Ethical AI 101: What You Need to Know
Find out what ethical AI means and why it matters, and how to translate ethical principles and values into concrete actions and measures.
Table of Contents:
- Introduction
- Ethical Principles for AI
- Policy Action Areas for Ethical AI
- Conclusion
- References
Introduction
Artificial intelligence is quickly influencing every major facet of our life, from health care to education, entertainment to transportation and the rate of transformation is too fast. AI can now execute jobs that were long thought to be impossible for humans, including diagnosing illnesses, recognizing faces, translating languages, and playing chess, among other things.
Yet, AI raises important ethical issues and concerns that we must solve as a society. We need to take care that AI systems will not violate fairness, transparency, or accountability, and are respectful of human rights and moral values. How can we keep artificial intelligence from bringing harm, prejudice, or injustice to people and the entire human species? How can we create trust, cooperation, and diversity in the creation and application of artificial intelligence?
Few insights we will explore and try to infer to build compliance based on ethical AI practice. we will try to figure out best practices and a practical guide to building ethical AI systems that benefit humanity and the environment. To do so, we will draw on the UNESCO Recommendation on the Ethics of Artificial Intelligence, the first-ever global standard on AI ethics adopted by all 193 Member States in November 2021.
I hope that this article will help you to decide and think critically about how to use AI by taking care of future humanity. So, Let’s get started!
Ethical Principles for AI
Before we dive into the policy action areas for ethical AI, we need to understand the ethical principles that should guide the design and use of AI systems. These principles are derived from the four core values of the UNESCO Recommendation: human dignity, human rights, and fundamental freedoms, respect for diversity and pluralism, and environmental sustainability (UNESCO, 2021).
Human dignity means that every human being has inherent worth and deserves respect, protection, and fulfillment of their potential. Human rights and basic values are universal and inalienable rights to which all people have a claim, especially the right to life, liberty, privacy, speech, and education. Respect for diversity and pluralism designates that AI systems should acknowledge and celebrate the richness of human cultures, languages, identities, and perspectives, which will bring avoidance to any form of discrimination or exclusion for individuals or groups. Environmental sustainability means that AI systems should contribute to the protection and restoration of the natural environment and its resources, and support the transition to a low-carbon and circular economy.
Based on these values, UNESCO has recommended a set of rules or principles to practice AI ethically; that can be summarized as follows (UNESCO, 2021):
- Fairness: AI systems should be designed and used in a way that ensures equal treatment, access, and opportunity for all people, regardless of their characteristics or circumstances.
- Beneficence: AI systems should be designed and used in a way that promotes the well-being of individuals, society, and the environment, and prevents or minimizes harm.
- Non-maleficence: AI systems should be designed and used in a way that avoids causing or contributing to any physical, psychological, social, or environmental harm or suffering.
- Responsibility: AI systems should be designed and used in a way that ensures accountability, liability, and oversight for their impacts on individuals, society, and the environment.
- Transparency: AI systems should be designed and used in a way that ensures openness, explainability, communication, and understanding of their purpose, functioning, outcomes, and limitations.
- Privacy: AI systems should be designed and used in a way that respects and protects the privacy and personal data of individuals and groups, and prevents any unauthorized or unlawful access or use.
- Trust: AI systems should be designed and used in a way that fosters trust among individuals, groups, and institutions by ensuring reliability, security, safety, and robustness.
- Freedom and autonomy: AI systems should be designed and used in a way that respects and enhances the freedom and autonomy of individuals and groups to make informed decisions and express their views without coercion or manipulation.
- Diversity and inclusion: AI systems should be designed and used in a way that reflects and supports the diversity and inclusion of individuals and groups in terms of their cultures, languages, identities, perspectives, values, and needs.
These ethical principles are not exhaustive or mutually exclusive. They may overlap or conflict with each other in certain situations during developing AI solutions. Therefore, they require careful interpretation and application depending on the context. Moreover, they are not static or fixed. They may evolve over time as new challenges or opportunities arise from the development and use of AI solutions in our day-to-day life.
Policy Action Areas for Ethical AI
We are gonna explore how they can be implemented in practice. The UNESCO Recommendation provides a comprehensive list of policy action areas that cover different domains and contexts where AI can have significant impacts on individuals, society, and the environment (UNESCO, 2021).
These policy actions are introduced to serve as a reference and inspiration for policymakers and other stakeholders to identify and address the ethical challenges and opportunities of AI in their specific situations, along with aiming to foster cross-sectoral and interdisciplinary collaboration and coordination among different actors and institutions.
Here we will select relevant policy actions based on the UNESCO Recommendation that can be applied to specific contexts of interest: Education and Research. We will see a few significant challenges and opportunities for ethical AI development and deployment in this domain and will try to find out some best practices and recommendations on how to address them in a responsible and inclusive way.
Data governance
Data is the fuel of AI. The quality, quantity, availability, and diversity of data determine the performance, accuracy, reliability, and fairness of AI systems. Therefore, data governance is a crucial policy action area for ensuring ethical AI.
