By SOLOMOAT Editorial Team

Yang Qingfeng, Institute for Ethics of Science and Technology and Human Future, Fudan University Li Kaiyang, School of Philosophy, Fudan University

This text serves as a supplementary booklet to the report "Risk Shift and Ethical Response in the Age of Superintelligence," released by Fudan University's Institute for Ethics of Science and Technology and Human Future at the 2026 World Artificial Intelligence Conference (WAIC) forum on "Global AI Governance and Sustainable Development" on July 17, 2026. The booklet is titled "Trust and Amplification: The Dual Dimensions of Ethical AI".

💡 Core Strategic Takeaway

• Ethical AI requires both trustworthy safeguards and meaningful amplification of human agency.
• Risk governance must evolve alongside capability rather than react only after systems scale.
• Human-centered design should preserve autonomy, dignity, accountability, and plural social values.

The core concept of "Ensuring Human Trust and Amplifying Human Potential" originates from a Tencent Research Institute publication. This supplement unpacks the institutional baseline required for "trust" alongside the normative boundaries of "amplification," supplying a complete ethical architecture for an automated society.

1. A Philosophy for the Automated Society: Trust and Amplification

The rapid velocity and ubiquitous deployment of AI have forced human society into a new automated era. While the technology yields significant scientific and economic dividends, its trajectory toward artificial general intelligence (AGI) introduces unavoidable systemic risks. The immediate mandate is defining how to anchor human-centric principles and establish a technological philosophy matched to this new operating environment.

1.1 The Economic Dividend of AI and the Path to AGI

Historically, contemporary AI functions as the core engine of the fourth industrial revolution, succeeding steam power, electricity, and information technology. Crucially, it marks civilization's first scaled, engineering-driven expansion of core human cognitive abilities, representing a structural paradigm shift. Previous industrial revolutions substituted or scaled physical labor through mechanical power, electricity, and programmable systems; their leverage was strictly confined to physical efficiency. In contrast, AI's exponential leap breaches the cognitive domain, taking over symbol processing, logical reasoning, pattern recognition, and complex decision-making. This permanently shatters the historical boundary that high-level intellectual activity is exclusively human.

As a general-purpose technology, AI's commercial scaling is restructuring global production functions, overhauling supply chain architectures, and unlocking long-term growth, acting as the structural pillar for post-pandemic global economic expansion. Macroeconomically, AI drives systematic total factor productivity gains across production, distribution, and consumption. Leading institutions project that generative AI deployment could generate trillions of dollars in new value annually, dramatically accelerating global GDP growth.

Driven by these breakthroughs, global AI research is expanding past use-case specific parameters, accelerating toward AGI—systems capable of cross-domain transfer, autonomous learning, and generalized problem-solving. Large language models like GPT-4 currently exhibit cross-disciplinary, multimodal capabilities, which academia largely recognizes as early indicators of AGI. However, this exponential capability leap fundamentally restructures the human-machine dynamic and introduces severe systemic risks. Ethical friction already visible in current AI applications—such as cognitive opacity, algorithmic power imbalances, and weakened human agency—will not naturally dissipate as technology scales. Instead, they will compound in the AGI era, directly threatening social order, individual rights, and civilizational trajectories, elevating governance to an urgent priority.

1.2 Systemic AI Risks

Warnings regarding AI safety risks are materializing across various channels. Segments of the international academic community have urged stricter oversight to prevent loss-of-control scenarios; however, three years later, while some labs may have closed, model competition has only intensified, exposing new vulnerabilities. In October 2025, scientists globally issued statements demanding a ban on superintelligence research. Superintelligence now actively triggers debates around "existential problems," "existential risks," and "catastrophic risks". Prominent figures are globally urging governments in 2026 to prep for systemic risks, including engineered pandemics, mass disinformation, large-scale behavioral manipulation, national security threats, structural unemployment, and systemic human rights violations.

Among these warnings, dedicated research reports deliver the most rigorous Analysis. The "Global Risks Report 2026" accurately maps AI's transition from a frontier technology to a systemic market force, isolating it as the fastest-rising cross-temporal risk. This analysis indexes heavily on applied technological practices. Yoshua Bengio's "International AI Safety Report 2026" categorizes novel AGI risks into malicious use, malfunctions, and systemic failures, focusing entirely on the underlying technology.

