[et_pb_section fb_built=\"1\u2033 admin_label=\"section\" _builder_version=\"4.23\u2033 background_color=\"#000000\u2033 custom_padding=\"0px||||false|false\" global_colors_info=\"{}\"][et_pb_row _builder_version=\"4.27.4\u2033 _module_preset=\"default\" width=\"100%\" custom_padding=\"||0px||false|false\" global_colors_info=\"{}\"][et_pb_column type=\"4_4\u2033 _builder_version=\"4.23\u2033 _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_text _builder_version=\"4.27.4\u2033 _module_preset=\"default\" global_colors_info=\"{}\"]<\/p>\n
About the ethics behind AI<\/em><\/p>\n By Peter Reddering<\/span><\/strong>, Domain Lead Data Strategy @ Cmotions, Katinka Knops<\/span><\/strong>, \u2013 Ethicus @ Digital Ethics \u2013 advice on ethics and digitalization & Jurriaan Nagelkerke<\/span><\/strong>, Data Science & AI Tech Lead @ Cmotions<\/em>[\/et_pb_text][et_pb_text admin_label=\u201dTekst\u201d _builder_version=\u201d4.27.4\u2033 _module_preset=\u201ddefault\u201d global_colors_info=\u201d{}\u201d]<\/p>\n Developments around AI are moving at a rapid pace and the promises are immense. Data is indeed \u2018the new gold\u2019, so what could be more logical than having data scientists dig into the data and start mining? Yet the real questions always seem to be not only technological, but also ethical: what do we consider desirable, who will experience the consequences, and which values are important?<\/p>\n In this article, we show that this ethical perspective is not a side issue, but a thread that runs through the development of every AI application. Technology is not neutral and when developing it, you constantly make choices. Therefore, you weave that thread from the very beginning. That starts with the right dialogue.<\/p>\n <\/p>\n [\/et_pb_text][et_pb_text _builder_version=\"4.27.4\u2033 _module_preset=\"default\" custom_padding=\"||0px|||\" global_colors_info=\"{}\"]<\/p>\n Anyone who develops an AI application makes decisions from day one that determine how the application behaves and who experiences the consequences of that, whether intended or unintended. Think of:<\/p>\n Each of these choices sounds technical, but at its core it is also ethical: with each choice you weigh values and decide which values take priority. Employees have to work with the results, customers have to be able to interact with a chatbot, decisions are made that have an impact on citizens. Our practice shows that the most important choices are not about the technology itself, but about the organization: what goals are you aiming for, which human considerations do you automate, and which values are so important that you want to ensure they are included in your design? Scoring with AI therefore does not start with the technology. It starts with the question of what you as an organization want to stand for.<\/p>\n <\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.5em” width=”100%” max_width=”100%” min_height=”191px” height=”100px” max_height=”100px” custom_margin=”-35px||||false|false” custom_padding=”10px|0px||21px|false|false” border_color_all=”RGBA(255,255,255,0)” box_shadow_style=”preset1″ box_shadow_horizontal=”1px” box_shadow_vertical=”0px” box_shadow_blur=”34px” box_shadow_spread=”1px” box_shadow_color=”#47B2A8″ global_colors_info=”{}”]<\/p>\n From the practice \u2014 No more calling<\/strong><\/p>\n A government organization in the healthcare sector assesses applications for care or care budgets. An important step in the process: a phone call with the applicant to check that everything is complete and that the client understands the implications of the application on their personal situation. To save scarce resources, it is being investigated whether this can be automated. Smart technology will soon check the completeness and request missing information digitally. Efficient! But\u2026 with this, the only personal contact moment may also disappear. Is that desirable \u2013 for the organization, the employees, and the client? Or is the emphasis on efficiency quietly sacrificing other values here?<\/em><\/p>\n [\/et_pb_text][et_pb_text admin_label=”Tekst” _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”54px||||false|false” global_colors_info=”{}”]<\/p>\n Anyone who thinks they can escape ethical considerations by relying on the neutrality of technology is deluded. An algorithm consistently and without complaint calculates results, and therefore the results appear objective. But it is calculated based on data that people have collected, with criteria that people have devised, towards goals that people have chosen. In short, there is surprisingly much human \u2014 and therefore surprisingly much morality \u2014 in that \u2018neutral\u2019 machine. Train a model on the past and it consistently reproduces that past, including the skewed perspective that was present in it. An algorithm that labels a particular postcode as \u2018risk\u2019 is not visionary. It is a mirror with a selective memory.<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.5em” width=”100%” max_width=”100%” min_height=”191px” height=”100px” max_height=”100px” custom_margin=”31px||||false|false” custom_padding=”10px|0px||21px|false|false” border_color_all=”RGBA(255,255,255,0)” box_shadow_style=”preset1″ box_shadow_horizontal=”1px” box_shadow_vertical=”0px” box_shadow_blur=”34px” box_shadow_spread=”1px” box_shadow_color=”#47B2A8″ global_colors_info=”{}”]<\/p>\n From practice \u2014 the diligent parrot<\/strong><\/p>\n A youth care organization is developing predictive models to estimate risks of problematic behavior, trained on historical records. The model neatly distinguishes between high and low risk. Until in-depth analysis reveals that young people with a certain cultural background have simply been reported and followed more frequently in the past \u2013 possibly due to differences in interpretation or biases. The model dutifully learns: that group = higher risk. Even without ancestry as a variable, postcode and school type sneak in as indirect indicators. Thus the algorithm becomes a diligent parrot of old patterns, with self-fulfilling prophecy as a risk. Technically, it does exactly what is asked of it. That is precisely the problem.