ABOUT ML Reference Document

Last Updated Data Integration Data Integration

Data Integration

We use the tenets of the Doan et. al (2012) definition which infers that proper data integration offers “uniform access to a set of autonomous and heterogeneous data sources.”

The task is often challenging for system logical, social, and administrative reasons.

Data integration might mean/include a few things: connecting data sources, data distribution to users, inclusion of data pipeline and data management, maintenance of data, audit of data usage and issues. It might be useful to separate the technical and data science aspect from the safety and continuous use of data. Use Use

Stating the intended and permitted uses of a dataset can be helpful for users to understand whether the dataset is appropriate for their projects. In particular, such disclosures should include information on how the composition of the dataset or way in which it was collected and cleaned might affect future uses, including what the data can and cannot represent, what applications and conclusions are not appropriate to draw, and what types of consent was obtained from data subjects. This is also the section to discuss guardrails on data use to avoid re-identification through certain kinds of data joins. Links to existing literature that uses the dataset can be helpful for illustrative purposes. This section would also include information on uses to be avoided and explanations of possible adverse consequences that might result from inappropriate uses. For data sets with high potential for misuse, this section may instead list the acceptable uses for the dataset and forbid all others. In the future, we may explore how documentation recommendations fit in with ongoing data licensing projects.A. Doan, A. Y. Halevy, and Z. G. Ives. Principles of Data Integration. Morgan Kaufmann, 2012


Some advantages to these disclosures are that information about the intent of the dataset helps give potential users greater context and minimizes potential misuse. It is important to clarify common misconceptions. For example, data collected to classify people’s facial expressions (smiling, frowning, etc.) might not be appropriate to use to classify people’s underlying moods (happiness, sadness, etc.). This information also would help hold users of the dataset accountable.

Some challenges to making these disclosures include that it is difficult to identify all potential malicious uses of a dataset. In addition, malicious actors might purposefully use a dataset in improper ways. Cautious legal departments may also be concerned about the possibility for liability with disclosing appropriate and inappropriate uses of the data. This can be mitigated through consultation with legal counsel, although that may be impractical where data sources include the public and are used at a high frequency (e.g., capturing visual data in airports). Explorations related to the following research questions could uncover insights into barriers to implementation along with mitigation strategies to overcome those barriers.

Sample Documentation Questions
  • Is there a repository that links to any or all papers or systems that use the dataset? (Gebru et al. 2018)
  • Are there tasks for which the dataset should not be used? (Gebru et al. 2018)
  • Was there consent obtained by data subjects, and does that consent place limits on the use of the data? Distribution Distribution

Distribution disclosures should relay how the dataset’s creators will distribute the data for use and update the data, either internally between segments of their company or publically. This makes it easier for people to find and use the data and clarifies the intended audience. Such disclosures should include information about the accessibility of the dataset and the intended audience. Licensing and the timing of licenses and consent are important considerations in this disclosure process. Depending on how broadly the dataset is distributed and the original consent given, additional consent might need to be obtained from the subjects of the data. Specific information on recipients may also need to be provided as a matter of local law. The distribution process should involve appropriate steps to preserve the privacy of data subjects. For example, there might be a log-in needed to access the dataset and dataset users might need to sign a contract stipulating the conditions of use. This would have the added benefit of ensuring that if someone withdrew their consent to have their data included in the dataset, their data could be deleted in one place. However, if the data is downloaded or stored by another user, consent revocation becomes more difficult to manage, and future tools need to be developed to manage this process. In addition, the dataset should be transformed such that the reidentification risk is permissible in line with relevant legal regimes prior to distribution and safeguards should be put in place to prevent de-anonymization.

Sample Documentation Questions
  • How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? (Gebru et al. 2018)
  • Will the dataset be distributed under a copyright or other intellectual property (IP) license and/or under applicable terms of use (ToU)? (Gebru et al. 2018)
  • Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? (Gebru et al. 2018)
  • What documentation and metadata will you be releasing with the model? (Gebru et al. 2018) Maintenance Maintenance

Providing information about the maintenance of datasets is important for helping users know whether they are using the latest dataset and whether the dataset will be kept up to date. The default assumption is generally that datasets are not maintained. If the dataset is not maintained, however, there can be concerns about the interpretability and applicability of the dataset for new projects. Developers who are interested in using the data should be informed about these potential issues so that they can draw appropriate inferences.