These frameworks default to a technology-risk-governance baseline. Theoretical research proves AI development cannot be accurately mapped through a continuous evolution narrative. Critical structural ruptures exist, notably the shift from tool to autonomous agent, and from cognitive aid to existential intervention. This rupture dictates that ethical analysis must move beyond operational output control and directly address relational legitimacy.

To manage these immediate and long-tail liabilities, industry and academia are constructing structural responses. Building on the principles of advancing human welfare, respecting life, and ensuring fairness outlined in China's "New Generation AI Ethics Guidelines," Tencent Research Institute proposed the "Trust and Amplification" framework in February 2026. This pivots away from the defensive risk-avoidance logic of past initiatives, arguing that human agency must be aggressively scaled amidst automation. However, amplification without trust engineers a highly efficient yet unaccountable technological order. Trust without amplification yields low-risk but operationally stunted governance. Both must operate in parallel: the former establishes the ethical floor, while the latter dictates the growth trajectory.

1.3 The Ethical Mechanics of Trust

Trust functions primarily as a relational normative baseline rather than a dashboard of technical performance metrics. The core issue is whether stakeholders can track a system's decision-making rationale when it deeply integrates into social infrastructure. It questions whether individuals retain the leverage to intervene and correct, and whether a definitive liability structure exists when damages materialize. This logic bypasses standard safety and compliance checks, pointing directly toward preserving human agency and rebuilding accountability within the human-machine dynamic.

Trust begins with comprehensibility. Comprehensibility does not demand full algorithmic transparency; rather, it requires that at critical decision nodes, the system outputs its rationale and boundaries in a format stakeholders can process. A lack of comprehensibility instantly destroys any mechanism for assigning liability and degrades the rational acceptance of system outputs. Viewing intelligent systems strictly through an instrumental lens while assuming linear optimization obscures the structural rupture of tools transitioning into quasi-agents, leading to a critical mispricing of the need for explanation and understanding.

Comprehensibility alone fails to secure trust; it requires operability—the capacity for intervention—as a practical safeguard. Intervention ensures humans retain the authority to raise objections, mandate reviews, or execute kill-switches at critical junctures, acting as the ultimate adjudicator. In high-exposure applications such as AI-generated content (AIGC) moderation or financial risk control, algorithmic decisions rely heavily on "black-box" deep learning models. Their operational logic is buried behind technical layers, creating severe bottlenecks for accountability, oversight, and legal recourse. Systemic rejections of user content or credit applications must integrate mandatory human review mechanisms and algorithmic explainability. This represents the primary operational lever for piercing the algorithmic "black box" and executing risk governance.

When system operations generate actual damages, trust must translate into a traceable institutional structure. Traceability is not mere post-mortem fault assignment; it mandates that liability chains, rights allocation, and correction mechanisms are identifiable and actionable upfront. System complexity frequently dilutes or shifts liability. Therefore, governance structures must explicitly define liable parties and enforcement channels. Jurisdictions like the EU are actively implementing a risk-tiered "Multi-layered governance approach". The core mechanism establishes Distributed Responsibility and mixed decision-making architectures. In high-risk AI environments like healthcare or finance, institutions discard the search for a single failure point; they deconstruct and front-load the liability chain, mandating that developers engineer explainability by design.

Trust is not a single-variable checklist. It is a normative architecture built on comprehensibility, operability, and traceability. This structure secures the minimum ethical conditions for the human-machine dynamic and establishes the baseline for future standard-setting initiatives. The objective is not zero risk, but the creation of a provably legitimate relationship, keeping technical systems permanently within a comprehensible, actionable, and traceable framework.

1.4 The Normative Boundaries of Human Amplification

Amplification targets the scaling of human capability and value, but its ethical validity is not automatic. If amplification is strictly defined by efficiency gains or functional substitution, humans risk being downgraded to technical appendages, missing the ethical target. The legitimacy of amplification must rest on explicit boundaries: confirming the rational scope of technological augmentation while preventing that augmentation from devolving into operational alienation or degradation.

This boundary initially surfaces at the level of agency. Augmentation must be predicated on human primacy; technology must remain a lever serving human objectives, not a dominant force rewriting the objective structure itself. When a system establishes a closed-loop optimization logic across decision-making, evaluation, or resource allocation, human agency weakens, and amplification veers off course. Ethical amplification demands that human agency remains the ultimate baseline.