<\/em><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”50px||||false|false” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\n So if we can\u2019t simply rely on the neutrality of the technology, how can we make the right choices in design? Perhaps a list of criteria? In our conversations and sessions with clients, we keep seeing this need for checklists and frameworks. Understandable: A checked list gives a sense of control. \u2018Look, I\u2019ve gone through all the criteria, so I can justify myself.\u2019<\/em> The problem is that ethics cannot be reduced to neat categories: the truly relevant questions are rarely included on a standard list, because they depend on the specific application and context.<\/p>\n What is innocent in one situation (an algorithm that suggests outfits based on the contents of your wardrobe) is not the same in the other (an algorithm that decides on your loan based on gender, age, and postcode). The ethical perspective is therefore not a form that you fill out once and archive it; it is a continuous dialogue about values and the consequences of your choices: who benefits from this application, who might be harmed by it, and can we accept those consequences together? In ethical issues, there is rarely a black-and-white \u2018good\u2019 or \u2018bad\u2019; it is about a balanced argument that supports choices. It is precisely in that balance \u2013 and the transparency thereof \u2013 that careful accountability lies. Not in a standard form filled out for 100%.<\/p>\n <\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”-15px||||false|false” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\n Those who prefer to ignore the conversation about values have one more trump card: the law. \u2018We act 100% ethically because we remain completely within the legal framework.\u2019 A reassuring thought, but ethics and legislation are not the same thing. In fact, ethics precedes legislation. In the law, we establish what we have agreed upon as a society after a social and moral debate. By doing so, legislation is by definition behind the reality, especially in a technology that changes much faster than an average legislative process! Moreover, something that is legally permitted cannot necessarily be a good idea that fits the values of your organization and stakeholders.<\/p>\n <\/p>\n <\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”-56px||||false|false” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\n The common thread is clear: the ethical perspective is present in every step\u2014in the choices you make, in the technology you build, and in how you justify your actions. Deploying AI responsibly therefore begins not with an algorithm, but with the right people at the table and a few \u201cawkward questions.\u201d Not much glamour, but a lot of impact. This naturally raises practical questions: how do you organize that conversation and who do you involve? How do you document the tradeoffs? And how do you structurally embed ethics within the organization so that it doesn\u2019t remain a one-time exercise but becomes a constant part of how you work with AI\u2014without it becoming, you guessed it, a checklist? In the follow-up to this article, we will further explore these questions!<\/p>\n [\/et_pb_text][et_pb_blurb _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” width=”100%” custom_padding=”||0px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.23″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” width=”100%” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\n Cmotions helps organizations use AI smartly and responsibly in their development process. Do you have questions about agentic working, governance around AI tooling, or scaling up Claude Code within your team? Feel free to contact us.<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section].<\/p>","protected":false},"excerpt":{"rendered":" AI offers organizations plenty of opportunities, but also raises important ethical questions. How do you ensure that AI applications are developed and deployed responsibly? And why aren\u2019t legislation and checklists enough?<\/p>\n In this article, you will discover why AI ethics must be part of the development of AI from the very beginning. Using practical examples, we show how human choices, values, and biases influence algorithms. We also discuss why a continuous dialogue about the consequences of AI is essential for responsible use within organizations.<\/p>","protected":false},"author":17,"featured_media":11696,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[82,51,24,25,76,48],"tags":[],"class_list":["post-11686","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-article","category-artikel","category-blog","category-cmotions","category-data-science-ai","category-data-strategy"],"_links":{"self":[{"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/posts\/11686","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/comments?post=11686"}],"version-history":[{"count":7,"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/posts\/11686\/revisions"}],"predecessor-version":[{"id":11697,"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/posts\/11686\/revisions\/11697"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/media\/11696"}],"wp:attachment":[{"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/media?parent=11686"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/categories?post=11686"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cmotions.com\/en\/wp-json\/wp\/v2\/tags?post=11686"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}The choices you make no matter what<\/span><\/h2>\n
\n
Neutral? <\/strong><\/h2>\n
The checklist desire<\/span><\/h2>\n
But shouldn\u2019t it be allowed by law?<\/strong><\/h2>\n
How then?<\/strong><\/h2>\n
Learn more<\/h2>\n