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Beneficence in AI

By Randy Salars

AI should be designed to benefit humanity and avoid harm. This includes maximizing positive impact and minimizing risks to individuals and society.

Why Beneficence Matters

Beneficence is the principle of doing good—ensuring that AI systems are developed and deployed to enhance wellbeing, promote human flourishing, and avoid causing harm. As AI becomes more powerful, its potential to help or hurt grows. Prioritizing beneficence means putting people first and aligning technology with ethical and social values.

Dimensions of Beneficence

Positive Impact: AI should be used to solve real-world problems, improve quality of life, and advance knowledge, health, sustainability, and justice.
Harm Avoidance: Systems must be designed to prevent foreseeable harms—such as discrimination, misinformation, or physical danger.
Risk Assessment: Ongoing evaluation of potential negative impacts, including unintended consequences and long-term effects.
Inclusive Benefit: Ensure that the advantages of AI are distributed fairly and do not exacerbate inequality.

Approaches to Beneficence

Conduct impact assessments before and after deployment to identify risks and opportunities for positive change.
Engage with stakeholders, including vulnerable and marginalized groups, to understand needs and concerns.
Design AI for social good—focus on applications in healthcare, education, accessibility, climate, and public interest.
Establish clear protocols for monitoring, reporting, and mitigating harm.

Challenges

Balancing innovation and benefit with the need for caution and risk mitigation.
Measuring and comparing benefits and harms across different groups and contexts.
Addressing unintended consequences and emergent risks as AI systems scale.

Where Can You Learn More?

Google – Responsible AI Practices (Beneficence)
AI for Good Foundation
United Nations – AI for Good
AI Ethics Journal – Beneficence in AI
Nature – How to Build Ethical AI (Beneficence Section)

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