Ethics2vec: aligning automatic agents and human preferences

• URL: http://arxiv.org/abs/2508.07673v1
• Date: Mon, 11 Aug 2025 06:52:46 GMT
• Title: Ethics2vec: aligning automatic agents and human preferences
• Authors: Gianluca Bontempi,
• Abstract summary: This paper proposes a way to map an automatic agent decision-making (or control law) strategy to a multivariate vector representation.<n>The Ethics2Vec method is first introduced in the case of an automatic agent performing binary decision-making.
• Score: 0.19580473532948395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Though intelligent agents are supposed to improve human experience (or make it more efficient), it is hard from a human perspective to grasp the ethical values which are explicitly or implicitly embedded in an agent behaviour. This is the well-known problem of alignment, which refers to the challenge of designing AI systems that align with human values, goals and preferences. This problem is particularly challenging since most human ethical considerations refer to \emph{incommensurable} (i.e. non-measurable and/or incomparable) values and criteria. Consider, for instance, a medical agent prescribing a treatment to a cancerous patient. How could it take into account (and/or weigh) incommensurable aspects like the value of a human life and the cost of the treatment? Now, the alignment between human and artificial values is possible only if we define a common space where a metric can be defined and used. This paper proposes to extend to ethics the conventional Anything2vec approach, which has been successful in plenty of similar and hard-to-quantify domains (ranging from natural language processing to recommendation systems and graph analysis). This paper proposes a way to map an automatic agent decision-making (or control law) strategy to a multivariate vector representation, which can be used to compare and assess the alignment with human values. The Ethics2Vec method is first introduced in the case of an automatic agent performing binary decision-making. Then, a vectorisation of an automatic control law (like in the case of a self-driving car) is discussed to show how the approach can be extended to automatic control settings.

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