree or equivalent in philosophy or similar discipline.
• Candidates with interests in analytical philosophy (e.g. ethics of
algorithms, ethics of AI, ethics of technology, philosophy of action)
and strong affinity (or degree in) philosophy of science, epistemology,
philosophy of technology, philosophy of engineering and computer science
are strongly encouraged to apply.
• Strong interests in interdisciplnary research, principally with
computer scientists.
• Excellent command of written and spoken English.
• Excellent communication skills and interested in translating research
ideas and findings for the benefit of non-academic stakeholders (e.g.
managers and policymakers).
• The ability to work independently and as a team player.

CONDITIONS OF EMPLOYMENT

Fixed-term contract: 4 years.

TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2325 per month in the first year to € 2972 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic
staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills. The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

EMPLOYER

Technische Universiteit Delft
Delft University of Technology (TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At TU Delft you will work in an environment where technical sciences and society converge. TU Delft comprises eight faculties, unique laboratories, research institutes and schools.

DEPARTMENT

Faculty Technology, Policy and Management
The Faculty of Technology, Policy and Management (TPM) makes an
important contribution to solving the complex technical and social
challenges that we face as a society. Challenges such as energy,
climate, mobility, IT, water and cyber security. This requires a
multidisciplinary approach that goes beyond technology. Our education
and research are therefore at the intersection of technology, society
and management. We combine insights from the engineering sciences with
those from the humanities and social sciences. We develop robust models
and designs, are internationally oriented, and have extensive networks
in science and practice.

ADDITIONAL INFORMATION

For information about this vacancy, you can contact Juan M. Durán,
email: j.m.duran@tudelft.nl.

For information about the selection procedure, please contact Mrs. Anita
van VIanen, HR Advisor, email: vacature-tbm@tudelft.nl.

When you are interested in this position, please include in your
application: (a) CV, (b) motivation letter including names of two
references, (c) list of publications in a single pdf entitled
"TPM20.038_YourLastname.pdf". Send your application to
vacature-tbm@tudelft.nl. Applications will be considered until
June 15, or until the position is filled. Due to the Covid 19
measurement a remotely start is possible.

The Effect of Integrity on Conscientious Objections in Healthcare (E.S.Goodrich)

Dédoublement du plan argumentatif chez L.M.Deschamps : réponse à la crise de l'ennui au 18e siècle en France (A. Tétreault)

The Path to a New Awakening: B. R. Ambedkar's Transformation of Buddhist Philosophy (M. Alamo Santos)

Attention : 

Événement reporté (date à déterminer) | Event postponed (date to be determined)