Measuring What Matters For People And Planet

Child measuring his height on wall. He is growing up so fast.

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In an era where artificial intelligence systems are increasingly woven into the fabric of our daily lives, from healthcare diagnostics to environmental monitoring, we face a challenge. Our technical capabilities evolve quickly, yet we are struggling to measure whether these advances truly serve ourselves, the society that we are part of and the planet that we depend on. How can we give the necessary impulses to garner that sort of benefit if we are navigating blindly? The emergence of AI as a transformative force demands more than technical performance indicators. It requires a shift in how we assess AI’s value — moving beyond narrow performance metrics toward a comprehensive understanding of AI’s social impact.

The need for a ProSocial AI Index has never been more urgent. AI systems are increasingly capable of cooperative and prosocial behavior, but also irreversible harm. It is time to develop a standardized framework to measure and influence these critical capabilities.

The Measurement Trap: Beyond Technical Metrics

The current landscape of AI evaluation is dominated by technical benchmarks that, while valuable, tell only part of the story. Accuracy rates, processing speed and computational efficiency have become the gold standards by which we judge AI systems. Yet these metrics, crucial as they are, fail to capture the deeper questions that should guide our technological development: Does this AI system enhance human agency? Does it contribute to environmental sustainability? Does it reduce inequality or inadvertently amplify it?

This narrow focus exemplifies a classic management principle gone awry: when we mistake “treasuring what we measure” for “measuring what we treasure,” we optimize for the wrong outcomes. Traditional AI metrics are like measuring a healthcare system solely by the speed of patient processing, ignoring whether patients actually get better. ProSocial AI refers to AI systems that are tailored, trained, tested, and targeted to bring out the best in and for people and planet, requiring us to fundamentally reframe how we evaluate AI systems.

The danger lies not in measurement itself, but in incomplete measurement. When we optimize AI systems solely for technical performance while ignoring their broader social and environmental impacts, we create sophisticated tools that may excel at their narrow tasks while failing spectacularly at serving human flourishing. Beware the measurement trap, a cognitive bias that leads us to prioritize quantifiable metrics over holistic understanding, often with unintended consequences that only become apparent when the damage is already done.

The 4T Framework For Comprehensive Assessment

ProSocial AI operates on four foundational principles — the 4T’s — that provide a comprehensive approach to AI development and deployment. These principles represent a paradigm shift from purely technical optimization toward a design that is deliberately driven by the intent to serve people and planet:

ProSocial AI systems are tailored to specific communities, contexts and needs, recognizing that one-size-fits-all solutions often fail the very people they claim to serve. They are trained using diverse datasets and learning approaches that reflect the full spectrum of human experience and values. They are tested through rigorous evaluation not just for technical performance, but for their social, ethical and environmental impacts. They are targeted toward specific prosocial outcomes, with mechanisms for ongoing monitoring and adjustment throughout deployment.

These 4T’s provide the conceptual foundation for a prosocial AI Index that could transform how we evaluate and deploy AI systems. Unlike traditional metrics that focus on isolated technical capabilities, this framework demands holistic assessment that considers AI’s role within broader social and ecological systems.

Values-Led AI: The Golden Rule As Universal North Star

At the heart of prosocial AI lies a values-led approach that recognizes technology as a means to an end, not an end in itself. This approach draws inspiration from universal ethical principles that have guided human societies across cultures and millennia. The Golden Rule — “treat others as you would wish to be treated” — appears in virtually every major belief system and provides a useful framework for AI development that transcends cultural boundaries.

Incorporating the Golden Rule into AI utility functions could provide a universal foundation for prosocial behavior in artificial systems. Beyond philosophical idealism it’s a practical approach to ensure AI systems serve human flourishing rather than narrow optimization targets.

When we embed universal values like the Golden Rule into our AI measurement frameworks, we create systems that inherently consider the welfare of all stakeholders — not just the interest of the organizations deploying them. This values-led approach requires us to ask fundamental questions: Would we want to be subject to the decisions this AI system makes? Would we want our children to grow up in a world shaped by this technology? Would we want our planet’s future determined by these algorithmic choices?

The ProSocial AI Index: A Tool To Test And Transform

A comprehensive ProSocial AI Index would serve multiple functions simultaneously: assessment tool, educational framework and catalyst for transformation. Unlike existing AI evaluation methods that primarily serve technical communities, this index would be designed for use across multiple stakeholder groups — individuals evaluating the AI systems they interact with, organizations assessing their AI implementations and nations developing AI governance frameworks.

The index can evaluate AI systems across multiple dimensions aligned with the 4T’s framework.

Tailored : Metrics to assess cultural responsiveness, accessibility and community engagement in development processes.

