Leveled up: Auditability of AI and Machine Learning
Architects and many others remember the tightrope walk between flexibility/performance and testability/predictability/V&V in systems with many run-time parameters, or parallelism, or late binding time ranging from polymorphism to SOA-UDDI and ad-hoc computing. Now, it’s leveled up by ML (machine learning). Essentially the same tradeoff, but growing broader and trickier.
ML stirs up the fire; despite its roots (rule induction and mining) in the successful decryption of an unbreakable cipher, its near future looks encoded in weight values somewhere in deep neural networks. Whereas black-box flight recorders clarified the chain of events & decisions in past emergencies, more and more IT is now landing in black boxes that hide opaque logic.
Predictability wasn’t a big deal in consumer IT and entertainment (when Youtube or Spotify wrongly offered you a title you were avoiding like the plague, you rarely asked why)… If you just say “skis this wide apart look amusing”, I guess you’re in consumer IT, but if you insist on a layer-by-layer explanation why most artificial vision systems have a hard time in strong sunshine on white slopes, I bet you’re in corporate (image: Ski Robot Challenge, Korea).
Tackle it one-way or two-way
Business apps are very different from apps for billions of consumers (see slides 9 to 15 in this talk by Oracle’s VP at SICS). Your enterprise or team can tackle the leveled-up tradeoff both top-down and bottom-up:
- assuring a framework of corporate values and procedures (particularly transparency, governance & compliance, accountability, and a security & safety culture)
- applying appropriate technologies and practices in IT to build in mechanisms upfront for auditability, comprehensibility, predictability, traceability, testability/V&V (as well as fraud-prevention, such as restricted access to learning-data sets).
On the latter (bottom-up) part, there’s ongoing AI research to “unpack” the opaque logic buried within deep learning systems, and to give them an ability to explain themselves. DARPA’s Explainable AI Program, XAI , aims at ML techniques (new or improved) that produce more explainable models, while maintaining a high level of prediction accuracy. New machine-learning systems will have the ability to explain their rationale, strengths, weaknesses, etc.
Hybrid-AI tech vendors often address organizations with more constrained schedules, budgets and levels of AI expertise. Hybrid learning systems combine “subsymbolic” ML with transparent symbolic computation (typically, wellknown knowledge-processing techniques). The combination lowers the total cost of entry into AI and ML, because it evolves from logic that domain experts already know (rules, decision trees, etc.)
From there, hybrid systems employ ML iteratively to fine-tune this explicit logic: for example, to narrow the IF-part of a rule to factors that prove most significant. That is, results of ML from big data decide about variables to be included (or omitted), about intervalization of a continuum of values, or about relevant threshold values of a particular variable.
Notably, a rule is still expressed as a rule yet with an ever-smarter and more accurate IF-part. This is transparent to humans, and paves the way to embedding AI and ML into daily IT-dev practice: devs and architects will gradually find thousands of decision points, enterprise-wide, suited for small AI apps in daily business. Those will generate valuable skills, know-how, and “tip feeling” as to where ML can work (or can’t).
Models, animations, transparency
Once the opaque logic is unpacked, or expressed as rules or trees, it’s time to revive your team’s modeling skills. Long story short, a decision tree (or an invocation path through a rule base) is an excellent input to animations or test executions of different scenarios, to make them transparent even to stakeholders and non-IT roles. That story is worth another blog post, later this spring.