Human values must guide AI evolution

For decades, global discourse around artificial intelligence has focused on managing its dangers. From autonomous weapons to data privacy breaches, the conversation has largely been defined by constraint and containment. The author argues that this narrative, while justified by risk, leaves a crucial vacuum: a collective vision for AI’s positive contribution to human life.

Artificial intelligence (AI) has reached an inflection point, forcing policymakers, researchers, and society to confront a defining question: what kind of AI future do we actually want? In a landmark study published in AI Magazine, researchers explore this question in depth, offering a structured framework for developing human-aligned, ethically grounded, and socially beneficial AI systems.

Titled “What Do We Want from AI?”, the paper calls for a shift from reactive AI regulation to proactive value-driven design. It identifies 18 guiding principles that describe the positive goals humanity should pursue in AI development, moving beyond what must be avoided, bias, exploitation, and loss of control, to what must be built: trust, transparency, accountability, and fairness.

Moving from fear to purpose: Reframing the AI debate

For decades, global discourse around artificial intelligence has focused on managing its dangers. From autonomous weapons to data privacy breaches, the conversation has largely been defined by constraint and containment. The author argues that this narrative, while justified by risk, leaves a crucial vacuum: a collective vision for AI’s positive contribution to human life.

The paper highlights that most institutional and legislative frameworks, the UK’s 12 Challenges of AI, the EU’s AI Act, UNESCO’s Ethics of AI principles, and the G7’s Hiroshima AI Process, define boundaries around AI’s potential harm rather than articulating its desired trajectory. This risk-focused mindset, the author suggests, has created a regulatory ecosystem that reacts to technological disruption instead of steering it.

The study repositions the question: instead of endlessly asking “What should AI not do?”, societies must ask “What should AI achieve?” This requires designing systems that do not merely prevent damage but actively promote social equity, human flourishing, and global sustainability.

To achieve this, the author constructs an ethical and operational blueprint organized around three major domains - AI and humans, AI characteristics, and AI in practice. Together, these domains redefine the standards of what responsible AI innovation should look like in the decades ahead.

The human dimension: Aligning AI with values, judgment, and dignity

The first domain, AI and Humans, establishes six principles focused on human-centered alignment. At its core is the belief that AI must reinforce, not replace, human agency. the author argues that technology should be built to complement cognitive skills, improve decision-making, and preserve autonomy even in high-automation environments.

The principle of Human Value Alignment demands that AI reflect pluralistic human ethics rather than universal technical objectives. Systems must adapt to cultural and social contexts without imposing uniform standards of behavior. Equally vital is Sound Judgment and Bias Correction, which requires AI models to achieve at least human-level performance in decision accuracy while continuously correcting embedded biases through iterative learning and transparent evaluation.

Another key pillar is Collaboration, where AI operates as a cooperative assistant in socio-technical systems rather than a replacement for human expertise. This model ensures a symbiotic relationship in which machines handle repetitive or data-heavy tasks, allowing humans to focus on reasoning, creativity, and empathy.

The author also introduces the principle of Deference to Humans, arguing that even when machines outperform people, users must retain the right to override or reject AI decisions. This idea preserves moral accountability within human control loops, ensuring that responsibility remains traceable and ethically defensible.

Completing this cluster is the “Helpful, Honest, and Harmless (HHH)” triad—a practical ethic of AI design that prioritizes utility, transparency, and non-maleficence. These values, the author notes, provide an operational counterpart to the abstract moral aspirations outlined in most AI policy frameworks.

Engineering trust: Building measurable, transparent and accountable AI

In the second domain, AI Characteristics, the paper transitions from philosophy to engineering, calling for measurable competence and predictability. the author identifies six technical attributes that responsible AI systems must embody: trustworthiness, dependability, consistency, explainability, moral and legal accountability, and transparency.

The Trustworthiness Principle requires AI developers to quantify system expertise through published metrics that specify reliability and operational boundaries. Just as medical devices undergo rigorous clinical validation, AI tools should disclose their rates of success, failure, and uncertainty.

