Does Explainable AI Have Moral Value?

• URL: http://arxiv.org/abs/2311.14687v1
• Date: Sun, 5 Nov 2023 15:59:27 GMT
• Title: Does Explainable AI Have Moral Value?
• Authors: Joshua L.M. Brand, Luca Nannini
• Abstract summary: Explainable AI (XAI) aims to bridge the gap between complex algorithmic systems and human stakeholders. Current discourse often examines XAI in isolation as either a technological tool, user interface, or policy mechanism. This paper proposes a unifying ethical framework grounded in moral duties and the concept of reciprocity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Explainable AI (XAI) aims to bridge the gap between complex algorithmic systems and human stakeholders. Current discourse often examines XAI in isolation as either a technological tool, user interface, or policy mechanism. This paper proposes a unifying ethical framework grounded in moral duties and the concept of reciprocity. We argue that XAI should be appreciated not merely as a right, but as part of our moral duties that helps sustain a reciprocal relationship between humans affected by AI systems. This is because, we argue, explanations help sustain constitutive symmetry and agency in AI-led decision-making processes. We then assess leading XAI communities and reveal gaps between the ideal of reciprocity and practical feasibility. Machine learning offers useful techniques but overlooks evaluation and adoption challenges. Human-computer interaction provides preliminary insights but oversimplifies organizational contexts. Policies espouse accountability but lack technical nuance. Synthesizing these views exposes barriers to implementable, ethical XAI. Still, positioning XAI as a moral duty transcends rights-based discourse to capture a more robust and complete moral picture. This paper provides an accessible, detailed analysis elucidating the moral value of explainability.

Related papers

• Dataset | Mindset = Explainable AI | Interpretable AI [36.001670039529586]
  "explainable" Artificial Intelligence (XAI)" and "interpretable AI (IAI)" interchangeably when we apply various XAI tools for a given dataset to explain the reasons that underpin machine learning (ML) outputs. We argue that XAI is a subset of IAI. The concept of IAI is beyond the sphere of a dataset. It includes the domain of a mindset. We aim to clarify these notions and lay the foundation of XAI, IAI, EAI, and TAI for many practitioners and policymakers in future AI applications and research.
  arXiv  Detail & Related papers  (2024-08-22T14:12:53Z)
• Can Artificial Intelligence Embody Moral Values? [0.0]
  neutrality thesis holds that technology cannot be laden with values. In this paper, we argue that artificial intelligence, particularly artificial agents that autonomously make decisions to pursue their goals, challenge the neutrality thesis. Our central claim is that the computational models underlying artificial agents can integrate representations of moral values such as fairness, honesty and avoiding harm.
  arXiv  Detail & Related papers  (2024-08-22T09:39:16Z)
• If our aim is to build morality into an artificial agent, how might we begin to go about doing so? [0.0]
  We discuss the different aspects that should be considered when building moral agents, including the most relevant moral paradigms and challenges. We propose solutions including a hybrid approach to design and a hierarchical approach to combining moral paradigms.
  arXiv  Detail & Related papers  (2023-10-12T12:56:12Z)
• Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness [55.037030060643126]
  Intersectionality is a critical framework that allows us to examine how social inequalities persist. We argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness.
  arXiv  Detail & Related papers  (2023-03-16T21:02:09Z)
• Beyond Bias and Compliance: Towards Individual Agency and Plurality of Ethics in AI [0.0]
  We argue that the way data is labeled plays an essential role in the way AI behaves. We propose an alternative path that allows for the plurality of values and the freedom of individual expression.
  arXiv  Detail & Related papers  (2023-02-23T16:33:40Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Relational Artificial Intelligence [5.5586788751870175]
  Even though AI is traditionally associated with rational decision making, understanding and shaping the societal impact of AI in all its facets requires a relational perspective. A rational approach to AI, where computational algorithms drive decision making independent of human intervention, has shown to result in bias and exclusion. A relational approach, that focus on the relational nature of things, is needed to deal with the ethical, legal, societal, cultural, and environmental implications of AI.
  arXiv  Detail & Related papers  (2022-02-04T15:29:57Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Aligning AI With Shared Human Values [85.2824609130584]
  We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. We find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
  arXiv  Detail & Related papers  (2020-08-05T17:59:16Z)
• Learning from Learning Machines: Optimisation, Rules, and Social Norms [91.3755431537592]
  It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making. Recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.
  arXiv  Detail & Related papers  (2019-12-29T17:42:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.