Merge pull request #89 from b2m/issue-88

Mark links in references as links

Author: MikeTrizna

_episodes/05-managing-data-bias.md

@@ -159,18 +159,17 @@ How else might GLAM staff actively help manage bias in machine learning approach
 > {: .solution}
 {: .challenge}
 
-
 >## Resources Consulted & Recommended Reading
-> - Barbosa, N., & Chen, M. (2021). Rehumanized Crowdsourcing. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Dl.acm.org. Retrieved 29 March 2021, from https://dl.acm.org/doi/10.1145/3290605.3300773. 
-> - Barlow, R. (2014). BU Research: A Riddle Reveals Depth of Gender Bias. BU Today. Boston University. Retrieved 29 March 2021, from https://www.bu.edu/articles/2014/bu-research-riddle-reveals-the-depth-of-gender-bias.
-> - Catanzaro, B. (2019, December 4). "Datasets make algorithms: how creating, curating, and distributing data creates modern AI." [Video file]. Retrieved from https://library.stanford.edu/projects/fantastic-futures.
-> - Coleman, C. (2020). Managing Bias When Library Collections Become Data. International Journal Of Librarianship, 5(1), 8-19. https://doi.org/10.23974/ijol.2020.vol5.1.162.
-> - Ekowo, M. (2016). Why Numbers can be Neutral but Data Can’t. New America. Retrieved 29 March 2021, from https://www.newamerica.org/education-policy/edcentral/numbers-can-neutral-data-cant/. 
-> - Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J., Wallach, H., Daumeé III, H., & Crawford, K. (2020). Datasheets for Datasets. arXiv.org. Retrieved 29 March 2021, from https://arxiv.org/abs/1803.09010v3.
-> - Hellström, T., Dignum, V., & Bensch, S. (2020). Bias in Machine Learning What is it Good (and Bad) for?. arXiv preprint. Retrieved 20 April 2021, from https://arxiv.org/abs/2004.00686v2.
-> - Jo, E., & Gebru, T. (2020). Lessons from archives. Proceedings Of The 2020 Conference On Fairness, Accountability, And Transparency. https://doi.org/10.1145/3351095.3372829. 
-> - Mayson, Sandra Gabriel, Bias In, Bias Out (2019). 128 Yale Law Journal 2218, University of Georgia School of Law Legal Studies Research Paper No. 2018-35, Available at SSRN: https://ssrn.com/abstract=3257004.
-> - Padilla, T. (2019). Responsible Operations: Data Science, Machine Learning, and AI in Libraries. OCLC Research Position Paper. https://doi.org/10.25333/xk7z-9g97. 
+>
+> - Barbosa, N., & Chen, M. (2021). Rehumanized Crowdsourcing. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Dl.acm.org. Retrieved 29 March 2021, from <https://dl.acm.org/doi/10.1145/3290605.3300773>.
+> - Barlow, R. (2014). BU Research: A Riddle Reveals Depth of Gender Bias. BU Today. Boston University. Retrieved 29 March 2021, from <https://www.bu.edu/articles/2014/bu-research-riddle-reveals-the-depth-of-gender-bias>.
+> - Catanzaro, B. (2019, December 4). "Datasets make algorithms: how creating, curating, and distributing data creates modern AI." [Video file]. Retrieved from <https://library.stanford.edu/projects/fantastic-futures>.
+> - Coleman, C. (2020). Managing Bias When Library Collections Become Data. International Journal Of Librarianship, 5(1), 8-19. <https://doi.org/10.23974/ijol.2020.vol5.1.162>.
+> - Ekowo, M. (2016). Why Numbers can be Neutral but Data Can’t. New America. Retrieved 29 March 2021, from <https://www.newamerica.org/education-policy/edcentral/numbers-can-neutral-data-cant/>.
+> - Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J., Wallach, H., Daumeé III, H., & Crawford, K. (2020). Datasheets for Datasets. arXiv.org. Retrieved 29 March 2021, from <https://arxiv.org/abs/1803.09010v3>.
+> - Hellström, T., Dignum, V., & Bensch, S. (2020). Bias in Machine Learning What is it Good (and Bad) for?. arXiv preprint. Retrieved 20 April 2021, from <https://arxiv.org/abs/2004.00686v2>.
+> - Jo, E., & Gebru, T. (2020). Lessons from archives. Proceedings Of The 2020 Conference On Fairness, Accountability, And Transparency. <https://doi.org/10.1145/3351095.3372829>.
+> - Mayson, Sandra Gabriel, Bias In, Bias Out (2019). 128 Yale Law Journal 2218, University of Georgia School of Law Legal Studies Research Paper No. 2018-35, Available at SSRN: <https://ssrn.com/abstract=3257004>.
+> - Padilla, T. (2019). Responsible Operations: Data Science, Machine Learning, and AI in Libraries. OCLC Research Position Paper. <https://doi.org/10.25333/xk7z-9g97>.
 
