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Example: Self-driving cars need to be robust to operate safely in various weather conditions and road scenarios.
A robust AI system in a self-driving car should be able to correctly identify and respond to pedestrians in both clear
and foggy conditions.
Transparency

Transparency in AI means being open and clear about how AI systems are created, how they
work, and what impacts they might have. It involves providing straightforward information about
the data, algorithms, and decision-making processes used in AI applications. This openness
encourages accountability, allows for scrutiny, and helps people to make informed choices about
the ethical and social implications of AI technologies.
Example: Social media platforms using AI to moderate content should disclose the criteria and algorithms they use
to flag or remove posts. If a platform removes content for hate speech, it should clearly explain the rules and the
AI’s decision-making process to the users.

Privacy
Privacy involves individuals having control over their personal information and avoiding
unwarranted interference in their lives. It encompasses the right to keep aspects of one’s life
private, such as private messages, activities, and data. Privacy is crucial as it safeguards
individual autonomy, dignity, and freedom from unnecessary intrusion.
Example: An AI-powered personal assistant, like Siri or Alexa, should ensure that users’ voice
recordings and personal data are kept secure and not misused. These assistants should not store
or share sensitive information without explicit user consent, and data encryption should be used to protect user privacy.

Accountability and Responsibility
AI developers, researchers, and organisations must take full responsibility for how AI systems
are designed and used. They should ensure that AI behaves ethically, avoids causing harm, and
respects legal and moral standards.
If any harm, bias, or misuse occurs, there should be clear mechanisms for correction, investigation,
and accountability.
Example: If a facial recognition system wrongly identifies someone, the organisation using it must review the algorithm,
fix the issue, and take responsibility for any negative outcomes.
Accountability ensures that AI remains trustworthy and transparent in society.

Inclusivity and Diversity
AI systems should be designed to include and respect the diverse identities, cultures, and
experiences of people around the world. Developers must make sure that AI does not favour or
discriminate against anyone based on gender, race, language, religion, or disability. Including
people from different backgrounds in AI design helps prevent bias and makes AI tools more fair
and useful for everyone.
Example: When developing voice assistants like Alexa or Google Assistant, including various Indian accents and
languages ensures that the system serves people from all regions equally. Inclusivity in AI promotes equality and
fairness in technology.
Sustainability and Societal Impact

AI must be developed and used in ways that benefit both people and the planet in the long run.
Developers should consider the social, economic, and environmental effects of AI systems before
deploying them. AI should aim to solve real-world problems like climate change, education,
healthcare, and poverty rather than creating new challenges.

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