General Data Protection Regulation (GDPR) is a legal framework that sets guidelines for the collection and processing of personal information from individuals who live in the European Union (EU). This and other international, federal, state and local regulations could impact the generalizability of documentation recommendations.

Moreover, if the dataset is not maintained, it can be difficult for individuals to remove their data. This is especially an issue with criminal records, which may need to be expunged periodically depending on local law. For developers for the EU, this can also create complications with GDPR, the overarching principle of accuracy, and the rights to rectification and to be forgotten (“right to erasure”). To ensure that users are using the latest version of the dataset, measures can be taken to ensure that users cannot download past versions of a dataset or that the dataset has an expiration date after which it is unusable without being updated. However, such measures could prevent comparisons of how different machine learning systems work on old versus new datasets.


Some benefits are that users would be able to better understand why and how the dataset changed. Proper maintenance techniques also make it possible for individuals to remove their content if they want to.

Some disadvantages are that maintaining datasets can be time- and resource-intensive and being explicit about plans for maintenance does, to some extent, require the dataset developer to follow through. In addition, doing maintenance well can be difficult, as there are potential issues with versioning and shifts in technology. Further, completely eliminating older datasets (as opposed to simply marking them as obsolete) can prevent historical analysis of how datasets have changed over time. Explorations related to the following research questions could uncover insights into barriers to implementation as well as meaningful concrete examples of GDPR relevance to ML documentation.

Sample Documentation Questions
  • Is there an erratum (list of mistakes)? (Gebru et al. 2018)
  • Will the dataset be updated? If so, how often, by whom, and how will updates be communicated to users? (Gebru et al. 2018)
  • When will the dataset expire? Is there a set time limit after which the data should be considered obsolete?

Those attempting documentation practices within any phase of the machine learning lifecycle can consider how ethics approval might be customizable for different disciplines or change over time by paying particular attention to:

  1. Establishing ethics scores and approvals
  2. Developing clear objectives during data collection with benchmarks and constraints for review (i.e. in 5, 7, or 10 years)
  3. Ensuring ease of contact for participants to whom the data belongs, asking questions such as “If you are doing longitudinal processes with a fairly transient population, how do you ensure you can find that person later to re-establish consent? “ and “Is that information still relevant for our use?”
  4. Committing to, after finding issues with a dataset, discontinuing use and/or putting mitigation practices in place
  5. Considering your team’s ability to fix, update, or remove any model or data released from distribution
  6. Replacing problematic benchmarks and encouraging use of better alternatives

ABOUT ML Reference Document

Section 0: How to Use this Document

Recommended Reading Plan

Quick Guides

How We Define

Contact for Support

Section 1: Project Overview

1.1 Statement of Importance for ABOUT ML Project

1.1.0 Importance of Transparency: Why a Company Motivated by the Bottom Line Should Adopt ABOUT ML Recommendations

1.1.1 About This Document and Version Numbering

1.1.2 ABOUT ML Goals and Plan

1.1.3 ABOUT ML Project Process and Timeline Overview

1.1.4 Who Is This Project For? Audiences for the ABOUT ML Resources Stakeholders That Should Be Consulted While Putting Together ABOUT ML Resources Audiences for ABOUT ML Documentation Artifacts Whose Voices Are Currently Reflected in ABOUT ML? Origin Story

Section 2: Literature Review (Current Recommendations on Documentation for Transparency in the ML Lifecycle)

2.1 Demand for Transparency and AI Ethics in ML Systems 

2.2 Documentation to Operationalize AI Ethics Goals

2.2.1 Documentation as a Process in the ML Lifecycle

2.2.2 Key Process Considerations for Documentation

2.3 Research Themes on Documentation for Transparency 

2.3.1 System Design and Set Up

2.3.2 System Development

2.3.3 System Deployment

Section 3: Preliminary Synthesized Documentation Suggestions

3.4.1 Suggested Documentation Sections for Datasets Data Specification Motivation Data Curation Collection Processing Composition Types and Sources of Judgement Calls Data Integration Use Distribution Maintenance