 {: .checklist }
-

_episodes/06-applying-machine-learning.md

@@ -227,20 +227,18 @@ Now you have created a model and have some way of using it you may be hoping tha
 
 **Retraining a model**: in some situations a model won’t just be trained once. You may want to retrain the model when the type of data changes or you have more training data that can be used. One typical example of this kind of process is models which predict some metrics for each quarter. After that quarter has passed you will have ‘ground truth’ data available that you will likely want to use in your model. Another source of training data might be generated by having a human in the loop. If a model is showing predictions to a human who can accept or reject these predictions this gives you additional examples your model can learn from.
 
- 
-
-
 >## Resources Consulted & Recommended Reading
+>
 > - Ameisen, Emmanuel. Building Machine Learning Powered Applications: Going from Idea to Product, 2020.
-> - Cordell, Ryan. ‘Machine Learning + Libraries’. LC Labs. Accessed 28 March 2021. https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf.
-> - Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. ‘Datasheets for Datasets’. ArXiv:1803.09010 [Cs], 19 March 2020. http://arxiv.org/abs/1803.09010.
+> - Cordell, Ryan. ‘Machine Learning + Libraries’. LC Labs. Accessed 28 March 2021. <https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf>.
+> - Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. ‘Datasheets for Datasets’. ArXiv:1803.09010 [Cs], 19 March 2020. <http://arxiv.org/abs/1803.09010>.
 > - Howard, Jeremy, Sylvain Gugger, and an O’Reilly Media Company Safari. Deep Learning for Coders with Fastai and PyTorch, 2020.
 > - Lakshmanan, Valliappa, Sara Robinson, Michael Munn, and an O’Reilly Media Company Safari. Machine Learning Design Patterns, 2021.
-> - Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. ‘Model Cards for Model Reporting’. Proceedings of the Conference on Fairness, Accountability, and Transparency, 29 January 2019, 220–29. https://doi.org/10.1145/3287560.3287596.
-> - Padilla, Thomas. ‘Responsible Operations: Data Science, Machine Learning, and AI in Libraries’. OCLC, 26 August 2020. https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html.
-> - Slee, Tom. ‘The Incompatible Incentives of Private Sector AI’. Tom Slee, 31 March 2019. https://tomslee.github.io/publication/oup_private_sector_ai/.
-> - Suresh, Harini, and John V. Guttag. ‘A Framework for Understanding Unintended Consequences of Machine Learning’. ArXiv:1901.10002 [Cs, Stat], 17 February 2020. http://arxiv.org/abs/1901.10002.
-> - Omoju Miller. ‘The Myth of Innate Ability in Tech’. Accessed 20 March 2021. http://omojumiller.com/articles/The-Myth-Of-Innate-Ability-In-Tech.
-> - Thomas, Rachel. ‘The Problem with Metrics Is a Big Problem for AI · Fast.Ai’. fast.ai blog. Accessed 18 March 2021. https://www.fast.ai/2019/09/24/metrics/.
-> 
+> - Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. ‘Model Cards for Model Reporting’. Proceedings of the Conference on Fairness, Accountability, and Transparency, 29 January 2019, 220–29. <https://doi.org/10.1145/3287560.3287596>.
+> - Padilla, Thomas. ‘Responsible Operations: Data Science, Machine Learning, and AI in Libraries’. OCLC, 26 August 2020. <https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html>.
+> - Slee, Tom. ‘The Incompatible Incentives of Private Sector AI’. Tom Slee, 31 March 2019. <https://tomslee.github.io/publication/oup_private_sector_ai/>.
+> - Suresh, Harini, and John V. Guttag. ‘A Framework for Understanding Unintended Consequences of Machine Learning’. ArXiv:1901.10002 [Cs, Stat], 17 February 2020. <http://arxiv.org/abs/1901.10002>.
+> - Omoju Miller. ‘The Myth of Innate Ability in Tech’. Accessed 20 March 2021. <http://omojumiller.com/articles/The-Myth-Of-Innate-Ability-In-Tech>.
+> - Thomas, Rachel. ‘The Problem with Metrics Is a Big Problem for AI · Fast.Ai’. fast.ai blog. Accessed 18 March 2021. <https://www.fast.ai/2019/09/24/metrics/>.
+
 {: .checklist }

