The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Assessing Ethical Thinking about AI
Abstract
As is evidenced by the associated AI, Ethics and Society conference, we now take as given the need for ethics education in the AI and general CS curricula. The anticipated surge in AI ethics education will force the field to reckon with delineating and then evaluating learner outcomes to determine what is working and improve what is not. We argue for a more descriptive than normative focus of this ethics education, and propose the development of assessments that can measure descriptive ethical thinking about AI. Such an assessment tool for measuring ethical reasoning capacity in CS contexts must be designed to produce reliable scores for which there is established validity evidence concerning their interpretation and use.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Researchers have stressed that AI education should go beyond literacy and foster ethical reflection.
“researchers have stressed that AI education should go beyond literacy and foster ethical reflection”
From Assessment of AI ethical reflection: the development and validation of the AI ethical reflection scale (AIERS) for university students (2025) · cites this paper as Goldsmith et al., 2020
AI ethical reflection can be contextualized in three dimensions: AI ethical awareness, AI critical evaluation, and AI for social good.
“this study built on the two facets of ethical description and felt ethical responsibility of Goldsmith et al. (2020) and Rest’s (1986) model of morality to contextualize AI ethical reflection in three dimensions: AI ethical awareness, AI critical evaluation, and AI for social good”
From Assessment of AI ethical reflection: the development and validation of the AI ethical reflection scale (AIERS) for university students (2025) · cites this paper as Goldsmith et al., 2020
Goldsmith et al. (2020) argued that AI ethical reflection can consist of two primary facets: ethical description and felt ethical responsibility.
“Goldsmith et al. (2020) argued that AI ethical reflection can consist of two primary facets: ethical description and felt ethical responsibility.”
From Assessment of AI ethical reflection: the development and validation of the AI ethical reflection scale (AIERS) for university students (2025) · cites this paper as Goldsmith et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Why Teaching Ethics to AI Practitioners Is Important
Goldsmith, J., & Burton, E. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11139 · Cited by 60
Self-citation
Flagged first, then by citations
Research works printed but not linked: 13
- Bebeau, M. J. 2002. The defining issues test and the four component model: Contributions to professional education. Journal of moral education 31(3):271-295.
- Borenstein, J.; Drake, M.; Kirkman, R.; and Swann, J. 2008. The test of ethical sensitivity in science and engineering (TESSE): a discipline-specific assessment tool for aware- ness of ethical issues. In Annual ASEE Conference, Ameri- can Society for Engineering Education, Pittsburgh, PA.
- Borenstein, J.; Drake, M. J.; Kirkman, R.; and Swann, J. L. 2010. The engineering and science issues test (esit): A discipline-specific approach to assessing moral judgment. Science and Engineering Ethics 16(2):387-407.
- Burton, E.; Goldsmith, J.; and Mattei, N. 2018. How to teach computer ethics through science fiction. Communications of the ACM 61(8):54-64.
- Cronbach, L. J., et al. 1983. Ninety-five theses for reforming program evaluation. In Evaluation Models. Springer. 405- 412.
- Drake, M. J.; Griffin, P. M.; Kirkman, R.; and Swann, J. L. 2005. Engineering ethical curricula: Assessment and com- parison of two approaches. Journal of Engineering Educa- tion 94(2):223.
- Flanagan, O. J. 2009. Varieties of moral personality: Ethics and psychological realism. Harvard University Press.
- Beresford, A. 2006. Protagoras. Penguin Clas- sics.
- Quinn, M. J. 2006. On teaching computer ethics within a computer science department. Science and Engineering Ethics 12(2):335-343.
- Rest, J. R.; Narvaez, D.; Thoma, S. J.; and Bebeau, M. J. 1999a. DIT2: Devising and testing a revised instrument of moral judgment. Journal of Educational Psychology 91(4):644.
- Thoma, S. J., and Dong, Y. 2014. The defining issues test of moral judgment development. Behavioral Development Bulletin 19(3):55.
- Titus, C.; Zoltowski, C. B.; Huyck, M.; and Oakes, W. C. 2011. AC 2011-1833: The creation of tools for assessing ethical awareness in diverse multi-disciplinary programs. In Proceedings of the 2011 ASEE Annual Conference.
- Woodward, B. 2007. Growth and training impact in IT: A measure of ethical reasoning. Issues in Information Systems 8(2):220-224.
Cited by
Sorted by citations
Wang, Z., Chai, C.-S., Li, J., & Lee, V. W. Y. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00519-z · Cited by 78
Metadata
- Authors
- Goldsmith, J., Burton, E., Dueber, D. M., Goldstein, B., Sampson, S., & Toland, M. D.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 09
- Pages
- 13525-13528
- ISSN
- 2374-3468
- 2159-5399
- Cited by
- 11
- References
- 1
- Licence
- www.aaai.org/ (unspecified)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
