{"id":25045,"date":"2018-03-12T00:00:00","date_gmt":"2024-09-13T08:48:11","guid":{"rendered":"https:\/\/wordpress-583806-4798031.cloudwaysapps.com\/leveled-up-auditability-of-ai-and-machine-learning\/"},"modified":"2024-11-15T16:22:01","modified_gmt":"2024-11-15T15:22:01","slug":"leveled-up-auditability-of-ai-and-machine-learning","status":"publish","type":"post","link":"https:\/\/informator.se\/en\/leveled-up-auditability-of-ai-and-machine-learning\/","title":{"rendered":"Leveled up: Auditability of AI and Machine Learning"},"content":{"rendered":"<span style=\"vertical-align: inherit;\"><span style=\"vertical-align: inherit;\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<div dir=\"ltr\" style=\"text-align: left;\">\n<p style=\"text-align: left;\"><span style=\"font-size: large;\">Architects and many others remember the tightrope walk between flexibility\/performance and testability\/predictability\/V&amp;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\u2019s leveled up by ML (machine learning). Essentially the same tradeoff, but growing broader and trickier.\u00a0<\/span><\/p>\n<h3 style=\"text-align: left;\"><b>Black box\u00a0<\/b><\/h3>\n<p>ML stirs up the fire; despite its roots (rule <a href=\"http:\/\/www.telegraph.co.uk\/news\/obituaries\/1556846\/Professor-Donald-Michie.html\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color: #0433ff;\">induction<\/span><\/a> and mining) in the successful <i>decryption<\/i> of an unbreakable cipher, its near future looks <i>encoded <\/i>in weight values <i>somewhere <\/i>in deep neural networks. Whereas black-box flight recorders <i>clarified<\/i> the chain of events &amp; decisions in past emergencies, more and more IT is now landing in black boxes that <i>hide <\/i>opaque\u00a0logic.<\/p>\n<p style=\"text-align: left;\">Predictability wasn\u2019t 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)\u2026 If you just say \u201cskis this wide apart look amusing\u201d, I guess you\u2019re in <i>consumer IT<\/i>, 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\u2019re in <i>corporate<\/i> <i>(image: Ski Robot Challenge, Korea).\u00a0<\/i><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/1.bp.blogspot.com\/-IQZDiUnPVcQ\/WqaD2LaDKMI\/AAAAAAAAA-c\/fa7xUW06VHMzi9oYjpvW1kCYxuCcQxSiwCLcBGAs\/s400\/Ski%2BRobot%2Bposter.jpg\" \/><\/p>\n<h3 style=\"text-align: left;\"><b>Tackle it one-way or two-way<\/b><\/h3>\n<p>Business apps are very different from apps for billions of consumers (see slides 9 to 15 in <a href=\"https:\/\/sics.box.com\/s\/7wi0c16b6d5zjgysmjx869pt99uks3cm\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color: #0433ff;\">this<\/span><\/a> talk by Oracle\u2019s VP at SICS). Your enterprise or team can tackle the leveled-up tradeoff both <i>top-down<\/i> and <i>bottom-up<\/i>:<\/p>\n<ul>\n<li style=\"font-stretch: normal; line-height: normal; margin: 0px 0px 10px;\"><span style=\"font-family: inherit; font-size: small; font-weight: normal;\">assuring a framework of corporate <i>values and procedures<\/i> (particularly transparency, governance &amp; compliance, accountability, and a security &amp; safety culture)<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-stretch: normal; line-height: normal; margin: 0px 0px 10px;\"><span style=\"font-family: inherit; font-size: small; font-weight: normal;\">applying appropriate <i>technologies and practices<\/i> in IT to build in mechanisms upfront\u00a0 for auditability, comprehensibility, predictability, traceability, testability\/V&amp;V (as well as fraud-prevention, such as restricted access to learning-data sets). \u00a0 \u00a0 \u00a0\u00a0<\/span><\/li>\n<\/ul>\n<p>On the latter (bottom-up) part, there\u2019s ongoing AI research to \u201c<i>unpack<\/i>\u201d the opaque logic buried within deep learning systems, and to give them an ability to <i>explain<\/i> themselves. DARPA\u2019s 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.<\/p>\n<p><i>Hybrid-AI <\/i>tech vendors often address organizations with more constrained schedules, budgets and levels of AI expertise. Hybrid learning systems combine \u201csubsymbolic\u201d 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.)<\/p>\n<p style=\"text-align: left;\">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.<\/p>\n<p>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 \u201ctip feeling\u201d as to where ML can work (or can\u2019t).<\/p>\n<p><b style=\"color: inherit; font-size: 1.56em;\">Models, animations, transparency<\/b><\/p>\n<p>Once the opaque logic is unpacked, or expressed as rules or trees, it\u2019s time to revive your team\u2019s modeling\u00a0skills. 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.<\/p>\n<\/div>\n<\/div><\/div>\n<\/div><\/div>\n<\/div><\/div>\n<\/div><\/div>\n<\/span><\/span>\r\n<h4><span style=\"vertical-align: inherit;\"><span style=\"vertical-align: inherit;\">Milan Kratochvil\u00a0<\/span><\/span><\/h4>\r\n<span style=\"vertical-align: inherit;\"><span style=\"vertical-align: inherit;\"><div style=\"font-size: 12.800000190734863px;\">\n<div style=\"text-align: left;\"><i>Trainer at Informator, senior modeling and architecture consultant at\u00a0<a href=\"http:\/\/www.kiseldalen.com\/\"><span style=\"color: #0433ff;\">Kiseldalen.com<\/span><\/a>, main author : UML Extra Light (Cambridge University Press) and Growing Modular (Springer), Advanced UML2 Professional (<a href=\"http:\/\/www.omg.org\/cgi-bin\/searchcert.cgi\"><span style=\"color: #0433ff;\">OCUP<\/span><\/a>\u00a0cert level 3\/3).<\/i><i style=\"text-align: left;\">Milan and Informator collaborate since 1996 on architecture, modelling, UML, requirements, and design.\u00a0<\/i><\/div>\n<\/div>\n&nbsp;<\/span><\/span>","protected":false},"excerpt":{"rendered":"<p>Architects and many others remember the tightrope walk between flexibility\/performance and testability\/predictability\/V&#038;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\u2019s leveled up by ML (machine learning).<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[571,570,922],"tags":[594,592],"class_list":["post-25045","post","type-post","status-publish","format-standard","hentry","category-ai","category-blogg","category-blogi","tag-arkitektur","tag-machine-learning"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.0.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"Architects and many others remember the tightrope walk between flexibility\/performance and testability\/predictability\/V&amp;V in systems with many run-time parameters, or parallelism, or late binding time ranging from polymorphism to SOA-UDDI and ad-hoc computing. 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