This policy paper examines critical challenges in AI assistance systems, focusing on consumer privacy protection, fairness in algorithmic decision-making, and legal accountability frameworks. The analysis identifies key ethical issues including data misuse and algorithmic bias, while highlighting legal concerns around accountability gaps and data protection inadequacies. A comprehensive policy framework is proposed incorporating privacy safeguards, bias monitoring systems, and deontological ethical principles to ensure responsible AI deployment.
Ethical Issues: One of the prominent ethical issues in relation to AI assistance has got to do with consumer privacy. This is more so the case given that there exists plenty of opportunities for misuse of personal information. Yet another ethical issue of relevance on this front relates to fairness and equity. This is particularly the case in relation to the deployment of AI in marketing efforts. For instance, according to Parsons (2019), models used by AI assistance “can be biased based on the consumer training data or based on overarching business rules…” (117).
Legal Issues: One legal issue in this realm, according to Rodrigues (2020), has got to do with accountability for damage/harms. This is especially a major concern given that as Rodrigues (2020) further indicates, an ‘accountability gap’ has been shown to exist. This accountability gap is of great relevance in the realms of compensation, justice, and causality (Rodrigues, 2020). The other legal issue which also ought to be highlighted relates to data protection. This is in relation to the measures taken to secure the integrity, availability as well as privacy of user data (Darrell, 2015).
Proposed Policy: In as far as consumer privacy is concerned, a robust framework advancing certain safeguards (i.e. implementation of privacy impact assessments, deployment of anonymisation tools and systems, etc.) and spelling out stiff penalties for infringement of user privacy rights should be put in place. In relation to fairness and equity, a proposal is hereby made to the effect that a framework be established for the close assessment and monitoring of the extent to which data sets could be deemed representative. This could be accomplished via the conduction of regular audits seeking to establish whether there are any biased algorithmic elements. The proposed policy is in line with the deontological ethical theory as it is a pointer towards the need for stakeholders in AI to honor their obligations to other people. This particular ethical principle, as Kizza (2013) observe, “means that a person will follow his or her obligations to another individual or society because upholding one’s duty is what is considered ethically correct” (79).
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