The book: https://link.springer.com/book/10.1007/978-3-031-21491-2
## Purpose
The book focuses on the empirical study of how and why businesses manage the ethical challenges and threats posed by their use of data, advanced analytics, and AI. It aims to understand the conceptual frameworks businesses use for data ethics management and how these contribute to the broader discussions on AI governance.
## Methods
- Conducted interviews and surveys with approximately 50 companies at the forefront of data ethics management.
- Employed a "grounded theory" approach, iteratively moving between observation and theory.
- Reviewed existing literature on ethical principles and legal frameworks related to AI and advanced analytics.
## Key Findings
1. The concept of "social license to operate" is increasingly dependent on an organization's data ethics performance in today's digital and algorithmic economy.
2. Businesses face complex ethical dilemmas, like whether to use AI to predict personal traits, which are often not public but have profound implications.
3. The field of AI governance and data ethics is evolving, with companies actively engaging in managing the risks associated with AI and analytics.
4. There exists a convergence on core ethical principles (e.g., transparency, justice, non-maleficence) and a growing body of legal frameworks addressing AI use.
5. Empirical research on how companies actually implement AI governance is crucial but currently underrepresented in academic literature.
## Discussion
This study reveals the practical aspects of AI governance, highlighting the real-world challenges businesses face in balancing technological advancements with ethical considerations. It underscores the need for empirical research in understanding and shaping effective AI governance practices.
## Critiques
1. The focus on businesses at the forefront of data ethics management might not represent the broader industry practices.
2. The study primarily centers on U.S.-based companies, which may limit the generalizability of its findings.
3. The book largely reviews existing literature and theories, potentially underemphasizing novel or emerging perspectives in AI ethics.
## Tags
#DataEthics #AIGovernance #BusinessEthics #AdvancedAnalytics #EmpiricalStudy #EthicalDilemmas #AIManagement.