The Machine Learning Manifesto

Data Engineers, Agile Practitioners, Machine Learning Operators and Engineers, Data Scientists, Privacy Lawyers, and all people of the world affected by the radical economic and workplace transformation that is revolutionizing our thinking resist the rise of data myopia and declare:

1. Design and implement models in accordance to as well as stay informed on the latest thinking regarding the ethical use of artificial intelligence 
2. Encourage and foster direct discussions between model designer and model user  
3. Reduce intermediaries and hierarchies so that information can flow freely and with minimal distortions to the modeling teams 
4. Explore rigorously and communicate the model evaluation metrics transparently and not rely on beneficial metrics to advertise success 
5. Champion to avoid bias in data, opinion, and design through balanced staffing and data collection strategies 
6. Exercise great care in the preparation and collection of both label and feature data 
7. Communicate openly, transparently, and repeatedly the model mechanics so that anyone who wishes to understand the logic will be able to do so 
8. For every model deployed, establish a model operations plan to monitor performance and data consistency, quality, completeness, and structural changes 
9. Use documentation effectively to facilitate communication of model assumptions, input, key algorithm design aspects, and mechanics  
10. Strive to collect sufficiently accurate, consistent, clean, and definitive labels and maintain a label update process 
11. Establish a positive error culture in order to fail early and often and lead by example by admitting to mistakes and staying open to critique 
12. Discourage building extensive central ivory tower data science teams  
13. Recognize that excellent data management and especially documentation and quality of data are essential for trusted data inputs driving models that people feel confident of using 
14. Encourage and help to establish AI acceptance before deployment rather than relying on passive tolerance or rejection, especially for models that rely on feedback loops 
15. Write data input quality checks and integrate this step in the model design process and as far as possible into model evaluation 
16. Work towards reduction of bias against AI results and include AI as a dimension of diversity and inclusion for organizations 
17. Establish cross-functional teams that involve as many people as possible using the model results in the engineering process to reduce fear and rejection 
18. Provide tangible perspectives of model operations, maintenance, and parameter improvement to people whose present work will change because of AI deployments  
19. Listen to advice from model and data engineers that have been employed irrespective of their social, ethical, professional, or corporate position 
20. Always stay informed and attuned to technical aspects of the work 
21. Support the industry in the creation of appropriate design, operations, control, and monitoring procedures and frameworks 
22. Recognize that artificial intelligence is affecting the core of what defines human nature and thus the special responsibility for care to affected parties  
23. Strive to avoid cross-functional team specialization and teach the essence of data and model engineering so that every team member can contribute meaningfully 
24. Prevent that unrealistic expectations for AI/ML model speed and accuracy are established 
25. Do not try to break the laws of physics or mathematics through the use of machine learning 
26. Complement AI/ML with appropriate statistical analysis both pre- and post-modeling; it’s not a competition but a symbiotic relationship 
27. Allow models to learn and improve over time and recognize that the learning process may require potentially extensive human activity observation before models perform in the desired fashion, especially for such models with a feedback loop/human-in-the-loop/collaborative filtering  
28. Evaluate models against data issues before deployment 
29. Celebrate the engineers as the heroes of data, understanding their critical role and heed their recommendations and advice 
30. Celebrate the non-technical professions in the data field as the heroes in their domains 
31. Never play engineers and non-technical professions against each other but recognize that good models need collaboration between several disciplines 
32. Document and keep updated the details of data elements such as attributes, code meaning, time and source as well as legal permissions always as close to the data record so that it is evident during data exploration 
33. Create nimble machine learning structures with many happy AI-enabled teams across the organization 
34. Establish recurring procedures to keep data, models, and the environment they operate in well-aligned 
35. Design AI/ML models in such a way that they can be asked to unlearn and forget if this should become necessary  
36. Keep track of model, data, and configuration versions used for training and deployment 
37. Establish the legally permissible use of each data element at the point of data collection and, in the event of secondary data, get a legal opinion on how the input data can be used 
38. Stay curious, learn and accept the fact that experiences are biased by the events that people have witnessed by chance during the course of their life 
39. Leverage existing role definitions from agile and technical domains rather than creating firms specific niche roles that make it harder for everyone to navigate the many responsibilities in the field of AI 
40. Help your leadership team to establish serving leader incentives 
41. Build a business case and revenue/cost model for your initiative 
42. Do not solve mathematical problems using machine learning models 

Written in recognition of the Clue Train Manifesto with full references published in the free book Unmanage. The story does not end here. Please share your views or show support for the manifesto.