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      Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective

      research-article
      Journal of Business Ethics
      Springer Netherlands
      Artificial intelligence, Marketing, Ethics, Social good, Well-being

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          Abstract

          Artificial intelligence (AI) is (re)shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications (will) provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being.

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          Most cited references177

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          Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

          Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.
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            Dissecting racial bias in an algorithm used to manage the health of populations

            Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
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              Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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                Author and article information

                Contributors
                hermann@ihp-microelectronics.com
                Journal
                J Bus Ethics
                J Bus Ethics
                Journal of Business Ethics
                Springer Netherlands (Dordrecht )
                0167-4544
                1573-0697
                26 May 2021
                26 May 2021
                : 1-19
                Affiliations
                GRID grid.424874.9, ISNI 0000 0001 0142 6781, Wireless Systems, , IHP - Leibniz-Institut für innovative Mikroelektronik , ; Frankfurt (Oder), Germany
                Article
                4843
                10.1007/s10551-021-04843-y
                8150633
                34054170
                578ec9f8-8975-40c1-95de-83d355491557
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 December 2020
                : 12 May 2021
                Funding
                Funded by: IHP GmbH – Leibniz-Institut für innovative Mikroelektronik (3475)
                Categories
                Original Paper

                artificial intelligence,marketing,ethics,social good,well-being

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