Algorithmic Political Bias in Artificial Intelligence Systems
Philosophy and Technology, ISSN: 2210-5441, Vol: 35, Issue: 2, Page: 25
2022
- 37Citations
- 85Captures
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations37
- Citation Indexes37
- 37
- CrossRef5
- Captures85
- Readers85
- 85
- Mentions1
- News Mentions1
- News1
Most Recent News
Representation of intensivists’ race/ethnicity, sex, and age by artificial intelligence: a cross-sectional study of two text-to-image models
Mia Gisselbaek 1, Mélanie Suppan 1, Laurens Minsart 2, Ekin Köselerli 3, Sheila Nainan Myatra 4, Idit Matot 5, Odmara L. Barreto Chang 6, Sarah
Article Description
Some artificial intelligence (AI) systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in some of the same ways in which algorithmic gender and racial biases emerge. However, it differs importantly from them because there are (in a democratic society) strong social norms against gender and racial biases. This does not hold to the same extent for political biases. Political biases can thus more powerfully influence people, which increases the chances that these biases become embedded in algorithms and makes algorithmic political biases harder to detect and eradicate than gender and racial biases even though they all can produce similar harm. Since some algorithms can now also easily identify people’s political orientations against their will, these problems are exacerbated. Algorithmic political bias thus raises substantial and distinctive risks that the AI community should be aware of and examine.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85127516896&origin=inward; http://dx.doi.org/10.1007/s13347-022-00512-8; http://www.ncbi.nlm.nih.gov/pubmed/35378902; https://link.springer.com/10.1007/s13347-022-00512-8; https://dx.doi.org/10.1007/s13347-022-00512-8; https://link.springer.com/article/10.1007/s13347-022-00512-8
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know