1. Ahumada, J. A., Fegraus, E., Birch, T., Fores, N., Kays, R., O'Brien, T. G., et al. (2020). Wildlife insights: A platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environmental Conservation, 47(1), 1-6. [
DOI:10.1017/S0376892919000298]
2. Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., et al. (2019). Quantum su-premacy using a programmable superconducting processor. Nature, 574(7779), 505-510. [
DOI:10.1038/s41586-019-1666-5] [
PMID]
3. Bird, T. J., Bates, A. E., Lefcheck, J. S., Hill, N. A., Thomson, R. J., Edgar, G. J., et al. (2014). Statis-tical solutions for error and bias in global citizen science datasets. Biological Conservation, 173, 144-154. [
DOI:10.1016/j.biocon.2013.07.037]
4. Blackwell, A. (2015). Interacting with an inferred world: The challenge of machine learning for hu-mane computer interaction. Aarhus Series on Human Centered Computing, 1(1), 12. [
DOI:10.7146/aahcc.v1i1.21197]
5. Bonney, R., Ballard, H., Jordan, R., McCallie, E., Phillips, T., Shirk, J., & Wilderman, C. C. (2009). Public participation in scientific research: Defining the field and assessing its potential for informal science education. A CAISE inquiry group report. Washington, DC: Center for Advancement of Informal Science Education (CAISE).
6. Burrell, J. (2016). How the machine 'thinks': Un-derstanding opacity in machine learning algo-rithms. Big Data & Society, 3(1). [
DOI:10.1177/2053951715622512]
7. Ceccaroni, L., Bibby, J., Roger, E., Flemons, P., Michael, K., Fagan, L., & Oliver, J. L. (2019). Opportunities and risks for citizen science in the age of artificial intelligence. Citizen Science: Theory and Practice, 4(1), 29. [
DOI:10.5334/cstp.241]
8. Chen, D., & Gomes, C.P. (2018). Bias reduction via end-to-end shift learning: Application to citizen science. [
DOI:10.1609/aaai.v33i01.3301493]
9. Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P. (2019). The future of hu-man-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. In T. Bui (Ed.), Proceedings of the Hawaii International Conference on System Sciences (HICSS) (pp. 1-10). ScholarSpace/AIS Electronic Library (AISeL). [
DOI:10.24251/HICSS.2019.034]
10. Doshi-Velez, F., & Kim, B. (2017). Towards a rig-orous science of interpretable machine learning.
11. Edwards, L., & Veale, M. (2018). Enslaving the algorithm: From a 'right to an explanation' to a 'right to better decisions'? IEEE Security and Pri-vacy, 16(3), 46-54. [
DOI:10.1109/MSP.2018.2701152]
12. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1, 1. [
DOI:10.1162/99608f92.8cd550d1]
13. Fortson, L., Masters, K., Nichol, R., Borne, K., Edmondson, E., Lintott, C., et al. (2012). Galaxy Zoo: Morphological classification and citizen sci-ence. [
DOI:10.1201/b11822-16]
14. Franzen, M. (2019). Changing science-society rela-tions in the digital age: The citizen science movement and its broader implications. In D. Si-mon, S. Kuhlmann, J. Stamm, & W. Canzler (Eds.), Handbook on science and public policy (pp. 336-356). Cheltenham: Edward Elgar. [
DOI:10.4337/9781784715946.00028]
15. Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2019). Explaining ex-planations: An overview of interpretability of ma-chine learning. In Proceedings - 2018 IEEE 5th international conference on data science and ad-vanced analytics - DSAA 2018 (pp. 80-89).. [
DOI:10.1109/DSAA.2018.00018]
16. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), Article 93, 1-42. [
DOI:10.1145/3236009]
17. Haklay, M. (2013). Citizen science and volunteered geographic information: Overview and typology of participation. In D. Z. Sui, S. Elwood, & M. Goodchild (Eds.), Crowdsourcing geographic knowledge (pp. 105-122). Dordrecht: Springer. [
DOI:10.1007/978-94-007-4587-2_7]
18. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. [
DOI:10.1038/s42256-019-0088-2]
19. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. [
DOI:10.1126/science.aaa8415] [
PMID]
20. Lehejcek, J., Adam, M., Tomasek, P., & Trojan, J. (2019). Informacni system pro spravu fotopasti [National database of photo trap records].