Dependability refers to system stability, AI must maintain performance across time and context without degradation. This attribute becomes especially critical in high-stakes domains like healthcare, transportation, and defense, where errors can have irreversible consequences.

Consistency and Thoroughness are defined as operational virtues: AI should not succumb to fatigue, distraction, or inconsistency as humans often do. Instead, it must sustain accuracy and precision under varying workloads, creating a benchmark for human–machine complementarity rather than competition.

Confidence and Explainability introduce the dimension of interpretability. AI systems should express uncertainty and, when possible, articulate the reasoning behind their conclusions. While complete transparency in complex models like large neural networks may not always be possible, even partial interpretability improves user understanding and accountability.

Finally, the author focuses on Moral and Legal Accountability, a principle that demands the creation of frameworks for assigning responsibility when AI systems cause harm or errors. The study highlights that current liability laws remain ill-equipped to handle cases involving autonomous agents. Without clear accountability, AI risks operating in moral anonymity, a danger that could erode public trust.

Transparency, the sixth characteristic, ties all others together. Users must always know when they are interacting with AI-generated outputs and be informed of any limitations or potential biases embedded within the system. This visibility fosters both trust and informed consent, crucial for maintaining democratic oversight over algorithmic processes.

From design to deployment: Making AI work for society

The final domain, AI in Practice, transforms ethical aspiration into actionable governance. Here, the author outlines six practical imperatives for how AI should be deployed, managed, and integrated into society.

At the top of this list is Risk and Hazard Management. The study proposes that AI should replace humans primarily in dangerous or unhealthy tasks, mining, demining, hazardous manufacturing, or disaster response, where automation enhances human safety rather than employment displacement.

Societal Benefit Focus ensures that innovation priorities are aligned with human welfare rather than commercial or military advantage. AI’s potential in health diagnostics, sustainable agriculture, and education should take precedence over applications that maximize surveillance, control, or profit.

Public Sector Integration is presented as a strategic opportunity. Governments, the author argues, must deploy AI not only for efficiency but also for equity, modernizing transport, improving environmental monitoring, and expanding access to healthcare and social welfare.

Another key concept, Local Impact Control, limits the reach of AI decision-making to specific contexts or jurisdictions. This principle recognizes that algorithms trained for global use can create harm when applied without cultural or legal adaptation.

The author also calls for Rigorous Engineering Standards, where AI systems are constructed from verifiable, modular components that undergo safety certification comparable to aviation or medical devices. Such a standards-driven approach would enable reproducibility, reduce systemic risk, and simplify regulatory oversight.

Lastly, Training Data Provenance demands transparent documentation of data sources, collection methods, and ownership rights. By mandating ethical data sourcing, this principle guards against hidden biases and privacy violations that undermine fairness and reproducibility.

Building a responsible future for artificial intelligence

The analysis acknowledges that realizing these principles is not a purely technical challenge. The obstacles ahead, economic inequality, regulatory lag, and lack of AI literacy, are fundamentally social and political. The study proposes concrete mechanisms to translate ideals into governance:

• Legislative Accountability to assign responsibility for harm caused by AI failures.
• Performance Disclosure to create transparency through standardized safety metrics.
• Education Reform to cultivate AI literacy across society.
• Global Cooperation to establish interoperable standards for responsible AI.

The study also explores the future of work in an AI-driven economy, urging policymakers to consider income redistribution mechanisms like Universal Basic Income to mitigate automation-induced job loss. At the same time, it encourages the creation of new, human-centric professions that combine ethical oversight, creative problem-solving, and system design.

The author situates these reforms within a broader moral argument: AI is not inherently good or evil, it is a social artifact shaped by human intention and collective will. To ensure that it strengthens rather than weakens society, development must be guided by ethics as rigorously as it is by engineering.

The paper closes with a call to action for governments, corporations, and citizens to collaborate in defining an AI future that serves humanity rather than governs it. In the author’s vision, artificial intelligence should not merely think like humans, it should care like humans, too.