_episodes/07-ecosystem.md

@@ -52,18 +52,18 @@ There are various ways in which this work can be documented. Academic papers are
 
 Beyond academic papers, there are a growing number of tools for managing machine learning projects which include data versioning, experiment tracking and other features for documenting work. Public version control repository like GitHub or GitLab offer venus for sharing code and you may explore using other tools like Jupyter notebooks to help make your models more accessible to others. 
 
-
 >## Resources Consulted & Recommended Reading
+>
 > - Ameisen, Emmanuel. Building Machine Learning Powered Applications: Going from Idea to Product, 2020.
-> - Cordell, Ryan. ‘Machine Learning + Libraries’. LC Labs. Accessed 28 March 2021. https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf.
-> - Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. ‘Datasheets for Datasets’. ArXiv:1803.09010 [Cs], 19 March 2020. http://arxiv.org/abs/1803.09010.
+> - Cordell, Ryan. ‘Machine Learning + Libraries’. LC Labs. Accessed 28 March 2021. <https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf>.
+> - Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. ‘Datasheets for Datasets’. ArXiv:1803.09010 [Cs], 19 March 2020. <http://arxiv.org/abs/1803.09010>.
 > - Howard, Jeremy, Sylvain Gugger, and an O’Reilly Media Company Safari. Deep Learning for Coders with Fastai and PyTorch, 2020.
 > - Lakshmanan, Valliappa, Sara Robinson, Michael Munn, and an O’Reilly Media Company Safari. Machine Learning Design Patterns, 2021.
-> - Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. ‘Model Cards for Model Reporting’. Proceedings of the Conference on Fairness, Accountability, and Transparency, 29 January 2019, 220–29. https://doi.org/10.1145/3287560.3287596.
-> - Padilla, Thomas. ‘Responsible Operations: Data Science, Machine Learning, and AI in Libraries’. OCLC, 26 August 2020. https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html.
-> - Slee, Tom. ‘The Incompatible Incentives of Private Sector AI’. Tom Slee, 31 March 2019. https://tomslee.github.io/publication/oup_private_sector_ai/.
-> - Suresh, Harini, and John V. Guttag. ‘A Framework for Understanding Unintended Consequences of Machine Learning’. ArXiv:1901.10002 [Cs, Stat], 17 February 2020. http://arxiv.org/abs/1901.10002.
-> - Omoju Miller. ‘The Myth of Innate Ability in Tech’. Accessed 20 March 2021. http://omojumiller.com/articles/The-Myth-Of-Innate-Ability-In-Tech.
-> - Thomas, Rachel. ‘The Problem with Metrics Is a Big Problem for AI · Fast.Ai’. fast.ai blog. Accessed 18 March 2021. https://www.fast.ai/2019/09/24/metrics/.
-> 
+> - Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. ‘Model Cards for Model Reporting’. Proceedings of the Conference on Fairness, Accountability, and Transparency, 29 January 2019, 220–29. <https://doi.org/10.1145/3287560.3287596>.
+> - Padilla, Thomas. ‘Responsible Operations: Data Science, Machine Learning, and AI in Libraries’. OCLC, 26 August 2020. <https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html>.
+> - Slee, Tom. ‘The Incompatible Incentives of Private Sector AI’. Tom Slee, 31 March 2019. <https://tomslee.github.io/publication/oup_private_sector_ai/>.
+> - Suresh, Harini, and John V. Guttag. ‘A Framework for Understanding Unintended Consequences of Machine Learning’. ArXiv:1901.10002 [Cs, Stat], 17 February 2020. <http://arxiv.org/abs/1901.10002>.
+> - Omoju Miller. ‘The Myth of Innate Ability in Tech’. Accessed 20 March 2021. <http://omojumiller.com/articles/The-Myth-Of-Innate-Ability-In-Tech>.
+> - Thomas, Rachel. ‘The Problem with Metrics Is a Big Problem for AI · Fast.Ai’. fast.ai blog. Accessed 18 March 2021. <https://www.fast.ai/2019/09/24/metrics/>.
+
 {: .checklist }