The most common challenges and opportunities for data governance in education and research are:
- How to make sure that the data are getting used for educational or research purposes are representative, inclusive, diverse, accurate, reliable, and unbiased?
- How to ensure that data used for educational and research purposes are collected, and used respecting the privacy, consent, security, and ownership of individuals and groups?
- How to ensure that data used for educational or research purposes are accessible, interoperable, reusable, and transparent?
- How to verify that data used for educational or research purposes are aligned with ethical principles and values?
Best practices and recommendations to apply data governance in education and research are:
- Adoption of data protection laws and regulations that safeguard the rights and interests of individuals and groups (e.g: GDPR).
- Developing data quality standards and guidelines that provide protection of validity, reliability, completeness, timeliness, relevance, and diversity (e.g., FAIR principles).
- Establishment of data governance frameworks and mechanisms that will enrich accountability, oversight, auditability, traceability, and explainability(e.g., data stewardship).
- Promoting data literacy and awareness among educators, researchers, and students for seamless handling of data in an ethical way.(e.g., data ethics courses).
Education
From Learning to upskilling, AI already started to play a big role in the education sector. AI is helping learners to acquire the knowledge, and skills to use and evaluate AI systems in a responsible way. Apart from that, the conventional education system can be benefited from the application of AI systems to enhance learning outcomes, access, quality, equity, inclusion, innovation, and lifelong learning.
A few questions to be asked based on challenges and opportunities for education in relation to ethical AI can be:
- How to ensure that education curricula at all levels include relevant content on AI ethics principles, values, and practices?
- How to ensure that education pedagogies at all levels foster critical thinking, creativity, collaboration, and communication skills among learners in relation to AI?
- How to ensure that education assessment at all levels measures not only cognitive but also affective, social, and emotional outcomes of learners in relation to AI?
- How to ensure that Ed-tech organizations at all levels leverage AI systems in optimal ways that are aligned with Ai ethics, and respect the rights and interests of learners, and educators?
Some of the best practices and recommendations for education in relation to ethical AI are:
- Developing and implementing national and international frameworks and guidelines that define learning objectives, competencies, and standards on AI ethics for different levels and types of education (e.g., AI4K12 initiative).
- Designing and delivering innovative and engaging learning experiences and resources that expose learners to real-world problems and solutions involving AI ethics (e.g., AI Ethics Lab).
- Adopting and adapting valid and reliable assessment tools and methods that capture the holistic development of learners in relation to AI ethics (e.g., PISA 2021).
- Evaluating and selecting AI systems that support educational goals and processes in a transparent, fair, accountable, and trustworthy way (e.g., UNESCO-Pearson Guidelines).
Research
Research is the driving force of ethical AI. Research can generate new insights, methods, tools, and applications using historical knowledge that can advance human perceptions for the evaluation of AI systems in a responsible way. Research can also find the ethical challenges and implications posed by AI to us.
Some of the challenges and opportunities for research in relation to ethical AI are:
- How to ensure that research agendas and priorities on AI ethics are inclusive, diverse, participatory, and responsive to the needs and aspirations of different stakeholders and communities?
- How to ensure that research methodologies and practices on AI ethics are rigorous, robust, reproducible, transparent, and accountable?
- How to ensure that research outputs and outcomes on AI ethics are accessible, interoperable, reusable, and impactful?
- How to ensure that research collaborations and partnerships on AI ethics are ethical, equitable, sustainable, and mutually beneficial?
Some of the best practices and recommendations for research in relation to ethical AI are:
- Conducting stakeholder analysis to identify and prioritize relevant research questions, gaps, and opportunities to optimize the research approaches(e.g., AI Ethics Canvas).
- Applying review processes and codes of conduct for making sure the integrity, quality, safety, security, privacy, consent, beneficence, and non-maleficence of research activities(e.g., AI Ethics Checklist).
- Disseminating research findings and recommendations through open-access platforms and enhancing their visibility, usability, credibility, and citation (e.g., AI Ethics Journal).
- Establishing multidisciplinary and multi-stakeholder networks or platforms which facilitate knowledge exchange, capacity building, and innovation transfer. (e.g., AI Ethics Global Network).
Conclusion
I hope that you have grasped something new and useful from this guide. Let’s infer a few key takeaways:
- Ethical AI is AI that respects and protects human dignity, rights, values, and diversity and contributes to human well-being and environmental sustainability by taking care of the future of the existence of humanity without violation.
- Ethical AI practice is built with an exclusive set of rules, such as fairness, beneficence, non-maleficence, responsibility, transparency, privacy, trust, freedom and autonomy, and diversity and inclusion.
- Ethical AI also requires implementing these principles in practice through policy action areas that cover different domains and contexts where AI can have significant impacts, such as data governance, education, research, etc.
- It is not only a moral duty but also a strategic opportunity for individuals, organizations, and society to harness the potential of AI for the well-being of humanity for developing concrete compliance for the developing practice of Artificial intelligence.
The development and use of AI is not a neutral or deterministic process. It is shaped by human choices and values. This is the reason, we all have a role and a responsibility to ensure that AI serves humanity and the environment in an ethical way.
I have attached other guidelines for Ethical AI dow below.
References:
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