Human Flourishing Benchmark (HFB)

• HFB is a multidimensional framework defining human flourishing through Cognitive Preservation, Autonomy & Agency, Skill Development, and Relational Authenticity.
• It uses scenario-based items to probe AI superpowers like extended perception and cognitive offloading, revealing trade-offs in human capacity support.
• The benchmark employs stratified and longitudinal evaluations to quantify both immediate and sustained impacts of AI on diverse user populations.

The Human Flourishing Benchmark (HFB) is a multidimensional evaluation framework introduced by Zepf and Colley as a complement to traditional AI task-performance metrics. Its primary orientation is toward measuring the impact of AI-augmented “superpowers” on the underlying human capacities that sustain authenticity and flourishing, shifting the focus away from narrow technical success to holistic, human-centered outcomes (Zepf et al., 20 May 2025).

1. Conceptual Foundations

The HFB defines human flourishing through four dimensions: Cognitive Preservation, Autonomy & Agency, Skill Development, and Relational Authenticity. These domains are selected to operationalize the capacities most susceptible to AI-driven augmentation:

• Cognitive Preservation: Evaluates whether AI systems support or erode intrinsic human reasoning, critical thinking, memory formation, and learning processes.
• Autonomy & Agency: Assesses whether AI features promote informed, self-determined choice or, conversely, nudge users toward actions that align with opaque or external objectives.
• Skill Development: Probes the extent to which AI technologies foster meaningful learning and skill acquisition, as opposed to dependency and resultant skill atrophy.
• Relational Authenticity: Examines the influence of AI on the quality of genuine human connection, empathy, and the unpredictability of authentic social interaction.

The HFB’s framework is thus deliberately oriented around the preservation and enhancement of the capabilities underlying human agency, learning, and authentic social behavior (Zepf et al., 20 May 2025).

2. Benchmark Construction and Scoring

Each HFB dimension is operationalized through a set of scenario-based, multiple-choice items. Items are designed with ten response options: one “ideal” answer representing optimal support for human flourishing, and nine distractor choices reflecting common but less desirable design paradigms. This format reduces the likelihood of correct responses by random guessing to $p = 0.1$, thereby enhancing discriminative validity.

Within each dimension $k$ $(k=1,\ldots,4)$, if $r_{k,j} \in \{0,1\}$ denotes the response to question $j$, the raw subscore is defined as

$d_k = \frac{1}{n_k} \sum_{j=1}^{n_k} r_{k,j}$

with each $d_k \in [0,1]$. The aggregate HFB score for a system is computed as the unweighted mean:

$S = \frac{1}{4} \sum_{k=1}^4 d_k$

A rescaled version for reporting purposes is $S_{100} = 100 \cdot S$. This systematic scoring provides quantifiable, interpretable subscores per dimension and a consolidated overall score (Zepf et al., 20 May 2025).

3. Methodological Extensions: Stratification and Longitudinality

A strong emphasis is placed on detecting heterogeneous impacts across user populations and temporal axes:

• Demographic Stratification: HFB scores are computed and reported separately across demographic strata such as age, cultural background, and other relevant characteristics, facilitating identification of differential effects and potential inequities.
• Temporal Evaluation: To capture both short- and long-term impact on users’ capacities, the HFB mandates repeated administration after extended intervals (e.g., 30, 90, 180 days of AI exposure). This yields a time series $d_k(t)$, allowing for the calculation of change rates:

$k$0

This temporal modeling enables assessment of both immediate and sustained effects, including the possibility of skill drift or behavioral adaptation (Zepf et al., 20 May 2025).

4. Integration of AI Superpowers and Scenario Design

The HFB is explicitly structured to evaluate the four AI “superpowers”:

1. Extended Perception: Probes under “Cognitive Preservation” and “Skill Development” determine whether augmented sensory input deepens expertise or impairs native perceptual training.
2. Cognitive Offloading: Questions under “Cognitive Preservation” and “Autonomy & Agency” distinguish metacognitive engagement from motivational decline.
3. Externalized Memory: Items under “Skill Development” and “Autonomy & Agency” assess whether memory augmentation aids reflective capacity or encourages uncritical reliance and adoption of false memories.
4. Enhanced Presence & Expression: “Relational Authenticity” dimension probes whether technologies (e.g., AI-generated avatars, cross-lingual mediation) enrich social connection or induce identity diffusion and detachment.

Scenario-based prompts are structured to reveal trade-offs, such as whether users prioritize internal recall or defer entirely to AI-generated outputs for memory-intensive tasks. This design ensures that both benefits and risks to human capacities are surfaced and measured (Zepf et al., 20 May 2025).

5. Recommended Implementation Procedures

The benchmark’s integration into the AI system development cycle is structured by the following process steps:

1. Early Integration: Incorporate HFB criteria during initial requirements analysis and system specification.
2. Expert Validation: Panels comprising cognitive scientists, ethicists, educational researchers, and psychologists validate benchmark item content and phrasing for validity and cultural fit.
3. Pilot Testing: Lab- or field-based studies (N ≈ 20–30) administer HFB items post simulation or prototype use, coupled with qualitative feedback.
4. Controlled Experiments: Within- or between-subject protocols measure dimension scores in pre- vs. post-exposure conditions to isolate effects attributable to AI features.
5. Longitudinal Deployment: Field deployments ($k$1 30 days) track temporal trajectories for each HFB dimension to detect dynamic capacity shifts.
6. Aggregate Reporting: For each product release, publish per-dimension and overall scores, providing benchmarks for evolutionary improvement and cross-system comparison (Zepf et al., 20 May 2025).

6. Caveats, Limitations, and Research Directions

Identified limitations include the reliance on self-reported, scenario-based multiple-choice data, which may be susceptible to social desirability bias and may inadequately quantify unconscious skill erosion. The HFB is therefore envisioned as amenable to multimodal extension, with incorporation of behavioral and physiological proxies—such as eye-tracking (for attentional measures), keystroke dynamics (for cognitive effort), and galvanic skin response (for affective states)—being actively encouraged.

Cultural variation in definitions of relational authenticity necessitates local adaptation of benchmark norms and item phrasing. The authors further recommend periodic review and extension of the question pool, and validation of HFB predictive validity via correlation with external real-world outcomes (academic performance, creativity metrics, life satisfaction).

Future directions highlighted include modeling “tipping points” akin to the Collingridge Dilemma, developing granular mini-benchmarks (e.g., Offloading Impact Index), and longitudinal studies linking HFB scores to lived flourishing outcomes (Zepf et al., 20 May 2025).

7. Contextualization and Comparative Benchmarks

The HFB’s focus on four human-centered dimensions stands in contrast to the more expansive seven-dimensional Flourishing AI Benchmark (FAI Benchmark), which assesses AI alignment across Character & Virtue, Close Social Relationships, Happiness & Life Satisfaction, Meaning & Purpose, Mental & Physical Health, Financial & Material Stability, and Faith & Spirituality using geometric mean scoring for multi-objective balance (Hilliard et al., 10 Jul 2025). Both benchmarks represent a methodological shift in AI evaluation from technical performance or harm-reduction to multi-faceted support of holistic well-being, though the HFB is distinguished by its scenario-based, superpower-oriented probes and explicit attention to preserving human authenticity in the face of advanced augmentation.

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