Use AI responsibly

Mitigate risks and promote trust

What is responsible AI?

Responsible AI means developing or using artificial intelligence in a way that is ethical, transparent, fair, and accountable – to ensure it’s both safe for society and consistent with human values.

Why is responsible AI important?

Using AI responsibly is critical to ensuring consumer privacy, avoiding discrimination, and preventing harm. Violating consumer trust can damage a brand’s reputation, go against regulatory requirements, and have negative impacts on society.

Benefits of responsible AI

Build trust with users and stakeholders
People – especially consumers – are more likely to adopt and interact positively with AI systems they trust to be fair, transparent, and ethical.
Drive inclusive and fair outcomes
Prioritizing fairness and inclusivity ensures that AI systems serve diverse populations without bias.
Stay ahead of regulatory requirements
As regulatory bodies introduce more regulations governing AI, prioritizing responsible AI ensures compliance with these evolving legal frameworks, helping to avoid fines and legal issues.
Manage risk
Identify and mitigate risks early, including ethical risks, reputational risks, and potential legal liabilities, particularly in areas subject to regulation.
Make better AI-powered decisions
AI systems designed with responsibility in mind often lead to better decision-making. They are more likely to consider a wider range of factors and implications.

How does responsible AI work?

Responsible AI systems are fair, transparent, empathetic, and robust. For AI to be considered responsible, its decision-making process needs to be explainable, hardened to real-world exposure, and behave in a way that aligns to human norms.

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What are the core principles of responsible AI?

Fairness

Artificial intelligence must be unbiased and balanced for all groups.

Transparency

AI-powered decisions must be explainable to a human audience.

Empathy

Empathy means that the AI adheres to social norms and isn’t used in way that’s unethical.

Robustness

AI should be hardened to the real world and exposed to a variety of training data, scenarios, inputs, and conditions.

Accountability

Accountability in AI is driven by organizational culture. Everyone across departments and functional areas must hold themselves and their AI to a high standard.

Fusing AI with empathy

To determine what about AI causes concern and mistrust, Pega conducted a survey on consumers’ views on AI and empathy.

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What are some potential AI risks?

Risks associated with opaque AI are amplifying discrimination and bias, driving negative feedback loops that reinforce misinformation based on inaccurate data, eroding consumer trust, and stifling innovation.

How to prepare for and prevent AI risks

Oversight and testing
AI relies on dozens, hundreds, or even thousands of models, so achieving fair outcomes requires testing them frequently and always having human oversight.
Data accuracy and cleanliness
Historical and training data must be high quality, diverse, bias-free, and representative of the actual population.
Ethical design
The intent of the algorithm design and outcomes the organization desires must be ethical and comply with social norms, regulations, and the organization’s values.

Frequently Asked Questions about responsible AI

While the terms "ethical AI" and "responsible AI" are related and often used interchangeably, they can have slightly different connotations. In general, both concepts aim to address the ethical considerations surrounding the development and deployment of artificial intelligence, but they focus on different aspects.

While ethical AI primarily concentrates on moral principles and values, responsible AI extends its focus to a broader set of considerations, emphasizing the need for a comprehensive and holistic approach to address the challenges and opportunities associated with AI technologies.

Identifying and reducing AI bias, especially when it's not obvious, requires a combination of careful design, continuous monitoring, and proactive measures. Pega Ethical Bias Check is a great tool that can help you identify fields with bias potential, simulate and test strategies, generate warnings, and validate and resolve biases.

To prepare for introducing AI at your organization, consider the following steps:

Define your goals and objectives: Clearly identify the problems you want to solve or the opportunities you want to leverage with AI.
Assess data readiness: Evaluate the quality, quantity, and accessibility of your data to ensure it is suitable for AI applications.
Build a skilled team: Assemble a team with expertise in AI, including data scientists, engineers, and domain experts.
Develop a strategy: Create a roadmap that outlines the AI initiatives, implementation plan, and resource allocation.
Start small: Begin with pilot projects to test and validate AI solutions before scaling them across the organization.
Ensure ethical considerations: Address ethical concerns related to data privacy, bias, and transparency in AI systems.
Provide training and education: Equip employees with the necessary knowledge and skills to work with AI technologies.
Monitor and evaluate: Continuously monitor the performance and impact of AI solutions and make adjustments as needed.
Foster a culture of innovation: Encourage experimentation, collaboration, and a growth mindset to drive AI adoption.
Stay updated: Keep up with the latest advancements and best practices

Responsible AI is a winner for everyone

Find out why Pega Ethical Bias Check earned an Anthem Award from the IADAS for preventing discrimination in AI outcomes.

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