They had it coming, they had it coming all along.\u00a0 And the part of the song I found interesting was the reframing of their violent actions through their moral lens: \u201cIt was a murder, but not a crime.<\/em>\u201d<\/p>\n In short, the musical tells a story of greed, murder, injustice<\/a>, and Blame-shifting<\/a> plots<\/strong> that unfold in a world where truth is manipulated by media, clever lawyers, and public fascination with scandal.<\/p>\n By being only an observer in the audience, it is easy to fall for their stories portrayed through the victim\u2019s eyes, who was merely responding to intolerable situations.<\/p>\n Logically, there is a scientific explanation for why blame-shifting feels satisfying. Attributing negative events to external causes (other people or situations) activates brain regions associated with reward processing<\/a>. If it feels good, it reinforces the behaviour and makes it more automatic.<\/p>\n This interesting play of blame-shifting is in the theatre of life now, where humans can also start calling out the tools powered by LLMs<\/strong> for poor decisions and life outcomes. Probably pulling out the argument of\u2026<\/p>\n Knowing how the differences (artistic or not) lead us to justify our bad acts and shift blame to others, it\u2019s only common sense to assume we will do the same to AI and the models behind it.\u00a0<\/p>\n When searching for a responsible party for AI-related failures, one paper, \u201cIt\u2019s the AI\u2019s fault, not mine<\/em><\/a>,<\/em>\u201d reveals a pattern in how humans attribute blame depending on who\u2019s involved and how they\u2019re involved.\u00a0<\/p>\n The research explored two key questions:\u00a0<\/p>\n Through three studies conducted in early 2022, before the \u201cofficial\u201d start of the generative AI era through UI, the research examined how humans distribute blame when AI systems commit moral transgressions, such as displaying racial bias, exposing children to inappropriate content, or unfairly distributing medical resources and found the following:<\/p>\n When AI was portrayed with more human-like mental capacities, participants were more willing to point fingers at the AI system for moral failures.<\/p>\n<\/blockquote>\n Other findings were that not all human agents got off the hook equally:\u00a0<\/p>\n And probably the most important discovery:\u00a0<\/p>\n Across all scenarios, AI consistently received a smaller percentage of blame compared to human agents, and the AI programmer or the AI team <\/strong>shouldered the heaviest blame<\/strong>\u00a0burden.<\/p>\n<\/blockquote>\n How were these findings explained?\u00a0<\/p>\n The research suggested it\u2019s about perceived roles and structural clarity:<\/strong><\/p>\n This \u201cmoral scapegoating<\/a>\u201d suggests corporate accountability might increasingly dissolve as AI systems appear more autonomous and human-like.<\/p>\n You would now say, this is\u2026\u00a0<\/p>\n Scapegoating and blaming others occur when the stakes are high, and the media usually likes to position a big headline, with the villain:\u00a0<\/p>\n From all these titles, you can instantly blame the end-user or developer because of a lack of knowledge of how the new tools (yes, tools<\/a>!) are built and how they should be used, implemented or tested, but none of this helps when the damage is already done, and someone needs to be held accountable for it.<\/p>\n Talking about accountability, I can\u2019t skip the EU AI Act<\/a> now, and its regulatory framework that is placing on the hook AI providers<\/em>, deployers<\/em> and importers<\/em><\/a>, by stating how:\u00a0<\/p>\n \u201cUsers (deployers) of high-risk AI systems have some obligations, though less than providers (developers).\u201d<\/p>\n<\/blockquote>\n So, among others, the Act explains different classes of AI systems<\/a> and categorises high-risk AI systems<\/em><\/strong> as those used in critical areas like hiring, essential services, law enforcement, migration, justice administration, and democratic processes.<\/p>\n For these systems, providers<\/em> must implement a risk-management system<\/strong><\/a> that identifies, analyses, and mitigates risks throughout the AI system\u2019s lifecycle.\u00a0<\/p>\n This extends into a mandatory quality management system<\/strong><\/a> covering regulatory compliance, design processes, development practices, testing procedures, data management, and post-market monitoring. It must include \u201can accountability framework setting out the responsibilities of management and other staff.<\/em><\/a>\u201d<\/p>\n On the other side, deployers<\/em> of high-risk AI systems<\/a> need to implement appropriate technical measures, ensure human oversight, monitor system performance, and, in certain cases, conduct fundamental rights impact assessments<\/a>.\u00a0<\/p>\n To sweeten this up, penalties for non-compliance<\/a> can result in a fine of up to \u20ac35 million or 7% of global annual turnover.<\/p>\n<\/blockquote>\n Maybe you now think, \u201cI\u2019m off the hook\u2026I am only the end-user, and all this is none of my concern<\/em>\u201d, but let me remind you of the already existing headlines above, where no lawer could razzle dazzle<\/a> a judge into believing innocent for leveraging AI in a work situation that seriously affected other parties.\u00a0<\/p>\n Now that we\u2019ve clarified this, let\u2019s discuss how everyone can contribute to the AI responsibility circle.<\/p>\n True responsibility in the AI pipeline requires personal commitment from everyone involved, and with this, the best you can do is:<\/p>\n Finally, I recommend familiarising yourself with the regulations<\/strong> to understand your rights alongside responsibilities. No framework can change the fact that AI decisions carry human fingerprints and that humans will consider other humans, not the tools, responsible for AI mistakes.<\/p>\n Unlike the fictional murderesses of the Chicago musical who danced their way through blame, in real AI failures, the evidence trail won\u2019t disappear with a smart lawyer and superficial story.\u00a0<\/p>\n\n
I didn\u2019t do it.\u00a0
But if I\u2019d done it, how could you tell me that I was wrong?<\/p>\n<\/blockquote>\nArtistic differences<\/a><\/h3>\n
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All that\u00a0jazz<\/a><\/h3>\n
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When you\u2019re good to AI, AI\u2019s good to\u00a0you<\/a><\/h3>\n
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