On conflicts between ethical and logical principles in artificial intelligence

Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective

“…D'Acquisto (2020) emphasizes the value of a balanced perspective in the battle between performance and explainability and points out that while a “certain level of transparency” (as opposed to a “certain level of autonomy”) of black box AI helps reduce mistrust in the system, the quest for explainability and transparency should not destabilize other principles and logical constraints (p. 899). Unreasonable and unjust AI explainability requirements can be a disincentive for innovation, especially as innovations are often protected by specific intellectual property laws with limited openness to external stakeholders.…”

“…Extant literature widely assumes that only humans should be regarded as responsible agents (Adadi and Berrada, 2018). The idea that all responsibility can be allocated to humans and not machines assumes that AI systems are not stakeholders and have no interests to defend (D'Acquisto, 2020). This idea suggests that regulations and ethics are only applicable to humans and that it is humans' responsibility to allow or avoid the point of no return of AI autonomy, and therefore humans are the only responsible agents for AI system decisions.…”

“…(Coeckelbergh, 2020, p. 2,054) This discussion highlights that organizations and decision-makers need to make responsibility expectations clear to all stakeholders. Furthermore, societies need to think about strategies that can promote policies that ensure machine outcomes can be controlled and harmful consequences can be assessed and acted upon by a trusted centralized entity (D'Acquisto, 2020). In assessing and managing machine outcomes, individual experiences can be distinguished from group experiences.…”

“…regressions) are the least precise (Gunning and Aha, 2019). D'Acquisto (2020) argues that explainability can be a complex problem in autonomous machines since machines are geared first to the principles of formal logic and then (possibly) to ethical or legal principles. This complexity often needs in-depth reflection to prevent conflicts between the legal and ethical principles and the formal logic upon which the AI system is based to serve organizations and/or human users.…”

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

“…In other words, it has a clear purpose, which implies justification as one requirement for beneficence (Morley et al, 2020 ). Of course, one has to point out that the notion of goodness, which is at the core of the beneficence principle, is far from being objective both on the individual and superordinate levels (D’Acquisto, 2020 ). On the individual (customer) level, predictions of future choices based on patterns of customers’ past choices and preferences of similar other customers through recommender systems can be considered as a surrogate for social influence (Cappella, 2017 ).…”