21. Lintott, C., & Reed, J. (2013). Human computation in citizen science. In P. Michelucci (Ed.), Hand-book of human computation (pp. 153-162). New York: Springer. [
DOI:10.1007/978-1-4614-8806-4_14]
22. Lukyanenko, R., Wiggins, A., & Rosser, H. K. (2019). Citizen science: An information quality research frontier. Information Systems Frontiers. [
DOI:10.1007/s10796-019-09915-z]
23. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A survey on bias and fairness in machine learning.
24. Michael, M., & Lupton, D. (2015). Toward a mani-festo for the 'public understanding of big data'. Public Understanding of Science, 25, 104-116. [
DOI:10.1177/0963662515609005] [
PMID]
25. Neal, L. (2013). The 'human sensor'. Bridging between human data and services. In P. Michelucci (Ed.), Handbook of human computa-tion (pp. 581-593). New York: Springer. [
DOI:10.1007/978-1-4614-8806-4_45]
26. Poncela-Casasnovas, J., Gutiérrez-Roig, M., Gracia-Lázaro, C., Vicens, J., Gómez-Gardeñes, J., Perelló, J., et al. (2016). Humans display a re-duced set of consistent behavioral phenotypes in dyadic games. Science Advances, 2(8), 1-9. [
DOI:10.1126/sciadv.1600451] [
PMID] [
]
27. Popenici, S., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(22). [
DOI:10.1186/s41039-017-0062-8] [
PMID] [
]
28. Rudin, C. (2019). Stop explaining black box ma-chine learning models for high stakes decisions and use interpretable models instead. Nature Ma-chine Intelligence, 1(5), 206-215. [
DOI:10.1038/s42256-019-0048-x] [
PMID] [
]
29. Sathya, R., & Abraham, A. (2013). Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence, 2(2), 34-38. [
DOI:10.14569/IJARAI.2013.020206]
30. Sullivan, D. P., Winsnes, C. F., Åkesson, L., Hjelmare, M., Wiking, M., Schutten, R., et al. (2018). Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nature Biotechnology, 36(9), 820-832. [
DOI:10.1038/nbt.4225] [
PMID]
31. Swanson, A., Kosmala, M., Lintott, C., & Packer, C. (2016). A generalized approach for producing, quantifying, and validating citizen science data from wildlife images. Conservation Biology, 30 (3), 520-531. [
DOI:10.1111/cobi.12695] [
PMID] [
]
32. Torney, C. J., Lloyd-Jones, D. J., Chevallier, M., Moyer, D. C., Maliti, H. T., Mwita, M., et al. (2019). A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey images. Methods in Ecology and Evolu-tion, 10(6), 779-787. [
DOI:10.1111/2041-210X.13165]
33. Trojan, J., Schade, S., Lemmens, R., & Frantál, B. (2019). Citizen science as a new approach in ge-ography and beyond: Review and reflections. Moravian Geographical Reports, 27(4), 254-264. [
DOI:10.2478/mgr-2019-0020]
34. Vicens, J., Bueno-Guerra, N., Gutiérrez-Roig, M., Gracia-Lázaro, C., Gómez-Gardeñes, J., Perelló, J., et al. (2018). Resource heterogeneity leads to unjust effort distribution in climate change miti-gation. PLoS One, 13(10), 1-17. [
DOI:10.1371/journal.pone.0204369] [
PMID] [
]
35. Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bam-ford, S., Dickinson, H., et al. (2019). Galaxy Zoo: Probabilistic morphology through Bayesian CNNs and active learning. Monthly Notices of the Royal Astronomical Society, 491(2), 1554-1574. [
DOI:10.1093/mnras/stz2816]
36. Watson, D., & Floridi, L. (2018). Crowdsourced science: Sociotechnical epistemology in the e-research paradigm. Synthese, 195, 741-764. [
DOI:10.1007/s11229-016-1238-2]
37. Willi, M., Pitman, R. T., Cardoso, A. W., Locke, C., Swanson, A., Boyer, A., et al. (2019). Identifying animal species in camera trap images using deep learning and citizen science. Methods in Ecology and Evolution, 10(1), 80-91. [
DOI:10.1111/2041-210X.13099]