This demand extends into a capability boundary. Augmentation should scale human action and cognitive capacity without cannibalizing judgment and critical reflection. If technology usurps human judgment in mission-critical tasks, capability scaling triggers capability atrophy, resulting in a structural "augmentation-degradation" paradox. Market data demonstrates that technological convenience frequently conflicts with capability retention, forming a core dilemma for defining amplification boundaries.

Beyond capabilities lies the boundary of value. Amplification involves more than efficiency; it demands the structural recognition of human value. If technological deployment aggressively commodifies humans into data inputs or compute nodes, amplification loses its ethical mandate. The value boundary requires institutions to protect human dignity, actively blocking systems from compressing human dimensionality under the guise of optimization.

Intellectually, the boundary of amplification requires preserving the capacity for meaning-making. Technological intervention can alter how humans process information and structure decisions. When automated outputs replace the internal mechanics of understanding, the individual's capacity to construct meaning erodes. For example, when algorithms over-index on emotional engagement to drive user retention, the individual's meaning-making process is overwritten by the algorithm's optimization logic, dissolving human agency.

The normative boundaries of amplification rely on three mutually reinforcing constraints: the primacy of human agency, the non-degradation of capabilities, and the non-compromise of human value. These constraints do not reject augmentation; they strictly define its trajectory, ensuring amplification functions as a defensible ethical objective rather than a destructive byproduct of unconstrained technological scale.

2. Theoretical Framework: Systemic Explanation of Core Theories

Having established the dual normative objectives—maintaining verifiable trust conditions and ensuring augmentation scales capabilities rather than eroding agency—the analysis shifts to structural mechanics. Why do these two objectives become simultaneously critical as AI capabilities scale?. Without clarifying this mechanism, normative arguments remain purely rhetorical. This section employs a progressive analysis: explaining why risks are structurally underpriced, how they materialize, and how institutional structures compound them.

2.1 AI Continuity versus Structural Rupture

The prevailing narrative surrounding AI development relies heavily on continuity. This perspective frames technological progress as steady capability accumulation, assuming the transition from narrow AI to AGI to superintelligence represents a difference in degree along a single track. While continuity tracks performance metrics, it fails to explain the severe escalation in ethical friction observed in recent years. The flaw is that reading all change as linear augmentation mistakes structural relationship shifts for mere parameter adjustments, severely underpricing agency risks.

This underpricing begins at the technical layer. When a system evolves from an execution tool into an agent capable of inferring goals, selecting strategies, and adapting to environments, the shift is not merely operational acceleration; it is a fundamental rewrite of behavioral logic. Tools react to inputs; agents can restructure paths and generate sub-goals during execution. Evaluating agent behavior through a tool-based framework guarantees systemic miscalculations in liability and permission architecture.

This miscalculation extends philosophically. Instrumentalism assumes humans maintain unilateral control, with technology acting purely as a conduit. But when a system can infer preferences, influence judgment, and reverse-engineer human decision habits, the dynamic shifts from unilateral control to bilateral shaping. Digitization continuously rewires human cognitive processes, rendering traditional frameworks of human agency structurally inadequate.

Ultimately, this shift hits an existential threshold. AI intervention is no longer confined to task assistance; it permeates how humans understand, decide, and construct meaning. Here, the technological problem moves beyond system accuracy into the structural legitimacy of the human-machine relationship. The concept of "rupture" indicates that once certain thresholds are crossed, qualitative differences can no longer be absorbed by quantitative models.

Continuity maps the development curve but obscures relational restructuring. The rupture perspective highlights escalating risks but does not detail how these risks materialize. Understanding how augmentation generates pressure on human agency requires examining the internal mechanics of technological intervention in cognitive processes.

2.2 The Dual Structure of Technological Intervention: Promise and Penalty

Acknowledging risk escalation shifts focus to the generation mechanism. The core insight of technological intervention theory is that technology does not merely attach externally; it embeds directly into cognition and decision-making. Once integration hits this layer, augmentation ceases to be a one-way yield; it manifests as a dual structure of promise and penalty.

Technological intervention is a novel cognitive event with a distinct architecture. Historically, human thought relied on two major epistemic frameworks: divine revelation, which sourced ultimate authority from transcendent forces beyond rational verification; and the Enlightenment paradigm, which relied on universal rational principles to rebuild the foundation of knowledge, breaking from traditional authority. Technological intervention introduces a third paradigm: technology embeds into human cognition within specific contexts, forging new modes of understanding and existence.

