Ethics of AI: A Systematic Literature Review of Principles and Challenges (2109.07906v1)

Abstract: Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assess the ethical capabilities of AI systems and provide best practices for further improvements.

Systematic Literature Review on AI Ethics Principles and Challenges

The paper "Ethics of AI: A systematic literature review of principles and challenges" by Khan et al. offers a meticulous examination of existing literature concerning the ethical principles and challenges associated with AI. Conducted via a systematic literature review (SLR), this paper synthesizes information from various sources to provide a comprehensive overview relevant to both policymakers and academic researchers interested in AI ethics.

The investigation reveals a global consensus on 22 ethical principles pertinent to AI, with transparency, privacy, accountability, and fairness emerging as predominant. Transparency, frequently cited across 17 reviewed studies, is deemed crucial for understanding AI decision-making processes both operationally and technically. Privacy, identified in 16 studies, is fundamentally about safeguarding user data, which is imperative given AI's data-driven nature. Accountability, noted in 15 studies, relates to assigning liability for AI actions, signifying the importance of justice within ethical AI operations. Fairness, also appearing in 14 studies, addresses biases that may arise in AI systems and underscores the societal impact of ethical AI decisions.

The interplay among these principles reflects adherence to known ethical frameworks such as the ART framework, which incorporates key principles like accountability, responsibility, and transparency. The paper's identification of these focused principles is consistent with existing frameworks and emphasizes their relevance in contemporary AI ethics discourse.

The paper further identifies 15 challenges that hinder the effective adoption of these ethics in AI practice. Notably, a lack of ethical knowledge and presence of vague principles are prevalent barriers. This reflects an urgent need for better education and clearer guidelines within the field. The challenges underscore current deficiencies, such as a general ambiguity among ethical guidelines, which complicates real-world application and often lead to diverging interpretations by different stakeholders. Additionally, there is concern that existing guidelines lack practical applicability due to a deficit in technical understanding among policymakers, thereby exacerbating the knowledge gap between ethical theory and AI technology.

By highlighting these challenges, the paper stresses the necessity for further research and action in the field of AI ethics. Establishing more defined frameworks that quantify principled actions and integrating systematic models to guide implementation are suggested avenues for future research, potentially informing a maturity model to evaluate ethical capabilities in organizations developing AI systems.

In conclusion, Khan et al. provide insightful contributions that elucidate current practices and project essential future directions for the discourse on AI ethics. Key implications involve enhancing transparency and accountability in AI operations, as well as improving educational outreach regarding ethical practices among AI practitioners. Continued research, particularly empirical studies and the development of evaluation models, is vital for advancing ethical AI systems that align with societal values and technological realities.