Trained: Indicators to evaluate for dataset diversity, bias mitigation, and inclusion of marginalized perspectives.

Tested: Evaluation criteria for long-term social impact, environmental consequences and unintended effects.

Targeted: Metric to assess for clear prosocial objectives, measurable positive outcomes and adaptive learning capabilities.

Recent developments, including Switzerland’s AI for public good initiative and emerging AI ethics measurement frameworks, demonstrate growing recognition that we need new approaches to AI evaluation. The AI Safety Index and similar initiatives provide useful foundations, but it is time to go beyond compliance with minimum standards. A prosocial AI Index will go beyond safety to actively measure positive impact. Beyond looking at neutral versus harmful, it assesses the positive outcomes of the algorithmic landscape that we are designing in view of pragmatic recommendations for course correction and chartering.

From Planetary Health To Personal Wellbeing

The ProSocial AI Index operates from a systems thinking perspective that recognizes the interconnectedness of human and planetary wellbeing. Climate change, biodiversity loss, social inequality and technological disruption are not separate challenges — they are interconnected symptoms of systems that prioritize short-term optimization over long-term sustainability.

AI systems designed and evaluated through a prosocial lens necessarily consider their environmental footprint, their impacts on social cohesion, their effects on human agency and dignity and their contributions to global resilience. This requires measurement frameworks that can capture complex, interconnected outcomes rather than isolated metrics.

For example, an AI system used in agriculture would be evaluated not just for its ability to increase crop yields, but for its impacts on soil health, farmer autonomy, biodiversity, water usage, and community resilience. Similarly, an AI system used in healthcare must be assessed not only for diagnostic accuracy but for its effects on patient-provider relationships, healthcare accessibility and health equity.

Implementation: Making The Index Actionable

The true test of any measurement framework lies not in its theoretical elegance but in its practical application. A ProSocial AI Index is to be designed for use by diverse stakeholders with varying levels of technical expertise and different organizational contexts.

For individuals, the index will provide accessible tools to evaluate the AI systems they encounter daily — from recommendation algorithms to virtual assistants. A simple assessment framework can help people understand better whether the AI systems they use are designed to serve their interests or exploit their attention and data.

For organizations, the index provides comprehensive evaluation frameworks that integrate with existing governance structures. Companies can use the index to assess their AI implementations across the 4T’s, identifying areas for improvement and demonstrating genuine commitment to prosocial impact rather than mere compliance.

For nations, the index provides policy frameworks for AI governance that go beyond risk mitigation to actively promote positive outcomes. Countries could use the index to evaluate their AI ecosystems, identify areas needing support or regulation, and demonstrate global leadership in responsible AI development.

A Practical Framework: Applying The 4T’s

Pending official incentives organizations and individuals can begin implementing a prosocial AI assessment using the 4T’s framework today:

Tailored Assessment: Evaluate whether AI systems are designed with specific communities and contexts in mind. Ask: Who was involved in designing this system? Whose needs does it serve? How does it account for different cultural contexts and accessibility requirements? Score the systems that you use based on their inclusivity in design processes and responsiveness to diverse user needs.

Training Evaluation: Assess the data and learning approaches used to develop AI systems. Examine: What data was used for training? How diverse and representative are the datasets? What biases might be embedded in the training process? How are these biases being addressed? Measure systems based on data diversity, bias mitigation efforts, and transparency in training methodologies.

Testing Rigor: Evaluate the comprehensiveness of testing beyond technical performance. Consider: How has this system been tested for social impact? What are its environmental consequences? How might it affect different user groups? What unintended consequences have been identified and addressed? Rate systems based on the breadth and depth of their testing across social, ethical, and environmental dimensions.

Targeted Impact: Assess whether AI systems have clear prosocial objectives and measurable positive outcomes. Determine: What positive impact is this system designed to achieve? How is this impact being measured? How does the system adapt and improve over time? What mechanisms exist for accountability and course correction? Score systems based on clarity of prosocial objectives, measurement of positive outcomes, and adaptive learning capabilities.

While we might not have all the necessary information to answer these questions – the most important step is to start asking; sharpening our awareness of the multilayered implications of our artificial assets

Measuring Our Way To A Bright Hybrid Future

The development of a prosocial AI Index represents more than a technical challenge — it’s a moral imperative and a practical necessity for navigating our AI-infused future. By creating comprehensive measurement frameworks that capture the full spectrum of AI’s impact on human and planetary wellbeing, we can ensure that our most powerful technologies serve our highest aspirations.

It is a choice. We can continue optimizing AI systems for narrow technical metrics while hoping for positive social outcomes, or we can deliberately design measurement frameworks that align AI development with human flourishing. The prosocial AI Index offers a path forward — are we willing to walk it?