3.4.2 Suggested Documentation Sections for Models Model Specifications Model Training Evaluation Model Integration Maintenance

Section 4: Current Challenges of Implementing Documentation

Section 5: Conclusions

Version 0

Version 1

Appendix A: Compiled List of Documentation Questions 

Fact Sheets (Arnold et al. 2018)

Data Sheets (Gebru et al. 2018)

Model Cards (Mitchell et al. 2018)

A “Nutrition Label” for Privacy (Kelley et al. 2009)

The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards (Holland et al. 2019)

Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science (Bender and Friedman 2018)

Appendix B: Diverse Voices Process and Artifacts

Procurement Recruitment Email

Procurement Confirmation Email 

Appendix C: Glossary

Sources Cited

  1. Holstein, K., Vaughan, J.W., Daumé, H., Dudík, M., u0026amp; Wallach, H.M. (2018). Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? CHI.
  2. Young, M., Magassa, L. and Friedman, B. (2019) Toward inclusive tech policy design: a method for underrepresented voices to strengthen tech policy documents. Ethics and Information Technology 21(2), 89-103.
  3. World Wide Web Consortium Process Document (W3C) process outlined here: https://www.w3.org/2019/Process-20190301/
  4. Internet Engineering Task Force (IETF) process outlined here: https://www.ietf.org/standards/process/
  5. The Web Hypertext Application Technology Working Group (WHATWG) process outlined here: https://whatwg.org/faq#process
  6. Oever, N., Moriarty, K. The Tao of IETF: A novice's guide to the Internet Engineering Task Force. https://www.ietf.org/about/participate/tao/.
  7. Young, M., Magassa, L. and Friedman, B. (2019) Toward inclusive tech policy design: a method for underrepresented voices to strengthen tech policy documents. Ethics and Information Technology 21(2), 89-103.
  8. Friedman, B, Kahn, Peter H., and Borning, A., (2008) Value sensitive design and information systems. In Kenneth Einar Himma and Herman T. Tavani (Eds.) The Handbook of Information and Computer Ethics., (pp. 70-100) John Wiley u0026amp; Sons, Inc. http://jgustilo.pbworks.com/f/the-handbook-of-information-and-computer-ethics.pdf#page=104; Davis, J., and P. Nathan, L. (2015). Value sensitive design: applications, adaptations, and critiques. Handbook of Ethics, Values, and Technological Design: Sources, Theory, Values and Application Domains. (pp. 11-40) DOI: 10.1007/978-94-007-6970-0_3. https://www.researchgate.net/publication/283744306_Value_Sensitive_Design_Applications_Adaptations_and_Critiques; Borning, A. and Muller, M. (2012). Next steps for value sensitive design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). (pp 1125-1134) DOI: https://doi.org/10.1145/2207676.2208560 https://dl.acm.org/citation.cfm?id=2208560
  9. Pichai, S., (2018). AI at Google: our principles. The Keyword. https://www.blog.google/technology/ai/ai-principles/; IBM’s Principles for Trust and Transparency. IBM Policy. https://www.ibm.com/blogs/policy/trust-principles/; Microsoft AI principles. Microsoft. https://www.microsoft.com/en-us/ai/our-approach-to-ai; Ethically Aligned Design – Version II. IEEE. https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ead_v2.pdf
  10. Zeng, Y., Lu, E., and Huangfu, C. (2018) Linking artificial intelligence principles. CoRR https://arxiv.org/abs/1812.04814.
  11. essica Fjeld, Hannah Hilligoss, Nele Achten, Maia Levy Daniel, Sally Kagay, and Joshua Feldman, (2018). Principled artificial intelligence - a map of ethical and rights based approaches, Berkman Center for Internet and Society, https://ai-hr.cyber.harvard.edu/primp-viz.html
  12. Jobin, A., Ienca, M., u0026amp; Vayena, E. (2019). Artificial Intelligence: the global landscape of ethics guidelines. arXiv preprint arXiv:1906.11668. https://arxiv.org/pdf/1906.11668.pdf
  13. Jobin, A., Ienca, M., u0026amp; Vayena, E. (2019). Artificial Intelligence: the global landscape of ethics guidelines. arXiv preprint arXiv:1906.11668. https://arxiv.org/pdf/1906.11668.pdf
  14. Ananny, M., and Kate Crawford (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media and Society 20 (3): 973-989.
  15. Whittlestone, J., Nyrup, R., Alexandrova, A., u0026amp; Cave, S. (2019, January). The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the AAAI/ACM Conference on AI Ethics and Society, Honolulu, HI, USA (pp. 27-28). http://www.aies-conference.com/wp-content/papers/main/AIES-19_paper_188.pdf; Mittelstadt, B. (2019). AI Ethics–Too Principled to Fail? https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3391293
  16. Greene, D., Hoffmann, A. L., u0026amp; Stark, L. (2019, January). Better, nicer, clearer, fairer: A critical assessment of the movement for ethical artificial intelligence and machine learning. In Proceedings of the 52nd Hawaii International Conference on System Sciences. https://scholarspace.manoa.hawaii.edu/handle/10125/59651
  17. Raji, I. D., u0026amp; Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products. In AAAI/ACM Conf. on AI Ethics and Society (Vol. 1). https://www.media.mit.edu/publications/actionable-auditing-investigating-the-impact-of-publicly-naming-biased-performance-results-of-commercial-ai-products/
  18. Algorithmic Impact Assessment (2019) Government of Canada https://www.canada.ca/en/government/system/digital-government/modern-emerging-technologies/responsible-use-ai/algorithmic-impact-assessment.html
  19. Benjamin, M., Gagnon, P., Rostamzadeh, N., Pal, C., Bengio, Y., u0026amp; Shee, A. (2019). Towards Standardization of Data Licenses: The Montreal Data License. arXiv preprint arXiv:1903.12262. https://arxiv.org/abs/1903.12262; Responsible AI Licenses v0.1. RAIL: Responsible AI Licenses. https://www.licenses.ai/ai-licenses
  20. See Citation 5
  21. Safe Face Pledge. https://www.safefacepledge.org/; Montreal Declaration on Responsible AI. Universite de Montreal. https://www.montrealdeclaration-responsibleai.com/; The Toronto Declaration: Protecting the right to equality and non-discrimination in machine learning systems. (2018). Amnesty International and Access Now. https://www.accessnow.org/cms/assets/uploads/2018/08/The-Toronto-Declaration_ENG_08-2018.pdf ; Dagsthul Declaration on the application of machine learning and artificial intelligence for social good. https://www.dagstuhl.de/fileadmin/redaktion/Programm/Seminar/19082/Declaration/Declaration.pdf
  22. Dobbe, R., Dean, S., Gilbert, T., u0026amp; Kohli, N. (2018). A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics. https://arxiv.org/pdf/1807.00553.pdf
  23. Wagstaff, K. (2012). Machine learning that matters. https://arxiv.org/pdf/1206.4656.pdf ; Friedman, B., Kahn, P. H., Borning, A., u0026amp; Huldtgren, A. (2013). Value sensitive design and information systems. In Early engagement and new technologies: Opening up the laboratory (pp. 55-95). Springer, Dordrecht. https://vsdesign.org/publications/pdf/non-scan-vsd-and-information-systems.pdf
  24. Dobbe, R., Dean, S., Gilbert, T., u0026amp; Kohli, N. (2018). A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics. https://arxiv.org/pdf/1807.00553.pdf
  25. Safe Face Pledge. https://www.safefacepledge.org/
  26. Montreal Declaration on Responsible AI. Universite de Montreal. https://www.montrealdeclaration-responsibleai.com/
  27. Diverse Voices How To Guide. Tech Policy Lab, University of Washington. https://techpolicylab.uw.edu/project/diverse-voices/
  28. Bender, E. M., u0026amp; Friedman, B. (2018). Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6, 587-604.
  29. Ethically Aligned Design – Version II. IEEE. https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ead_v2.pdf
  30. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumeé III, H., u0026amp; Crawford, K. (2018). Datasheets for datasets. https://arxiv.org/abs/1803.09010 https://arxiv.org/abs/1803.09010; Hazard Communication Standard: Safety Data Sheets. Occupational Safety and Health Administration, US Department of Labor. https://www.osha.gov/Publications/OSHA3514.html
