{"id":6970,"date":"2024-01-17T14:07:43","date_gmt":"2024-01-17T13:07:43","guid":{"rendered":"https:\/\/www.artea.com\/ethics-and-artificial-intelligence-what-is-the-relationship\/"},"modified":"2026-09-29T09:29:55","modified_gmt":"2026-09-29T07:29:55","slug":"ethics-and-artificial-intelligence-what-is-the-relationship","status":"publish","type":"post","link":"https:\/\/www.artea.com\/en\/ethics-and-artificial-intelligence-what-is-the-relationship\/","title":{"rendered":"ETHICS AND ARTIFICIAL INTELLIGENCE: WHAT IS THE RELATIONSHIP?"},"content":{"rendered":"\t\t
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Artificial Intelligence (AI) is proving to have the power to transform our lives and the world we live in. But as a famous comic book hero once said, with great power comes great responsibility\u2014especially ethical responsibility. Reflecting on the relationship between ethics and artificial intelligence<\/strong> is now essential to fully understanding not only the potential but also the limitations and risks of using AI. <\/p>

This article explores three challenges that have become central to the public and scientific debate on AI: privacy, algorithmic discrimination, and the liability of intelligent machines<\/strong>, within the relevant regulatory framework.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The Privacy Dilemma in the Age of AI<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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AI raises fundamental privacy<\/strong> issues, which can take the form of both intentional and accidental breaches. Let\u2019s examine these two scenarios to understand the potential impact of new technologies on the privacy of our personal data. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Face Recognition: A Double-Edged Sword<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Facial recognition<\/strong> is an AI technology that opens the door to deliberate violations of our privacy.<\/p>

While it can improve the safety of our cities<\/strong>, it also poses the risk of surveillance and the misuse of<\/strong> personal biometric data<\/strong>. The challenge is to strike a balance between safety and privacy without compromising either one. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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GDPR and Targeted Advertising: A Very Fine Line<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In contexts such as targeted advertising or consumer data analysis, AI can easily lead to practices that violate the GDPR<\/strong>, the General Data Protection Regulation.<\/p>

Principles such as informed consent<\/strong>, the right to be forgotten<\/strong> (deletion of personal data), and data protection \u201cby design\u201d and \u201cby default\u201d are becoming central to the use of new technologies.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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When AI Reveals Our Secrets: Reverse Engineering and LLM<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Reverse engineering<\/strong> is the process of analyzing an AI model (such as a Large Language Model, or LLM) to understand how it works, often in order to discover the data on which it was trained.<\/p>

Through specific prompts, these models can reveal sensitive information<\/strong> if the model was not designed with adequate security measures (privacy by design). A recent case involved ChatGPT and Bard, which managed to extract product keys for Windows 10 and 11 using a role-playing ploy. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Digital Hallucinations: Even Algorithms Dream (Sensitive Information)<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Conversely, the so-called \u201challucinations\u201d in LLMs<\/strong> provide an example of unintentional privacy violations. These are situations in which the models generate false or misleading information<\/strong>, which may be mistakenly perceived as true. <\/p>

It is not uncommon for ChatGPT, when generating a biography of a public figure, to make connections and include details that are entirely fabricated or exaggerated\u2014perhaps about the person’s private life.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Algorithmic Discrimination: A Multidimensional Problem<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Discrimination embedded in algorithms<\/strong> is one of the most critical challenges facing artificial intelligence. Let’s take a closer look at its various manifestations and possible solutions. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Stereotypes in the Dataset: When AI Reflects Social Inequalities<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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AI often replicates and amplifies societal biases<\/strong>. This is due to the nature of the data it is trained on, which reflects real-world inequalities and biases, thereby embedding stereotypes in the dataset. <\/p>

For example, an AI-based scoring system might be more likely to deny credit to people from ethnic minorities\u2014not because of an objective analysis, but because, statistically, these groups have been subject to more credit denials.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The Lesson from Google Photos on the Dangers of Incomplete Datasets<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Another example of discrimination can occur when datasets are incomplete or non-homogeneous<\/strong>.<\/p>

A notable example is the case of Google Photos<\/strong>, which in 2015 mistakenly labeled photos of African Americans as gorillas because it had not been trained with a sufficient number of examples. Similar problems could have occurred with Europeans being mistaken for lemurs or children for seals. This incident underscores the importance of having datasets that are both representative and diverse. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Artificial Intelligence and Disinformation: The Risk of Rewriting History<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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When hallucinations occur in LLMs, the AI may unintentionally create and spread misinformation<\/strong>.<\/p>

These computational errors can lead to inaccurate representations of historical events<\/strong>, influencing public perception and, potentially, the very narrative of history itself. Digital disinformation can have very serious consequences, altering the collective understanding of past events and influencing the debate over current issues. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Building a Fair Future: Strategies to Combat Discrimination in AI<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Addressing algorithmic discrimination requires a holistic approach that combines advanced technology with careful consideration of human and social factors.<\/p>

Here is a list of strategies that can be applied from a data science perspective<\/strong>:<\/p>