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Towards a framework for understanding societal and ethical implications of Artificial Intelligence

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arxiv 2001.09750 v1 pith:YJUHRQIN submitted 2020-01-03 cs.CY

classification cs.CY
keywords societalchallengesethicalartificialconsequencesdecisionsdependingdifferent
verification ladder T0 review T1 audit T2 compute T3 formal
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Artificial Intelligence (AI) is one of the most discussed technologies today. There are many innovative applications such as the diagnosis and treatment of cancer, customer experience, new business, education, contagious diseases propagation and optimization of the management of humanitarian catastrophes. However, with all those opportunities also comes great responsibility to ensure good and fair practice of AI. The objective of this paper is to identify the main societal and ethical challenges implied by a massive uptake of AI. We have surveyed the literature for the most common challenges and classified them in seven groups: 1) Non-desired effects, 2) Liability, 3) Unknown consequences, 4) Relation people-robots, 5) Concentration of power and wealth, 6) Intentional bad uses, and 7) AI for weapons and warfare. The challenges should be dealt with in different ways depending on their origin; some have technological solutions, while others require ethical, societal, or political answers. Depending on the origin, different stakeholders might need to act. Whatever the identified stakeholder, not treating those issues will lead to uncertainty and unforeseen consequences with potentially large negative societal impact, hurting especially the most vulnerable groups of societies. Technology is helping to take better decisions, and AI is promoting data-driven decisions in addition to experience- and intuition-based discussion, with many improvements happening. However, the negative side effects of this technology need to be well understood and acted upon before we launch them massively into the world.

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  1. Ethical AI: Towards Defining a Collective Evaluation Framework

    cs.AI 2025-05 reject novelty 3.0 of 10

    A conceptual proposal for auditing AI ethics with modular 'ontological blocks' that is not implemented or empirically validated.

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