SayPro “Extract 100 technical issues common in AI models like SayPro Royalties AI.”

100 Technical Issues Common in AI Models Like SayPro Royalties AI

A. Data-Related Issues

1. Incomplete or missing training data
2. Poor data quality or noisy data
3. Data imbalance affecting model accuracy
4. Incorrect data labeling or annotation errors
5. Outdated data causing model drift
6. Duplicate records in datasets
7. Inconsistent data formats
8. Missing metadata or context
9. Unstructured data handling issues
10. Data leakage between training and test sets

B. Model Training Issues

11. Overfitting to training data
12. Underfitting due to insufficient complexity
13. Poor hyperparameter tuning
14. Long training times or resource exhaustion
15. Inadequate training dataset size
16. Failure to converge during training
17. Incorrect loss function selection
18. Gradient vanishing or exploding
19. Lack of validation during training
20. Inability to handle concept drift

C. Model Deployment Issues

21. Model version mismatch in production
22. Inconsistent model outputs across environments
23. Latency issues during inference
24. Insufficient compute resources for inference
25. Deployment pipeline failures
26. Lack of rollback mechanisms
27. Poor integration with existing systems
28. Failure to scale under load
29. Security vulnerabilities in deployed models
30. Incomplete logging and monitoring

D. Algorithmic and Architectural Issues

31. Choosing inappropriate algorithms for task
32. Insufficient model explainability
33. Lack of interpretability for decisions
34. Inability to handle rare or edge cases
35. Biases embedded in algorithms
36. Failure to incorporate domain knowledge
37. Model brittleness to small input changes
38. Difficulty in updating or fine-tuning models
39. Poor handling of multi-modal data
40. Lack of modularity in model design

E. Data Processing and Feature Engineering

41. Incorrect feature extraction
42. Feature redundancy or irrelevance
43. Failure to normalize or standardize data
44. Poor handling of categorical variables
45. Missing or incorrect feature scaling
46. Inadequate feature selection techniques
47. Failure to capture temporal dependencies
48. Errors in feature transformation logic
49. High dimensionality causing overfitting
50. Lack of automation in feature engineering

F. Evaluation and Testing Issues

51. Insufficient or biased test data
52. Lack of comprehensive evaluation metrics
53. Failure to detect performance degradation
54. Ignoring edge cases in testing
55. Over-reliance on accuracy without context
56. Poor cross-validation techniques
57. Inadequate testing for fairness and bias
58. Lack of real-world scenario testing
59. Ignoring uncertainty and confidence levels
60. Failure to monitor post-deployment performance

G. Security and Privacy Issues

61. Data privacy breaches during training
62. Model inversion or membership inference attacks
63. Insufficient access controls for model endpoints
64. Vulnerability to adversarial attacks
65. Leakage of sensitive information in outputs
66. Unsecured data storage and transmission
67. Lack of compliance with data protection laws
68. Insufficient logging of access and changes
69. Exposure of model internals to unauthorized users
70. Failure to anonymize training data properly

H. Operational and Maintenance Issues

71. Difficulty in model updating and retraining
72. Lack of automated monitoring systems
73. Poor incident response procedures
74. Inadequate documentation of models and pipelines
75. Dependency on outdated libraries or frameworks
76. Lack of backup and recovery plans
77. Poor collaboration between teams
78. Failure to manage model lifecycle effectively
79. Challenges in version control for models and data
80. Inability to track model lineage and provenance

I. Performance and Scalability Issues

81. High inference latency impacting user experience
82. Inability to process large data volumes timely
83. Resource contention in shared environments
84. Lack of horizontal scaling capabilities
85. Inefficient model architecture causing slowdowns
86. Poor caching strategies for repeated queries
87. Bottlenecks in data input/output pipelines
88. Unbalanced load distribution across servers
89. Failure to optimize model size for deployment
90. Lack of real-time processing capabilities

J. User Experience and Trust Issues

91. Lack of transparency in AI decisions
92. User confusion due to inconsistent outputs
93. Difficulty in interpreting AI recommendations
94. Lack of feedback loops from users
95. Over-reliance on AI without human oversight
96. Insufficient error explanations provided
97. Difficulty in correcting AI mistakes
98. Lack of personalized user experiences
99. Failure to communicate AI limitations clearly
100. Insufficient training for users interacting with AI