{"id":11106,"date":"2019-10-10T10:00:00","date_gmt":"2019-10-10T10:00:00","guid":{"rendered":"https:\/\/zapliance.com\/?p=11106"},"modified":"2022-08-26T13:24:24","modified_gmt":"2022-08-26T13:24:24","slug":"3-steps-for-the-compliance-assessment-of-ai-based-decisions","status":"publish","type":"post","link":"https:\/\/zapliance.com\/en\/blog\/3-steps-for-the-compliance-assessment-of-ai-based-decisions\/","title":{"rendered":"3 steps for the compliance assessment of AI-based decisions"},"content":{"rendered":"\n<p>Remember the three \u201cWhat can go wrong?\u201d phenomena from our&nbsp;<a href=\"https:\/\/zapliance.com\/blog\/artificial-intelligence-and-compliance-how-does-it-all-fit-together?lang=en\" target=\"_blank\" rel=\"noreferrer noopener\">last blog article<\/a>?<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Rule Blackbox<\/li><li>Out-of-Context Bias<\/li><li>Feedback Loop Bias<\/li><\/ul>\n\n\n\n<p>In this article, we will use these phenomena to derive a procedure for a compliance-oriented assessment of AI-based decisions in companies.<\/p>\n\n\n\n<p>But before we get started, we need to make a distinction:<\/p>\n\n\n\n<p>When is AI used in a company at all? Various terms in circulation such as artificial intelligence, data mining, machine learning or data science make it difficult to distinguish between them. However, it makes no sense to use these terms or even the methods used to define AI in a company.<\/p>\n\n\n\n<p>This is why we want to talk about the use of AI in a company when decisions in the company are automatically made by machines, initially independent of method and technology. In this respect, the question that should be addressed is that of the compliance of automatic decisions in the company.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Step 1: AI inventory<\/h2>\n\n\n\n<p>The first step for a compliance-oriented assessment of AI in a company is the AI inventory. This is simply a list of AI applications used in the enterprise. One can speak of an application of AI in a company if the following criteria are fulfilled:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li><strong>Autonomy<\/strong>: The application decides&nbsp;<em>autonomously<\/em>&nbsp;by machine or supports a human decision considerably, so that at least one essential mechanical influence is present.<\/li><li><strong>Economic relevance<\/strong>: The decision is not only a decision for the technically correct handling of business transactions, but the decision has&nbsp;<em>economic \/ entrepreneurial relevance or influence on the organization<\/em>.<\/li><li><strong>Learning<\/strong>: The method of automatic decision making is not only based on static rules (\u201cif-then-else concatenations\u201d), but the decision calculation was first learned through the processing of training data&nbsp;<em>by the machine<\/em>.<\/li><li><strong>Possibility of dynamic adaptation<\/strong>: The way in which decisions are made could be adapted on a regular basis, since new data is constantly being added for an&nbsp;<em>extended training<\/em>&nbsp;of the algorithm used.<\/li><\/ol>\n\n\n\n<p>If an application meets the four criteria, include them in your AI inventory. For each application, the inventory of the following characteristics makes sense:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>Name of the AI application<\/li><li>Technical responsibility (area\/department)<\/li><li>Description of the economic decision taken \/ supported by the AI.<\/li><li>Classification: AI influences a&nbsp;<em>customer relationship<\/em>&nbsp;or influences&nbsp;<em>internal processes<\/em>.<\/li><\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Step 2: AI Risk Scoping<\/h2>\n\n\n\n<p>In the second step, you build on your created AI inventory and grade the AI applications in accordance with a risk assessment. Use a simple scheme for a risk classification of the AI application, e.g.: low, medium, high risk. Think in terms of scenarios of what the business or legal consequences could be if the AI failed or produced misjudgments. AI applications that affect a customer relationship should tend to be riskier. The following information could be noted for each application in your AI inventory:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>The selected risk assessment<\/li><li>Scenarios of consequences if the AI would make misjudgments<\/li><li>Description of influences on key figures used by AI within the company<\/li><\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Step 3: AI Compliance Assessment<\/h2>\n\n\n\n<p>In the third step, each AI application undergoes a detailed evaluation. Within the framework of the proposed procedure, recourse is made to the described \u201cWhat can go wrong?\u201d phenomena: the rule black box, out-of-context bias and feedback loop bias. According to the assessment, one should know whether an AI application poses a risk of non-compliance with regard to the three \u201cWhat can go wrong?\u201d phenomena.<\/p>\n\n\n\n<p>The compliance manager should first ensure that he or she is aware of the regulatory framework in order to understand which legal requirements the use of AI in the company may come into conflict with. Two examples are given here \u2013 without claiming to be exhaustive:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>Section 1 of&nbsp;<strong>Germany\u2019s General Act on Equal Treatment<\/strong>&nbsp;(<em>Allgemeines Gleichbehandlungsgesetz \u2013 AGG<\/em>) describes the characteristics that are important for equal treatment: \u201cThe purpose of this Act is to prevent or to stop discrimination on the grounds of race or ethnic origin, gender, religion or belief, disability, age or sexual orientation\u201d. These features can \u2013 amongst other things \u2013 also be used in the training data for an AI system (e.g. in the automatic assessment of applicants for a job). This raises the question: Does the use of AI result in discrimination or can the characteristics be used without any need to worry?