Artificial Intelligence and Ethics
Bibliographic record
Abstract
The use of artificial intelligence (AI) in various fields of society has increased significantly in recent years. However, as AI systems become more advanced, ethical considerations that arise must be addressed. The chapter “Artificial Intelligence and Ethics” part of the book Digital Health Entrepreneurship provides a comprehensive overview of the ethical implications surrounding the use of AI in society. The chapter begins by defining ethics as a system of moral principles that guide human behavior, highlighting the need for these principles to guide the development and deployment of AI. It provides a detailed overview of AI, including its architectural structures, learning algorithms, and reliance on various types of data. The chapter identifies potential ethical challenges associated with AI, including autonomy loss, bias, deception, deep fakes, discrimination, erosion of society, exclusion, humane treatment of AI, incompetence, inequality, lethal autonomous weapons, malicious use, privacy violations, safety concerns, security risks, transparency loss, and unintended consequences. To address these ethical challenges, the authors call for action to engage the global community in ongoing discussions and initiatives focused on ethical AI. The chapter observes convergence around key ethical principles of transparency, justice and fairness, non-maleficence, responsibility, and privacy. The United Nations and the World Health Organization offer perspectives on ethical AI, emphasizing human-centered, safe, trustworthy, beneficial, transparent, responsible, explainable, interpretable, and meaningful AI. The ethical considerations surrounding AI have implications for a wide range of human stakeholders, including researchers, policymakers, industry leaders, and the public. Interdisciplinary collaboration is needed among experts in diverse fields. Additionally, engaging the public in these discussions is essential to ensure that AI is developed and deployed in ways that align with societal values and expectations. The chapter concludes by stressing the importance of integrating ethical considerations into AI development and deployment. It highlights the need for a universal global standard on ethical AI and the significance of collaboration among nations, organizations, and entities worldwide. By prioritizing ethics in AI, societies can ensure the responsible and beneficial use of this transformative technology for the well-being and safety of humanity. In summary, the chapter “Artificial Intelligence and Ethics” part of the book Digital Health Entrepreneurship published by Springer Nature offers valuable insights into the ethical implications of AI. It emphasizes the importance of a human-centric approach to AI development and deployment, highlighting the need for safety, fairness, transparency, accountability, and inclusivity. The chapter identifies potential ethical challenges associated with AI and offers solutions to address these challenges. Ongoing interdisciplinary approaches and international collaboration are crucial in navigating the complex ethical landscape of AI and ensuring its responsible and beneficial use for the betterment of humanity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".