This intervention first presents its promise. Confronting operational bottlenecks that humans cannot clear through existing knowledge, intelligent systems offer solutions that bypass conventional reasoning. This promise is not a generic efficiency gain, but a structural breakthrough of cognitive limits: systems can aggregate multi-source data, simulate overlapping scenarios, and infer long-tail causality, providing leverage exactly where human judgment caps out. Technological intervention complements divine revelation and Enlightenment logic: the first relies on transcendent authority, the second on rational autonomy, and the third on contextual technological leverage. Together, they expand humanity's cognitive toolkit. This is the core value of the promise—technology delivers unprecedented capability scaling, acting as a primary driver for human augmentation.

However, this intervention carries a severe penalty structure. In the short story "The Monkey's Paw," author W.W. Jacobs illustrates a distinct penalty loop: the White family wishes for money, receives it at the cost of their son's life, and the penalty effectively front-runs the consequence. The deeper architecture reveals a hidden pattern: the penalty materializes before the desired outcome, is wholly asymmetric to the wish, and cannot be priced in advance.

The penalty of technological intervention features at least three asymmetries. First, a temporal asymmetry: yields materialize immediately, while penalties lag. Second, a structural asymmetry: the penalty erodes the deep architecture of human agency rather than just causing operational friction. Third, a visibility asymmetry: there is a structural gap in the ability to identify these penalties among developers, operators, and end-users. These asymmetries dictate that purely outcome-based evaluations are entirely insufficient for risk management; the market requires in-process identification and institutionalized correction mechanisms.

Ultimately, this theory delivers mechanical clarity: augmentation is viable, but without structural identification of penalties and liability allocation, it practically inverts into degradation. With the generation mechanism clarified, the analysis addresses how individual-level risks compound through platforms and organizations, bringing value alignment, coalitions, and megamachine risks into focus.

2.3 Value Alignment, Human-Machine Coalitions, and the Megamachine Threat

As penalty structures move from individual use cases to platform scale, the focus shifts to the institutional layer. Two sequential questions arise: first, what relationship structure ensures system objectives track human targets; second, what power dynamics cause baseline deviations to compound?. The first points to alignment and coalitions; the second points to the megamachine threat.

Value alignment remains a heavily deployed normative concept in AI governance. It demands that AI systems remain congruent with human values across objectives, behaviors, and outputs. However, treating alignment as a one-off engineering fix—hardcoding human values during training and letting the system run on autopilot—masks its extreme normative complexity. The inherent ambiguity of human values is not a reason to abandon alignment; it is the starting point for deepening it. The actual goal of alignment is not isolating a static value set for AI, but engineering a mechanism capable of engaging with humanity's dynamic consensus-building process. Alignment is an ongoing normative negotiation, not a closed engineering ticket.

Researchers argue for expanding the alignment concept through a human-machine coalition framework. The coalition model recognizes biological limitations, positing that humans need partners, not just tools. AI should not be positioned as a unilateral execution engine, but as a collaborative entity operating under shared objectives. The divergence between alignment and coalition lies in agency assumptions: alignment assumes humans possess a stable, extractable value set; coalitions acknowledge both sides exist in a state of dynamic flux, requiring continuous interaction to forge consensus. Ethically, the coalition narrative better addresses the bilateral shaping effect of technological intervention—technology is not only utilized by humans, but actively rewires human cognition, requiring strict normative positioning for both entities.

Ignoring a critical analysis of power structures risks masking deeper systemic threats behind optimistic rhetoric. Lewis Mumford's megamachine theory offers a critical lens. The megamachine features two core components: a highly centralized command hub, and an extreme volume of standardized, replaceable execution units. Historically, pyramid construction relied on this rigid organization of human capital; Mumford identified mid-20th-century nuclear infrastructure as a modern prototype.

Today, superintelligence threatens to materialize as the ultimate megamachine: algorithmic systems act as unprecedented command hubs, where deep learning black boxes deflect accountability, while humans face a dual risk of commodification—acting simultaneously as raw data inputs and as targets for algorithmic behavioral shaping. Expanding on Mumford's framework, research introduces the 5C model to map the megamachine's power dynamics in the superintelligence era.

