Research
An overview of my academic work to date.
Peer-Reviewed Full Papers
Legend: * Equal contribution; 🏆 Best paper; 🏅 Best paper honorable mention
[F-2024.4] Schoeffer, J., Jakubik, J., Voessing, M., Kuehl, N., Satzger, G. AI reliance and decision quality: Fundamentals, interdependence, and the effects of interventions. Journal of Artificial Intelligence Research (JAIR)
[F-2024.3] Lawless, C., Schoeffer, J., Le, L., Rowan, K., Sen, S., St Hill, C., Suh, J., Sarrafzadeh, B. “I want it that way”: Enabling interactive decision support using large language models and constraint programming. ACM Transactions on Interactive Intelligent Systems (TiiS)
[F-2024.2] Deck, L., Schoeffer, J., De-Arteaga, M., Kuehl, N. A critical survey on fairness benefits of explainable AI. ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘24)
Also presented at NeurIPS ‘23 XAIA Workshop and AISoLA ‘24
🏅 [F-2024.1] Schoeffer, J., De-Arteaga, M.,* Kuehl, N.* Explanations, fairness, and appropriate reliance in human-AI decision-making. ACM CHI Conference on Human Factors in Computing Systems (CHI ‘24)
Also presented at ACM EAAMO ‘23, ACM CHI ‘23 TRAIT Workshop, SCECR ‘23, and AISoLA ‘23
🏆 [F-2023.3] Schoeffer, J.,* Jakubik, J.,* Voessing, M., Kuehl, N., Satzger, G. On the interdependence of reliance behavior and accuracy in AI-assisted decision-making. Hybrid Human Artificial Intelligence 2023 (HHAI)
[F-2023.2] Schoeffer, J.,* Ritchie, A.,* Naggita, K.,* Monachou, F.,* Finocchiaro, J.,* Juarez, M. Online platforms and the fair exposure problem under homophily. 37th AAAI Conference on Artificial Intelligence (AAAI-23)
Also presented at ACM EAAMO ‘21
[F-2023.1] Baier, L., Schloer, T., Schoeffer, J., Kuehl, N. Detecting concept drift with neural network model uncertainty. 56th Hawaii International Conference on System Sciences 2023 (HICSS-56)
[F-2022.3] Schoeffer, J., Kuehl, N., Machowski, Y. “There is not enough information”: On the effects of transparency on perceptions of informational fairness and trustworthiness in automated decision making. ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘22)
[F-2022.2] Schoeffer, J., Machowski, Y., Kuehl, N. Perceptions of fairness and trustworthiness based on explanations in human vs. automated decision-making. 55th Hawaii International Conference on System Sciences 2022 (HICSS-55)
[F-2022.1] Hemmer, P., Kuehl, N., Schoeffer, J. Utilizing active machine learning for quality assurance: A case study of virtual car renderings in the automotive industry. 55th Hawaii International Conference on System Sciences 2022 (HICSS-55)
[F-2021.1] Schoeffer, J., Kuehl, N., Valera, I. A ranking approach to fair classification. ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ‘21)
[F-2020.1] Hemmer, P., Kuehl, N., Schoeffer, J. DEAL: Deep evidential active learning for image classification. 19th IEEE International Conference on Machine Learning and Applications (ICMLA ‘20)
Peer-Reviewed Short Papers
[S-2024.1] Deck, L., Schomaecker, A., Speith, T., Schoeffer, J., Kaestner, L., Kuehl, N. Mapping the potential of explainable AI for fairness along the AI lifecycle. European Workshop on Algorithmic Fairness (EWAF)
[S-2022.3] Jakubik, J., Schoeffer, J., Hoge, V., Voessing, M., Kuehl, N. An empirical evaluation of predicted outcomes as explanations in human-AI decision-making. ECML PKDD International Workshop on Explainable Knowledge Discovery in Data Mining (XKDD)
[S-2022.2] Schoeffer, J., De-Arteaga, M., Kuehl, N. On the relationship between explanations, fairness perceptions, and decisions. ACM CHI 2022 Workshop on Human-Centered Explainable AI (HCXAI)
[S-2022.1] Schoeffer, J. A human-centric perspective on fairness and transparency in algorithmic decision-making. Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ‘22)
[S-2021.2] Schoeffer, J., Kuehl, N. Appropriate fairness perceptions? On the effectiveness of explanations in enabling people to assess the fairness of automated decision systems. Companion Publication of the 24th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’21 Companion)
[S-2021.1] Schoeffer, J., Machowski, Y., Kuehl, N. A study on fairness and trust perceptions in automated decision making. ACM IUI ‘21 Workshop on Transparency and Explanations in Smart Systems (TExSS)
Talks, Presentations, and Doctoral Consortia
May ‘24. Paper @ ACM CHI ‘24 📍Honolulu, HI 🇺🇸
Mar ‘24. Seminar @ McCombs School of Business at UT Austin 📍Austin, TX 🇺🇸
Oct ‘23. Poster @ ACM EAAMO ‘23 📍Boston, MA 🇺🇸
Aug ‘23. Seminar @ Microsoft Research 📍Redmond, WA 🇺🇸
Apr ‘23. Paper @ Workshop on Trust and Reliance in AI-Assisted Tasks (TRAIT) at ACM CHI ‘23 📍Hamburg 🇩🇪
Apr ‘23. Invited @ MILA & Vector Institute 📍virtual
Dec ‘22. Panel @ Human-Machine Collaboration in a Changing World (HMC22) Workshop 📍Paris 🇫🇷
Nov ‘22. Invited @ Vienna University of Economics and Business (WU Wien) 📍Vienna 🇦🇹
Oct ‘22. Poster @ Karlsruhe Service Summit ‘22 📍Karlsruhe 🇩🇪
Jun ‘22. Paper @ ACM FAccT ‘22 📍Seoul 🇰🇷
May ‘22. Paper @ Workshop on Human-Centered Explainable AI (HCXAI) at ACM CHI ‘22 📍New Orleans, LA 🇺🇸
May ‘22. Doctoral Consortium @ ACM CHI ‘22 📍New Orleans, LA 🇺🇸
Apr ‘22. Seminar @ McCombs School of Business at UT Austin 📍Austin, TX 🇺🇸
Jan ‘22. Paper @ HICSS-55 📍virtual
Nov ‘21. Invited @ KIT Speaker Series 📍Karlsruhe 🇩🇪
Oct ‘21. Poster @ ACM CSCW ‘21 📍virtual
Sep ‘21. Invited @ Medienakademie Köln 📍Cologne 🇩🇪
Jun ‘21. Paper @ ACM COMPASS ‘21 📍virtual
Apr ‘21. Paper @ Transparency and Explanations in Smart Systems (TExSS) Workshop at ACM IUI ‘21 📍virtual
Mar ‘21. Doctoral Consortium @ ACM FAccT ‘21 📍virtual
Feb ‘21. Invited @ Mittelstand 4.0-Kompetenzzentrum Saarbrücken 📍virtual