Michael Madaio

1.6k citations
32 papers · 790 indexed · 2 hit papers · h-index 14

Impact in

Papers in

Michael Madaio

31 papers receiving 764 citations

Hit Papers

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support 2022 · 88 citations
88202020262022202450100150200

Peers

Michael Madaio
Comparison fields: 5 of 86
  • Health Informatics 87
  • Safety Research 399
  • Computer Science Applications 110
  • Human-Computer Interaction 89
  • Artificial Intelligence 266
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Citations per field
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Citations per year

Countries citing papers authored by Michael Madaio

Since Specialization
Citations

This map shows the geographic impact of Michael Madaio's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Madaio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Madaio more than expected).

Fields of papers citing papers by Michael Madaio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Madaio. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Madaio. The network helps show where Michael Madaio may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Michael Madaio, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Madaio Line = papers co-authored together Michael Madaio links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20250
3 20241
4 202411
5 20246
6 20245
7 20241
8 202331
9 202343
10 202110
11
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
Hit paper breakdown →
2020231
12 202015
13 201921
14 201932
15 20184
16
A Climate of Support: A Process-Oriented Analysis of the Impact of Rapport on Peer Tutoring.
201812
17 201821
18
Using Temporal Association Rule Mining to Predict Dyadic Rapport in Peer Tutoring.
20179
19 201711
20 20165

About Michael Madaio

Michael Madaio is a scholar working on Safety Research, Computer Science Applications, Health Informatics, Human-Computer Interaction and Developmental and Educational Psychology, having authored 32 papers that have together received 790 indexed citations. Recurring topics across this work include Ethics and Social Impacts of AI (12 papers), ICT in Developing Communities (6 papers), Neuroethics, Human Enhancement, Biomedical Innovations (6 papers), Child Development and Digital Technology (5 papers), Online Learning and Analytics (4 papers), Innovative Teaching and Learning Methods (4 papers), Innovative Human-Technology Interaction (4 papers) and Educational and Psychological Assessments (3 papers). The work is most often cited by research in Health Informatics (87 citations), Safety Research (399 citations), Computer Science Applications (110 citations), Human-Computer Interaction (89 citations) and Artificial Intelligence (266 citations). Michael Madaio has collaborated with scholars based in United States, Ivory Coast and Canada. Frequent co-authors include Jennifer Wortman Vaughan, Hanna Wallach, Luke Stark, Amy Ogan, Richmond Y. Wong, Nick Merrill, Hariharan Subramonyam, Justine Cassell, Qian Yang and Kaja Kinga Jasińska. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, AERA Open, International Journal of Computer-Supported Collaborative Learning, Adaptive Agents and Multi-Agents Systems and ICLS.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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