Ethical considerations
Understanding Ethical Considerations of AI in Education
Did you know that AI systems can inadvertently perpetuate biases present in their training data, leading to unfair treatment of certain groups? This is just one of the many ethical challenges that business schools must address when integrating AI into their curricula.
As we integrate artificial intelligence into various sectors, including education, we must navigate the complex terrain of ethics to ensure that these powerful tools are used responsibly.
Let us have a look at this video from Bernard Marr 
(Link opens in a new tab) on the biggest ethical challenges for AI.
Key Ethical Principles
- Transparency: AI systems should be transparent in their operations, allowing users to understand how decisions are made.
- Accountability: There should be clear lines of accountability for AI systems, ensuring that individuals or organizations are responsible for the outcomes of AI applications.
- Fairness: AI should be free from biases that can lead to discrimination against certain groups of people.
- Privacy: AI must respect all individuals’ privacy, safeguarding personal data against unauthorized access and misuse.
- Security: AI systems must be secure from external threats that could compromise their integrity or the data they handle.
Case Studies and Scenarios
Imagine a scenario where an AI-powered grading system is introduced to a business school. While it may increase efficiency, questions arise:
- Is the AI fair to all students regardless of background?
- How transparent is the algorithm in determining grades?
- What measures are in place to protect students’ data?
Strategies for Ethical AI Use
- Developing Ethical Guidelines: Institutions should create comprehensive ethical guidelines for AI use that align with their values and mission.
- Inclusive Design: AI tools should be designed with input from a diverse group of stakeholders, including students, faculty, and IT professionals.
- Bias Mitigation: Regular audits and updates should be conducted to identify and mitigate biases in AI systems.
- Data Governance: Establish clear policies for data collection, storage, and usage that comply with privacy laws and ethical standards.
- Continuous Education: Offer workshops and training for faculty and students on the ethical implications of AI.
Strategies in the Classroom for Ethical AI Use
In the next chapter, you will learn about Learning Experience Design with AI. One of the First Steps in LXD is Research. When you or your students research while using AI Consider principles such as transparency, fairness, privacy, and accountability. Refer to resources such as the OECD Principles on AI (Link opens in a new tab) and the UN’s Universal Declaration of Human Rights. (Link opens in a new tab) According to the World Economic Forum “instilling human rights ideas as a foundation of AI practices helps to establish moral and legal accountability, as well as the development of human-centric AI for the common good.” (“9 Ethical AI Principles for Organizations to Follow” (Link opens in a new tab) ).
Next, you should develop strategies based on that research, and develop strategies for ethical AI use in the classroom. These strategies should promote understanding and applications of ethical AI principles in a business context. It would be good to Provide real-world examples to illustrate each strategy. These examples can be from existing businesses, case studies, or hypothetical scenarios.
Afterwards, reflect on the importance of linking ethical AI principles to human rights and organizational values. How can these links contribute to more ethical and responsible AI use in business?
Challenges in Upholding AI Ethics
Despite best efforts, challenges persist in maintaining ethical standards:
- Complexity of AI Systems: The intricate nature of AI algorithms can make transparency difficult to achieve.
- Rapid Technological Change: The development of AI often outpaces the creation of ethical guidelines and regulations.
- Diverse Cultural Perspectives: Global educational institutions must navigate varying cultural norms and values related to AI ethics.
Real-world examples of why it’s important to have ethical AI
Scrutinizing Bias and Fairness
AI systems are only as unbiased as the data they are trained on. For instance, Amazon had to scrap an AI recruiting tool (Link opens in a new tab) because it showed bias against women. The algorithm had learned from historical hiring data, which was skewed toward male candidates. Business schools must teach students to critically examine datasets for biases and understand the implications of deploying biased AI systems in real-world scenarios.
Data Privacy and Security
With great power comes great responsibility. The Cambridge Analytica scandal (Link opens in a new tab) is a stark reminder of how data can be misused. AI systems often require vast amounts of data, which can include sensitive personal information. Business schools have to instill in students a strong understanding of data privacy laws, such as GDPR, and the ethical handling of data.
AI Transparency and Explainability
AI can sometimes be a black box, making decisions that even its creators can’t fully explain. The European Union’s GDPR (Link opens in a new tab) introduced the right to explanation, where individuals can ask for the rationale behind an AI decision that affects them. Business schools should highlight the importance of developing transparent AI systems that stakeholders can trust and understand.
Global Ethical Standards
Ethical AI usage doesn’t have a one-size-fits-all solution, as cultural norms vary widely. For example, China’s social credit system, (Link opens in a new tab) which uses AI to monitor citizens’ behaviour, may be seen as a severe privacy invasion in other countries. Business schools need to prepare students to navigate the complex global landscape of AI ethics.
Stakeholder Impact and Responsibility
The deployment of AI can have far-reaching impacts on employees, customers, and society at large. When Microsoft’s chatbot Tay (Link opens in a new tab) was released on Twitter, it quickly learned to spout offensive language from interactions with users. Business schools must teach future leaders to consider the broader societal implications of AI and their responsibility towards all stakeholders.
Responsible Innovation
Innovation should not come at the cost of ethical considerations. Google’s Project Maven, (Link opens in a new tab) which aimed to improve drone strike accuracy using AI, raised ethical concerns and led to employee resignations. Students must understand that responsible innovation involves weighing the benefits of AI against potential ethical and moral costs.
The management of AI in higher education is a continuous process that requires a long-term commitment. Educational institutions must create an atmosphere that promotes ethical awareness and proactive management to ensure that AI is used to enhance education rather than harm it. As we explore the potential of AI, it is important to consider its ethical implications and use it responsibly.
Artificial Intelligence – OECD.
United Nations. “Universal Declaration of Human Rights | United Nations.” United Nations.
“9 Ethical AI Principles for Organizations to Follow.” World Economic Forum, 2 July 2021.
