Using AI responsibly and ethically is mandatory to prevent harm, ensure fairness, and maintain public trust. AI systems can amplify biases, invade privacy, or make harmful decisions if not carefully designed, used, and monitored.
As Agile Professionals work in the product development and delivery context, they should have a high-level awareness of the Responsible and Ethical use of AI.
Responsible & Ethical AI has 8 principles. Let's check them:
1- Transparency & Explainability
Transparency in Ethical AI refers to openly sharing how AI systems are developed, trained, and deployed, ensuring stakeholders understand the processes and data involved.
Explainability focuses on making AI decisions interpretable to users, providing clear reasons for outcomes to build trust.
AI systems should not operate like a "black box." Instead, users and stakeholders should understand how and why an AI system makes its decisions or predictions.
2- Fairness, Non-Discrimination (Bias mitigation)
Fairness ensures that AI systems treat all individuals and groups equitably, avoiding favouritism or harm based on attributes like race, gender, or age.
Non-Discrimination (Bias mitigation) involves actively identifying and reducing biases in data, algorithms, and decision-making processes to prevent unjust or prejudiced outcomes.
3- Safety, Robustness, and Reliability
Safety in Ethical AI ensures that AI systems operate without causing unintended harm to users, society, or the environment.
Robustness means the AI performs consistently under varying conditions, including adversarial attacks, edge cases, or noisy data, without failing unpredictably.
Reliability guarantees that the system functions accurately and dependably over time, maintaining correct and stable outputs to prevent errors that could lead to mistrust or dangerous outcomes.
4- Accountability & Responsibility
Accountability in Ethical AI ensures that organizations and individuals behind AI systems are answerable for their outcomes, including errors or harms, with clear mechanisms for redress.
Responsibility mandates that developers, deployers, and users uphold ethical standards throughout the AI lifecycle—from design to deployment—by proactively addressing risks and biases.
These two address the critical question of who is ultimately answerable when an AI system causes harm or makes errors. This principle ensures that humans maintain control and ownership over the outcomes generated by autonomous systems.
5- Privacy, Security, and Data Protection
Privacy in Ethical AI ensures that personal data is collected, used, and stored with respect for individual rights, minimizing unnecessary data exposure and obtaining informed consent.
Security involves safeguarding AI systems and their data from breaches, attacks, or misuse through robust encryption, access controls, and threat monitoring.
Data Protection guarantees compliance with legal standards (e.g., GDPR), ensuring data is handled transparently, retained only as needed, and anonymized where possible to prevent harm or re-identification.
6- Inclusiveness, Sustainable Development and Well-being
Inclusiveness in Ethical AI ensures that AI systems are designed for and accessible to diverse populations, including underrepresented groups, to prevent exclusion and promote equitable benefits.
Sustainable Development means AI solutions should support long-term environmental, social, and economic health, avoiding harmful shortcuts like excessive energy use or exploitative labor practices.
Well-being emphasizes that AI must prioritize human dignity and flourishing, safeguarding mental/physical health.
7- Human Oversight & Control
Human Oversight & Control ensures that humans retain ultimate authority over AI systems, with the ability to intervene, override, or stop decisions, especially in high-stakes scenarios like healthcare or criminal justice.
It mandates that AI operates as a tool to augment human judgment, not replace it, with clear protocols for monitoring outputs and addressing errors.
8- Environmental Impact
Environmental Impact addresses the ecological footprint of AI systems, from energy-intensive training processes (e.g., large language models) to hardware waste, urging sustainable practices like renewable energy use and efficient algorithms.
It emphasizes minimizing harm—such as reducing carbon emissions from data centers or avoiding e-waste from obsolete AI hardware—while ensuring AI advancements align with global climate goals.
If you want to have my PDF file of this topic, including examples for each principle, download it from this link: