What is responsible AI? 

This article aims to provide a general view of responsible AI. To learn more about IBM’s specific point of view, see our AI ethics page.

The widespread adoption of machine learning in the 2010s, fueled by advances in big data and computing power, brought new ethical challenges, like bias, transparency and the use of personal data. AI ethics emerged as a distinct discipline during this period as tech companies and AI research institutions sought to proactively manage their AI efforts responsibly.

According to Accenture research: “Only 35% of global consumers trust how AI technology is being implemented by organizations. And 77% think organizations must be held accountable for their misuse of AI.”¹ In this atmosphere, AI developers are encouraged to guide their efforts with a strong and consistent ethical AI framework.

This applies particularly to the new types of generative AI and AI agents that are now being rapidly adopted by enterprises. Responsible AI principles can help adopters harness the full potential of AI tools while minimizing unwanted outcomes.

AI must be trustworthy, and for stakeholders to trust AI, it must be transparent. Technology companies must be clear about who trains their AI systems, what data was used in that training, and, most importantly, what went into their algorithm’s recommendations. If we are to use AI to help make important decisions, it must be explainable.

Responsible AI is a socio-technical practice that brings together people, processes, tools and governance. It requires organizations to consider the full lifecycle of AI systems, from data collection and model development to deployment, monitoring and improvement. It also requires attention to the broader effects of AI on individuals, organizations society and the environment.

IBM’s Responsible Technology & Governance Framework brings together principles, properties, impact dimensions and governance practices into a single approach for building and deploying responsible technology. Responsible AI remains central to this approach, while the Framework extends the same governance model to emerging technologies such as agentic systems, quantum computing and future domains.

The Framework is organized around four components:

1. Principles for trust
   IBM’s foundational commitments for responsible technology development and deployment:

   • Technology should augment human capabilities.

   • Technology should be based on responsible data governance.

   • Technology should be open and transparent.

2. Pillars of trustworthy AI
   The qualities AI systems should demonstrate to enable trust:

   • Transparency

   • Fairness and human value alignment

   • Robustness

   • Privacy

3. Impact dimensions
   The broader outcomes IBM considers when evaluating technology:

   • Human agency and trust

   • Societal well-being

   • Environmental sustainability

4. Governance
   The programs, tools, education, policies, partnerships and review mechanisms that connect responsible technology commitments to everyday practice.

Together, these components enable responsibility to be engineered by design. As AI systems become more capable and embedded in business operations, organizations should be able to demonstrate that trust is operationalized through governance integrated in their systems and processes.

Implementing responsible AI practices at the enterprise level involves a holistic, end-to-end approach that addresses various stages of AI development and deployment.

Develop a set of responsible AI principles that align with the values and goals of AI innovation in the enterprise. Consider the key aspects described above in the “Pillars of Trust.” Such principles can be developed and maintained by a dedicated cross-functional AI ethics team with representation from diverse departments, including AI specialists, ethicists, legal experts and business leaders.

Conduct training programs to educate employees, stakeholders and decision-makers about responsible AI practices. This includes understanding potential biases, ethical considerations and the importance of incorporating responsible AI into business operations.

Embed responsible AI practices across the AI development pipeline, from data collection and model training to deployment and ongoing monitoring. Employ techniques to address and mitigate biases in AI systems. Regularly assess models for fairness using established metrics, especially regarding sensitive attributes such as race, gender or socioeconomic status. Prioritize transparency by making AI systems explainable. Provide clear documentation about data sources, algorithms, and decision processes. Users and stakeholders should be able to understand how AI systems make decisions.

Establish responsible AI governance practices and safeguards to protect end user privacy and sensitive data. Clearly communicate data usage policies, obtain informed consent and comply with data protection regulations.

Integrate mechanisms for human oversight in critical decision-making processes. Define clear lines of accountability to ensure responsible parties are identified and can be held responsible for the outcomes of AI systems. Establish ongoing monitoring of AI systems to identify and address ethical concerns, biases or issues that may arise over time. Regularly audit AI models to assess compliance with ethical guidelines.

Foster collaboration with external organizations, research institutions, and open-source groups working on AI safety. Stay informed about the latest developments in responsible AI practices and initiatives and contribute to industry-wide efforts.