  31. Holland, S., Hosny, A., Newman, S., Joseph, J., u0026amp; Chmielinski, K. (2018). The dataset nutrition label: A framework to drive higher data quality standards. https://arxiv.org/abs/1805.03677; Kelley, P. G., Bresee, J., Cranor, L. F., u0026amp; Reeder, R. W. (2009). A nutrition label for privacy. In Proceedings of the 5th Symposium on Usable Privacy and Security (p. 4). ACM. http://cups.cs.cmu.edu/soups/2009/proceedings/a4-kelley.pdf
  32. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... u0026amp; Gebru, T. (2019, January). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220-229). ACM. https://arxiv.org/abs/1810.03993
  33. Hind, M., Mehta, S., Mojsilovic, A., Nair, R., Ramamurthy, K. N., Olteanu, A., u0026amp; Varshney, K. R. (2018). Increasing Trust in AI Services through Supplier's Declarations of Conformity. https://arxiv.org/abs/1808.07261
  34. Veale M., Van Kleek M., u0026amp; Binns R. (2018) ‘Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making’ in Proceedings of the ACM Conference on Human Factors in Computing Systems, CHI 2018. https://arxiv.org/abs/1802.01029.
  35. Benjamin, M., Gagnon, P., Rostamzadeh, N., Pal, C., Bengio, Y., u0026amp; Shee, A. (2019). Towards Standardization of Data Licenses: The Montreal Data License. https://arxiv.org/abs/1903.12262
  36. Cooper, D. M. (2013, April). A Licensing Approach to Regulation of Open Robotics. In Paper for presentation for We Robot: Getting down to business conference, Stanford Law School.
  37. Responsible AI Practices. Google AI. https://ai.google/education/responsible-ai-practices
  38. Everyday Ethics for Artificial Intelligence. (2019). IBM. https://www.ibm.com/watson/assets/duo/pdf/everydayethics.pdf
  39. Federal Trade Commission. (2012). Best Practices for Common Uses of Facial Recognition Technologies (Staff Report). Federal Trade Commission, 30. https://www.ftc.gov/sites/default/files/documents/reports/facing-facts-best-practices-common-uses-facial-recognition-technologies/121022facialtechrpt.pdf
  40. Microsoft (2018). Responsible bots: 10 guidelines for developers of conversational AI. https://www.microsoft.com/en-us/research/uploads/prod/2018/11/Bot_Guidelines_Nov_2018.pdf
  41. Tramer, F., Atlidakis, V., Geambasu, R., Hsu, D., Hubaux, J. P., Humbert, M., ... u0026amp; Lin, H. (2017, April). FairTest: Discovering unwarranted associations in data-driven applications. In 2017 IEEE European Symposium on Security and Privacy (EuroSu0026amp;P) (pp. 401-416). IEEE. https://github.com/columbia/fairtest, https://www.mhumbert.com/publications/eurosp17.pdf
  42. Kishore Durg (2018). Testing AI: Teach and Test to raise responsible AI. Accenture Technology Blog. https://www.accenture.com/us-en/insights/technology/testing-AI
  43. Kush R. Varshney (2018). Introducing AI Fairness 360. IBM Research Blog. https://www.ibm.com/blogs/research/2018/09/ai-fairness-360/
  44. Dave Gershgorn (2018). Facebook says it has a tool to detect bias in its artificial intelligence. Quartz. https://qz.com/1268520/facebook-says-it-has-a-tool-to-detect-bias-in-its-artificial-intelligence/
  45. James Wexler. (2018) The What-If Tool: Code-Free Probing of Machine Learning Models. Google AI Blog. https://ai.googleblog.com/2018/09/the-what-if-tool-code-free-probing-of.html
  46. Miro Dudík, John Langford, Hanna Wallach, and Alekh Agarwal (2018). Machine Learning for fair decisions. Microsoft Research Blog. https://www.microsoft.com/en-us/research/blog/machine-learning-for-fair-decisions/
  47. Veale, M., Binns, R., u0026amp; Edwards, L. (2018). Algorithms that Remember: Model Inversion Attacks and Data Protection Law. Phil. Trans. R. Soc. A, 376, 20180083. https://doi.org/10/gfc63m
  48. Floridi, L. (2010, February). Information: A Very Short Introduction.
