12.1 Ethics, Sustainability, and Use of Information Systems
- Three main normative theories provide frameworks for assessing the ethics of actions: utilitarianism, which focuses on consequences of actions; deontology, which evaluates the action itself; and virtue ethics, which concentrates on the character of the actor.
- Systems thinking enhances ethical reasoning by emphasizing holistic analysis of complex situations’ interconnected components and relationships. This allows for a broader understanding of direct and indirect impacts.
- Sustainability considers the long-term viability of organizational systems in terms of environmental stewardship, economic viability, and social welfare. Information systems practices should align with these sustainability pillars to minimize waste and harsh effects on the environment, increase efficiencies in business, and promote a positive impact on society.
- Green IS focuses on minimizing information systems’ ecological footprint through energy efficiency, renewable resourcing, responsible disposal, and similar practices.
- Lean IS concentrates on eliminating information system waste and redundancies to optimize productivity, efficiency, and resource utilization.
- Sustainable IS provides a more holistic approach, considering information systems’ long-term impacts and viability, focusing on environmental, economic, and social implications.
- Sustainable supply chain management seeks to infuse sustainability principles into the supply chain process.
- Corporate social responsibility is an inherent recognition of the ethical relationship between a corporation and the larger societal and environmental system that it inhabits. Companies can use the three Ps of CSR (people, planet, profit) as a guide to determine appropriate information system and technology practices to follow.
- As information systems become more integrated into society, thoughtful application of ethical frameworks and sustainable practices will be crucial for responsible innovation that benefits humanity.
12.2 Intellectual Property
- The United States recognizes four forms of IP, and each is protected by its own law: copyrights, patents, trade secrets, and trademarks. These forms of IP law provide legal protections for a period of time to incentivize the creation of new information.
- Each form of IP has different requirements to initiate or apply for protection, as well as different protections provided, and varying durations.
- Each of these forms of IP has the potential to provide monetary rewards for organizations that employ information systems and information technology.
- Intellectual property laws raise ethical issues. For example, they have the potential to restrict access to information and technology, which can create a digital divide. Individuals and communities that lack access may experience disadvantages if they cannot use this information and technology for things such as decision-making.
- There are several international initiatives that seek to protect IP. These generally seek to harmonize existing IP laws across national borders to make IP function more uniformly and efficiently for companies that operate in multiple nations.
12.3 Ethics of Artificial Intelligence Development and Machine Learning
- Responsible AI development includes ethical governance that addresses considerations such as transparency, accountability, bias prevention, human control, and oversight. Regulations and industry standards ensure that technology is aligned with human values and principles.
- Multistakeholder collaboration between governments, companies, and civil society is critical to developing policies and norms that can effectively govern the growth of ethical AI innovation.
- Algorithmic bias must be proactively addressed through testing that involves diverse datasets, audits, minority oversampling, and human monitoring. Transparency regarding AI development builds trust with users.
- Explainability methodologies provide insight into AI decision logic. Continual human validation and oversight are essential, especially for high-stakes decisions.
12.4 Ethics in Health Informatics
- The use of AI and automation in health care requires thoughtful governance that addresses accountability, privacy, security, bias prevention, and human oversight to ensure responsible innovation. Regulations and policies must balance emerging technologies with patient rights.
- Upholding individual control and consent regarding health data usage builds patient trust and prevents misuse, while still allowing ethical data sharing for the public good.
- Safeguarding the privacy of sensitive patient information requires robust technical protections such as access controls, encryption, and de-identification, as well as responsible data governance policies.
- Algorithmic bias and systemic discrimination must be proactively addressed to promote health equity. Inclusive design and community engagement foster the ethical use of technology.
- With patient well-being at the core, innovations like AI can be harnessed to augment—not replace—humanistic health care. Ethical governance and compassionate design are imperative.