What is Responsible AI?
Also called: Ethical AI · Trustworthy AI · AI governanceDefinition
Responsible AI is the set of principles and practices organisations use to make sure artificial intelligence is developed and used in ways that are fair, safe, transparent, accountable and respectful of privacy and law. In practice it means assessing risks before deployment, testing for bias and errors, protecting personal and confidential data, keeping humans accountable for consequential decisions, documenting how systems work, and monitoring them after launch. It is supported by policies, governance roles, training and, increasingly, regulation.
Responsible AI programmes usually combine a policy that sets out acceptable and prohibited uses, a risk assessment for each significant use case, controls proportionate to risk, and clear ownership. Higher-risk uses, such as those affecting hiring, credit or health, get more testing, human review and documentation.
For employers, responsible AI is both risk management and a condition for adoption. Employees use AI more confidently when they know what is allowed, customers and regulators expect evidence of controls, and laws such as India’s Digital Personal Data Protection Act, 2023 and the EU AI Act shape how data and AI can be used.
Frequent gaps include having principles but no process to apply them, banning AI outright so staff use unapproved tools anyway, and assuming vendor tools need no assessment. Training every employee on safe, practical use is one of the most effective controls.
Key points
- Fairness, safety, transparency, accountability and privacy.
- Risk assessment proportionate to the use case.
- Humans remain accountable for consequential decisions.
- Shaped by law, including data protection rules.
- Employee training is a core control.
An example at work
Before using AI to screen CVs, an HR team at a Delhi NCR conglomerate runs a bias check on past outcomes, documents the criteria, and requires a recruiter to review every rejection the tool recommends.
Where this is used at Bodhih
Related terms
AI literacy
AI literacy is the ability to understand what AI can and cannot do, use it effectively and safely, and judge the quality of its output.
AI hallucination
An AI hallucination is output from a generative AI model that sounds confident and plausible but is false, unsupported or made up.
Generative AI
Generative AI is a type of artificial intelligence that creates new content, such as text, images, audio, video or code, from patterns learnt from data.
AI agent
An AI agent is a system in which an LLM plans and takes actions, using tools such as search, code or business software, to complete a goal over several steps.
LLM evaluation
LLM evaluation is the systematic testing of a language model or LLM application against defined criteria to measure quality, accuracy and safety.
More about Responsible AI
What are the principles of responsible AI?
Frameworks differ in wording, but most include fairness, reliability and safety, privacy and security, transparency or explainability, accountability, and human oversight. Organisations turn these into practice through policies, risk assessments, testing, documentation, monitoring and staff training.
What should a company AI policy include?
Approved tools, data that must never be entered into AI tools, acceptable and prohibited uses, the requirement to review AI output, disclosure rules, who to ask for approval of new uses, and how incidents are reported. It should be short enough that employees read it and backed by training.