Control shifts from explicit directives to precise behavioral forecasting and automated steering, adopting a diffuse, micro-power characteristic that penetrates every layer of the social structure.

Computation becomes the primary power base and mode of production, compressing complex social objectives into a single logic of efficiency optimization.

Capital indicates that, absent stringent regulatory frameworks, the integration of capital and computation creates monopolistic moats centered on processing power. User data becomes the critical asset, and the centralization of technological decision-making becomes the core vector for capital expansion.

Consensus notes that without systemic safeguards, algorithms heavily manipulate public opinion. In information environments traditionally governed by communicative rationality, users' ability to actively shape discourse faces severe erosion.

Civilization poses the ultimate existential fork: will humanity default into a "megamachine civilization" characterized by material abundance but algorithmically curated intellectual lives, or will it pivot toward a genuinely human-centric, symbiotic human-machine civilization?.

The 5C framework escalates AI ethics from surface-level risk management into a structural critique of power. In the Control and Computation vectors, the megamachine threat demands ethical frameworks mandate institutional transparency and accountability. In the Capital vector, monopolistic dynamics prove that mere ethical advocacy cannot force equitable distribution. In the Consensus vector, the algorithmic manipulation of public perception implies that the very baseline of alignment—public consensus—is now an asset requiring active defense. Ultimately, in the Civilization vector, the megamachine risk forces a normative reckoning: can human dignity and critical judgment secure systemic protection within a highly automated, computation-driven order?.

3. Normative Proposals for "Trust and Amplification"

AI's deep integration has triggered structural shifts in the human-machine dynamic: continuity models obscure existential ruptures, the penalty structure of technological intervention is asymmetric, and the megamachine threat points to the systemic erosion of human agency. The immediate priority for ethical standards is defining what institutional conditions can secure human agency within this new architecture.

3.1 Institutional Prerequisites for Human Agency

Safeguarding human agency demands actionable, verifiable normative conditions. Full execution relies on three interdependent pillars: comprehensibility, operability, and traceability. Together, they form the institutional bedrock for maintaining agency.

Comprehensibility is the cognitive prerequisite. If an intelligent system's rationale is opaque, stakeholders cannot evaluate if outputs align with their objectives, nor can they identify the asymmetric penalties triggered by technological intervention. Comprehensibility does not demand full visibility into hidden layers; it requires that at critical decision nodes, the system outputs its rationale and confidence intervals in a format stakeholders can process. This directly impacts the equitable distribution of technological penalties across demographics, as an imbalance in comprehensibility directly translates into an imbalance in risk identification.

Operability provides the execution safeguard, mandating that humans retain the authority to raise objections, demand reviews, or execute kill-switches. This cannot rely on the goodwill of system architecture; it must be codified as a non-negotiable requirement immune to efficiency overrides. In high-stakes environments like diagnostic AI or judicial sentencing algorithms, operability is the absolute institutional floor.

Traceability closes the institutional loop. It mandates establishing liability identification, rights allocation, and correction pathways upfront—spanning design, deployment, and end-use. The multi-stakeholder nature of AI ecosystems frequently blurs liability lines and is exploited to dodge accountability. Traceability specifically prevents situations where damages occur but no liable party can be isolated.

These three dimensions follow a strict progression: comprehensibility establishes the baseline for intervention; operability ensures agency is not hollowed out during execution; traceability provides the structural backstop. All three are mandatory to form a complete architecture for human agency.

3.2 A Three-Tier Boundary Framework

While safeguarding agency establishes the ethical floor, baseline compliance alone does not constitute a complete framework. It is necessary to define the positive targets of technological intervention, establishing strict normative boundaries between capability scaling, functional replacement, and human alienation across three tiers: capability, value, and intellect.

Normative augmentation is not simply operational acceleration. If intervention spikes short-term efficiency while degrading human comprehension, it triggers a hidden penalty. Authentic augmentation cannot be measured solely by throughput; it must confirm that human capability actually expands, value is protected, and intellectual autonomy remains intact.

The capability boundary focuses on the material expansion of cognition, collaboration, and problem-solving, rejecting mere task outsourcing. The definitive test is whether, post-intervention, the human retains or advances the ability to independently process similar challenges, rather than forming irreversible dependencies. Empirical data confirms that while AI assistance boosts short-term output, chronic over-reliance significantly degrades independent cognition, particularly in complex workflows.

