Wave upon wave of digital technologies and the fast-advancing frontier of artificial intelligence (AI) are disrupting well-established, institutionalized ways of doing things, resulting in a host of ethical, regulatory, and policy challenges. The challenge involves addressing social, ecological, moral, and economic risks associated with technological advancements while, at the same time, encouraging competitiveness, innovation, and value generation from ubiquitous digital data. To address current ethical challenges and risks and anticipate future issues related to emerging technologies, governments are seeking to adjust regulations and set policy. Organizations are seeking to adapt their governance schemes to regulatory and ethical issues. This special issue seeks sociotechnical and information systems contributions to our thinking on ethics, regulation, and policy around digital technologies, data, and AI.
Ethical tensions abound in relation to the pervasiveness of digital data and the widespread adoption of AI, including tensions between data privacy, fairness, and bias on the one hand, and business needs for using data for market and innovation purposes on the other. AI systems that increasingly replace cognitive tasks previously done by humans raise questions about deskilling and labor substitution and ultimately human dignity. While AI systems promise productivity gains and the potential to address key social and environmental issues, they also generate significant threats to sustainability and social challenges. Moreover, the responsibility of algorithms for the tasks they perform and the decisions they make is often not clear. The autonomy, interconnectedness, and increasing agency of digital technologies present challenges to established individualistic responsibility approaches since they make it difficult to attribute responsibility.
The tension between addressing ethical risks and encouraging innovation requires balancing data privacy, fairness, and bias concerns with business needs for competitiveness and value generation from digital data and AI systems.
Ethical challenges have inspired regulatory and policy action around digital technologies, particularly with respect to data and AI. The United States National Artificial Intelligence Act of 2020 sets forth programs and activities and highlights values like trustworthiness; at the same time, it aims at creating competitive advantage for the US economy by creating an innovation ecosystem or fostering public-private partnership mechanisms. The European Union's AI Act embeds fundamental values as it seeks to accomplish a high level of protection of health, safety, and fundamental rights and defines requirements for transparency monitoring, market surveillance, and governance that provide legitimacy for the use of AI systems. Regulatory responses involve institutional construction and ongoing institutional change, often requiring the deliberate coordination and action of change agents at different levels, including political, corporate, or non-governmental individuals and groups. Rapid changes in the sociotechnical context fueled by the decentralized and immaterial nature of digital technologies require frequent updates and often render conventional approaches to setting regulation and policy slow and ineffective.
Digital technologies serve a dual role—they are both the source of ethical concerns triggering regulatory change and a tool for implementing, monitoring, and updating policies and regulations, distinguishing regulation of technology from regulation through technology. Such tools help regulatory institutions achieve legitimacy and stability, which marks the distinction between regulation of technology and regulation through technology. Ethical challenges as well as policy and regulatory change require organizational efforts to adopt and comply with new regulations and policies. This is challenging, since it requires organizational actors to make sense of and interpret the often-complex changes in the regulatory institutional environment and demands changes in organizational practices as well as technology architecture and implementation.