Ethical and Trustworthy AI Lab

The Ethical and Trustworthy AI Lab at Illinois Institute of Technology’s Center for the Study of Ethics in the Professions is an interdisciplinary group of researchers interested in the social and ethical implications of Artificial Intelligence (AI).

The group investigates philosophical, ethical, and social aspects of AI, including trustworthiness and the question of what makes AI uses ethical, just, and trustworthy; the roles of ethics codes, ethical guidelines, and policy-making in the regulation of AI technology; and AI applications in agriculture and medical contexts.

We seek to involve stakeholders from all fields, such as computer science, technology, engineering, philosophy, social sciences, practitioners, and students, in an interdisciplinary reflection on the ethical uses of AI.

We collaborate closely with the AI@IllinoisTech initiative, particularly its AI Ethics Working Group (AIEWG) and the international Z-Inspection® network. The Z-Inspection® assessment method for Trustworthy AI is based on the Ethics Guidelines for Trustworthy AI by the European Commission High-Level Expert Group on Artificial Intelligence. Z-Inspection® is listed in the new OECD Catalogue of AI Tools & Metrics.

Lab members

Elisabeth Hildt (head)
Ori Freiman, University of Toronto
Kelly Laas
Roberto V. Zicari, Z-Inspection® initiative


“Responsible use of AI” Pilot Project with the Province of Fryslân, Rijks ICT Gilde & the Z-Inspection®️ Initiative.

The pilot project took place from May 2022 through January 2023. During the pilot, the practical application of a deep learning algorithm from the province of Frŷslan was assessed. The AI maps heathland grassland by means of satellite images for monitoring nature reserves.

Hildt, Elisabeth. The Prospects of Artificial Consciousness: Ethical Dimensions and Concerns, AJOB Neuroscience, 14:2, 58-71, 2023. doi: 10.1080/21507740.2022.2148773

Mirghaderi, L., Sziron, M., Hildt, E.: “Investigating user perceptions of commercial virtual assistants: A qualitative study,” Frontiers in Psychology 13:944714, 2022. doi: 10.3389/fpsyg.2022.944714.

Vetter, D., Amann, J., Bruneault, F. et al. Lessons Learned from Assessing Trustworthy AI in Practice. DISO 2, 35 (2023).

Zicari, R.V., Amann, J., Bruneault, F., Coffee, M., Düdder, B., Hickman, E., Gallucci, A., Gilbert, T.K., Hagendorff, T., van Halem, I., Hildt, E., Kararigas, G., Kringen, P., Madai, V.I., Mathez, E.W., Tithi, J.J., Vetter, D., Westerlund, M., Wurth, R.: “How to Assess Trustworthy AI in Practice.” 2022.

Allahabadi, H., Amann, J., Balot, I., Beretta, A., Binkley, C., Bozenhard, J., Bruneault, F., Brusseau, J., Candemir, S., Cappellini, L.A., Chakraborty, S., Cherciu, N., Cociancig, C., Coffee, M., Ek, I., Espinosa-Leal, L., Farina, D., Fieux-Castagnet, G., Frauenfelder, T., Gallucci, A., Giuliani, G., Golda, A., van Halem, I., Hildt, E., Holm, S., Kararigas, G., Krier, S.A., Kühne, U., Lizzi, F., Madai, V.I., Markus, A.F., Masis, S., Mathez, E.W., Mureddu, F., Neri, E., Osika, W., Ozols, M., Panigutti, C., Parent, B., Pratesi, F., Moreno-Sánchez, P.A. Sartor, G., Savardi, M., Signoroni, A., Sormunen, H., Spezzatti, A., Srivastava, A., Stephansen, A.F., Theng, L.B., Tithi, J.J., Tuominen, J., Umbrello, S., Vaccher, F., Vetter,D., Westerlund, M., Wurth, R., Zicari, R.V.: “Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients,” IEEE Transactions on Technology and Society, DOI: 10.1109/TTS.2022.3195114, 2022.

Laas, K., Davis, M., Hildt, E. (eds): Codes of Ethics and Ethical Guidelines. Emerging Technologies, Changing Fields, Springer, 2022.

Zicari, R.V., Ahmed, S., Amann, J., Braun, S.A., Brodersen, J., Bruneault, F., Brusseau, J., Campano, E., Coffee, M., Dengel, A., Düdder, B., Gallucci, A., Gilbert, T.K., Gottfrois, P., Goffi, E., Haase, C.B., Hagendorff, T., Hickman, E., Hildt, E., Holm, S., Kringen, P., Kühne, U., Lucieri, A., Madai, V.I., Moreno Sánchez, P.A., Medlicott, O., Ozols, M., Schnebel, E., Spezzati, A., Tithi, J.J., Umbrello, S., Vetter, D., Volland, H., Westerlund, M., Wurth, R.: “Co-design of a Trustworthy AI System in Healthcare: Deep Learning based Skin Lesion Classifier,” Front. Hum. Dyn., 3:688152, 2021. doi: 10.3389/fhumd.2021.688152.

Hildt, E.: “What Sort of Robots Do We Want to Interact With? Reflecting on the Human Side of Human-Artificial Intelligence Interaction,” Frontiers in Computer Science 3:671012, 2021. doi: 10.3389/fcomp.2021.671012.

Schiff, Daniel, Justin Biddle, Jason Borenstein, Kelly Laas. (2021). “AI Ethics Documents in the Public, Private, and NGO Sectors: A Review of a Global Document Collection.” IEEE Transactions on Technology and Society. 2(1): 31-42. doi: 10.1109/TTS.2021.3052127

Zicari, R.V., Brusseau, J., Blomberg, S.N., Christensen, H.C., Coffee, M., Ganapini, M.B., Gerke, S., Gilbert, T.K., Hickman, E., Hildt, E., Holm, S., Kühne, U., Madai, V.I., Osika, W., Spezzati, A., Schnebel, E., Tithi, J.J., Vetter, D., Westerlund, M., Wurth, R., Amann, J., Antun, V., Beretta, V., Bruneault, F., Campano, E., Düdder, B., Gallucci, A., Goffi, E., Haase, C.B., Hagendorff, T., Kringen, P., Möslein, F., Ottenheimer, D., Ozols, M., Palazzani, L., Petrin, M., Tafur, K., Tørresen, J., Volland, H., Kararigas, G.: “On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls,” Front. Hum. Dyn 3:673104, 2021. doi: 10.3389/fhumd.2021.673104.

Hildt, E., Laas, K., Sziron, M.: “Editorial: Shaping Ethical Futures in Brain-based and Artificial Intelligence Research,” Science and Engineering Ethics 26(5): 2371-2379, 2020.

Hildt, E.: “Artificial Intelligence: Does Consciousness Matter?,” Frontiers in Psychology 10:1535, 2019, doi: 10.3389/fpsyg.2019.01535.