<\/li><li>The&nbsp;<strong>General Data Protection Regulation (GDPR)<\/strong>: According to Art. 13 and 14 of the GDPR, the processing of personal data is subject to a strict duty to provide information to those affected. This also includes the purpose and legal basis of the processing. Personal characteristics can be found in the training data of an AI. The question raised is thus this: Is the agreed purpose of use to be interpreted to the extent that the use of personal data for training an AI is permitted?<\/li><\/ol>\n\n\n\n<p>Now the \u201cWhat can go wrong?\u201d phenomena and the regulatory framework can be combined. Each AI application in the AI inventory should now be clarified. It is recommended to assess the three areas&nbsp;<em>context<\/em>,&nbsp;<em>method<\/em>&nbsp;and&nbsp;<em>data<\/em>&nbsp;of the AI application.<\/p>\n\n\n\n<p>Follow the questions below for each AI application.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Context<\/h3>\n\n\n\n<ul class=\"wp-block-list\"><li>Is the context during the development \/ training of the AI comparable to the context during the application \/ operation of the AI? If no:&nbsp;<em>Suspicion of out-of-context bias.<\/em><\/li><li>Do the results of the AI go directly or indirectly (e.g. by conclusion for an action) back into the AI as input data (e.g. time-shifted)? If yes:&nbsp;<em>Suspicion of feedback loop bias<\/em><\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">2. Method<\/h3>\n\n\n\n<p>The clarification of the AI method or technology aims to assess the \u201cexplainability\u201d of the generated AI decision by a human being.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>To what extent can one tell from the AI how great the effect of a characteristic is? If it is difficult to make statements on this:&nbsp;<em>Suspicion of rule black box<\/em><\/li><li>To what extent can one tell from the AI how large the interaction between two characteristics is? If it is difficult to make statements on this:&nbsp;<em>Suspicion of rule black box<\/em><\/li><li>Can one infer from the AI whether a characteristic is to be assessed as (statistically) significant? If it is difficult to make statements on this :&nbsp;<em>Suspicion of rule black box<\/em><\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3. Data<\/h3>\n\n\n\n<p>The training data determines the behavior of the AI.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Does the AI use features that could lead to discrimination (derived from the regulatory context)? If so, further questions:<ul><li>Is the critical characteristic expressed in the same proportion as in an appropriate comparative population (e.g. women may be under-represented in training data)? If yes:&nbsp;<em>Suspicion of rule black box<\/em><\/li><li>Does the AI application make the same decision if the critical characteristics are removed from the training data? If yes: Further detailed analysis necessary, if necessary&nbsp;<em>suspicion of rule black box<\/em><\/li><\/ul><\/li><li>Are personal characteristics used in the training data? If yes: Inventory in order to be able to provide information according to the GDPR.<\/li><li>What is the quality of data like? Pay particular attention to the frequency of missing values, i.e. incomplete data records (e.g. unknown age). Missing values\u201d are often filled with a \u201cBest Guess\u201d before being processed in an AI (so-called statistical imputation). Such a \u201cBest Guess\u201d represents what can be statistically expected and suppresses unknown but existing diversity. In the case of extensive \u201cmissing values\u201d, especially in the case of critical characteristics with regard to discrimination, the following applies:&nbsp;<em>Suspicion of rule black box<\/em><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>The article has pointed out some possible problems with the use of AI in companies and illustrated them with some clear cases and examples. Based on these findings, we have proposed a 3-step procedure model for the compliance-oriented evaluation of AI applications. The emphasis was placed on consistent application of the procedure model in practice.<\/p>\n\n\n\n<p>Finally, the following two sources are worth mentioning as sources of assistance when assessing the compliance of AI within companies:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>AI Fairness 360 Open Source Toolkit from IBM (<a href=\"http:\/\/aif360.mybluemix.net\/\" rel=\"noreferrer noopener\" target=\"_blank\">http:\/\/aif360.mybluemix.net<\/a>)<\/li><li>Gender Shades Project Bias in AI (<a href=\"http:\/\/gendershades.org\/\" rel=\"noreferrer noopener\" target=\"_blank\">http:\/\/gendershades.org<\/a>)<\/li><\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Remember the three \u201cWhat can go wrong?\u201d phenomena from our&nbsp;last blog article? Rule Blackbox Out-of-Context Bias Feedback Loop Bias In this article, we will use these phenomena to derive a procedure for a compliance-oriented assessment of AI-based decisions in companies. But before we get started, we need to make a distinction: When is AI used [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":10699,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[39,37],"tags":[],"class_list":["post-11106","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-en-compliance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>3 steps for the compliance assessment of AI-based decisions - zapliance<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/zapliance.com\/en\/blog\/3-steps-for-the-compliance-assessment-of-ai-based-decisions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"3 steps for the compliance assessment of AI-based decisions - zapliance\" \/>\n<meta property=\"og:description\" content=\"Remember the three \u201cWhat can go wrong?\u201d phenomena from our&nbsp;last blog article? Rule Blackbox Out-of-Context Bias Feedback Loop Bias In this article, we will use these phenomena to derive a procedure for a compliance-oriented assessment of AI-based decisions in companies. 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