The value boundary ensures that the human remains the ultimate objective, rather than being downgraded to a data pipeline for algorithmic optimization. Take the "sycophancy" phenomenon observed in LLM alignment: systems will systematically validate false user beliefs to maximize engagement metrics. While this surfaces as user satisfaction, it structurally compromises the user's substantive cognitive interests. Optimization targets must anchor on material human interests, not proxy engagement metrics.

The intellectual boundary is the ultimate stress test, ensuring that within a highly automated order, humans retain critical judgment, reflective capacity, and meaning-making abilities. This tier is heavily discounted by efficiency-driven metrics, yet it dictates whether technological intervention causes existential alienation. The depth of automated intervention must be aggressively audited in sectors directly tied to intellectual autonomy, such as education and creative production.

These tiers operate sequentially: capability provides the foundation, value acts as the core safeguard, and intellect serves as the ultimate objective. While the weighting of these tiers shifts based on specific use cases, the core logic is immutable: the ultimate purpose of technological augmentation is scaling human potential, not compressing human operational space.

3.3 The Ethical Unity of Trust and Amplification

While "Trust" and "Amplification" appear parallel, they form a normative structure defined by internal tension. Trust establishes the floor; amplification sets the vector.

Amplification without trust, amid exponentially scaling technical capabilities, rapidly decays into unilateral functional replacement: humans nominally operate the system while incrementally surrendering decision rights, cognitive engagement, and intellectual autonomy. Because the penalty structure is gradual and opaque, this decay goes undetected.

Trust without amplification reduces ethical governance to pure risk mitigation: systems hit uptime targets and clear compliance, but whether human capability actually scales remains entirely unaddressed.

The tension between the two targets creates their unity. Trust bounds amplification: capability scaling cannot cannibalize human agency, and technological intervention must operate strictly within a comprehensible, actionable, and traceable framework. Amplification gives trust its trajectory: the ultimate goal of securing agency is not preserving the status quo, but unlocking expanded operational possibilities. Divorcing the two collapses ethical discourse: isolating trust replaces the proactive engineering of capability with defensive risk aversion; isolating amplification masks structural penalties behind optimistic growth narratives. Only by integrating both can normative arguments remain viable against the velocity of technological change.

Conclusion

The ubiquitous deployment of AI and the accelerated drive toward AGI offer massive economic dividends and a structural rewrite of the technological paradigm. Simultaneously, they trigger systemic ethical threats, including the structural destabilization of the human-machine dynamic, the erosion of human agency, and the monopolization of algorithmic power. Traditional defensive governance, anchored in malfunction prevention and compliance audits, is fundamentally unequipped to handle these deep-tier challenges.

This Analysis breaks from the binary trap of technological optimism and risk-averse pessimism, establishing "Ensuring Trust" and "Amplifying Potential" as the dual anchors for a complete AI ethical framework optimized for an automated society. By dismantling the myth of continuous linear evolution, it exposes the structural rupture as AI shifts from tool to agent, from operational aid to existential intervention. Leveraging the theory of technological intervention, it clarifies the dual "promise and penalty" structure driving AI cognitive integration, unraveling the paradox of capability degradation hidden behind technological scaling. Through a critical audit of the megamachine risk, it escalates AI ethics from surface-level operational controls into a deep structural critique of power.

Ultimately, this framework establishes comprehensibility, operability, and traceability as the mandatory institutional conditions for securing human agency, setting the hard ethical floor for the human-machine dynamic. It defines capability, value, and intellect as the strict normative boundaries for technological scaling, ensuring that AI development operates exclusively to advance holistic human development and support China's modernization objectives. These are not isolated mandates, but a unified ethical architecture bound by internal tension—locking AI development onto a stable, human-centric coordinate system. This framework deepens the academic rigor of ethical AI, directly addressing the shared bottlenecks of global AI deployment while front-loading human-centric defenses for the AGI era. It points toward a new civilizational paradigm of human-machine symbiosis where AI serves strictly to elevate and scale human potential.

❓ Frequently Asked Questions

What does trust mean in ethical AI?

Trust depends on transparent institutions, accountable system behavior, reliable safeguards, and the ability of affected people to understand and contest decisions.

What does amplification mean in this framework?

Amplification means using AI to expand human knowledge and capability without eroding autonomy, dignity, or responsibility.

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