  49. Data Information Specialists Committee UK, 2007. http://www.disc-uk.org/qanda.html.
  50. Harwell, Drew. “Federal Study Confirms Racial Bias of Many Facial-Recognition Systems, Casts Doubt on Their Expanding Use.” The Washington Post, WP Company, 21 Dec. 2019, www.washingtonpost.com/technology/2019/12/19/federal-study-confirms-racial-bias-many-facial-recognition-systems-casts-doubt-their-expanding-use/
  51. Hildebrandt, M. (2019) ‘Privacy as Protection of the Incomputable Self: From Agnostic to Agonistic Machine Learning’, Theoretical Inquiries in Law, 20(1) 83–121.
  52. D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., ... u0026amp; Sculley, D. (2020). Underspecification presents challenges for credibility in modern machine learning. arXiv preprint arXiv:2011.03395.
  53. Selinger, E. (2019). ‘Why You Can’t Really Consent to Facebook’s Facial Recognition’, One Zero. https://onezero.medium.com/why-you-cant-really-consent-to-facebook-s-facial-recognition-6bb94ea1dc8f
  54. Lum, K., u0026amp; Isaac, W. (2016). To predict and serve?. Significance, 13(5), 14-19. https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x
  55. LabelInsight (2016). “Drive Long-Term Trust u0026amp; Loyalty Through Transparency”. https://www.labelinsight.com/Transparency-ROI-Study
  56. Crawford and Paglen, https://www.excavating.ai/
  57. Geva, Mor u0026amp; Goldberg, Yoav u0026amp; Berant, Jonathan. (2019). Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets. https://arxiv.org/pdf/1908.07898.pdf
  58. Bender, E. M., u0026amp; Friedman, B. (2018). Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6, 587-604.
  59. Desmond U. Patton et al (2017).
  60. See Cynthia Dwork et al.,
  61. Katta Spiel, Oliver L. Haimson, and Danielle Lottridge. (2019). How to do better with gender on surveys: a guide for HCI researchers. Interactions. 26, 4 (June 2019), 62-65. DOI: https://doi.org/10.1145/3338283
  62. A. Doan, A. Y. Halevy, and Z. G. Ives. Principles of Data Integration. Morgan Kaufmann, 2012
  63. Momin M. Malik. (2019). Can algorithms themselves be biased? Medium. https://medium.com/berkman-klein-center/can-algorithms-themselves-be-biased-cffecbf2302c
  64. Fire, Michael, and Carlos Guestrin (2019). “Over-Optimization of Academic Publishing Metrics: Observing Goodhart’s Law in Action.” GigaScience 8 (giz053). https://doi.org/10.1093/gigascience/giz053.
  65. Vogelsang, A., u0026amp; Borg, M. (2019, September). Requirements engineering for machine learning: Perspectives from data scientists. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW) (pp. 245-251). IEEE
  66. Eckersley, P. (2018). Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function). arXiv preprint arXiv:1901.00064.
  67. Partnership on AI. Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System, Requirement 5.
  68. Eckersley, P. (2018). Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function). arXiv preprint arXiv:1901.00064.https://arxiv.org/abs/1901.00064
  69. If it is not, there is likely a bug in the code. Checking a predictive model's performance on the training set cannot distinguish irreducible error (which comes from intrinsic variance of the system) from error introduced by bias and variance in the estimator; this is universal, and has nothing to do with different settings or
  70. Selbst, Andrew D. and Boyd, Danah and Friedler, Sorelle and Venkatasubramanian, Suresh and Vertesi, Janet (2018). “Fairness and Abstraction in Sociotechnical Systems”, ACM Conference on Fairness, Accountability, and Transparency (FAT*). https://ssrn.com/abstract=3265913
  71. Tools that can be used to explore and audit the predictive model fairness include FairML, Lime, IBM AI Fairness 360, SHAP, Google What-If Tool, and many others
  72. Wagstaff, K. (2012). Machine learning that matters. arXiv preprint arXiv:1206.4656. https://arxiv.org/abs/1206.4656
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