AI-augmented scientific reasoning for interspecific affective comparisons within the Welfare Footprint Framework

commitment: 7 hrs/week
format: Research and writing · Creative project, product design, and entrepreneurship
topic: AI tools to empower advocates · Neglected and emerging animal groups (e.g. fish, insects) · Macrostrategy, philosophy, and cause prioritisation · Other topic within Sentient Futures scope
open to mentee proposals: No, I only want a mentee(s) to work on my proposed project

mentored by

Wladimir AlonsoWelfare Footprint Institute

The project

This project is centered on the Ψ Interspecific Affect GPT, an experimental AI tool developed by the Welfare Footprint Institute to support interspecific affective comparisons within the Welfare Footprint Framework. The tool is available through WFI’s AI Tools and Applications page. The scientific rationale, methodological approach, and current workflow are described in this article. The initial focus is on provisional, evidence-informed hypotheses about the maximum plausible intensity of Pain that members of selected animal taxa may be capable of experiencing, expressed relative to human-anchored reference categories. These estimates concern potential affective capacity, not the intensity that animals ordinarily experience. For each taxon, the tool produces an explicit reasoning dossier addressing taxonomic scope, the plausibility of sentience, relevant neurobiological, behavioral, cognitive, pharmacological, evolutionary, and ecological evidence, competing hypotheses, arguments against the preliminary conclusion, uncertainty, a provisional affective-capacity ceiling, and priorities for further research. The project would test both the substantive interspecific conclusions and the broader research workflow. Expected outputs could include:

critically audited dossiers for approximately three to five selected taxa;
a structured dataset recording AI claims, citation checks, errors, omissions, human corrections, and unresolved uncertainties;
an analysis of recurrent AI failure modes and variation across repeated runs;
recommendations for improving the tool’s instructions, evidence standards, knowledge base, and evaluation protocol; and
depending on the maturity of the results, a technical report, preprint, academic manuscript, EA Forum post, or a combination of these.

The mentee's role

The mentee would critically audit the AI-generated analyses rather than construct every evidence synthesis manually from the beginning. Their work would include verifying citations and bibliographic details; reading and interpreting the underlying studies; identifying omitted, contradictory, or disconfirming evidence; challenging hidden assumptions and unjustified taxonomic generalisations; checking whether conclusions exceed the evidence; comparing repeated analyses or outputs from different models; and documenting systematic AI failure modes. The mentee would also help design and apply a structured audit protocol, revise conclusions and uncertainty statements where warranted, and recommend improvements to the tool. The project therefore investigates both interspecific affective capacity and a broader model of AI-augmented scientific reasoning. AI conducts the initial evidence synthesis and generates explicit hypotheses and arguments, while the human researcher concentrates on scrutiny, attempted falsification, correction, uncertainty assessment, and methodological improvement.

Who I'm looking for

Must-haves

Strong critical-reading, scientific-reasoning, and evidence-synthesis skills.
Willingness to verify citations and read primary sources rather than relying on AI-generated summaries.
Ability to identify unsupported inferences, contradictory evidence, and unjustified generalisations.
Comfort working with substantial scientific uncertainty and competing hypotheses.
Reliable independent work, intellectual honesty, and clear analytical writing.
Interest in animal sentience, affective experience, comparative welfare science, neglected animal groups, or AI-augmented research. Nice-to-haves
Interest in the capabilities, limitations, reproducibility, and scientific evaluation of AI models and research tools.
Familiarity with one or more relevant fields, such as animal-welfare science, neuroscience, comparative cognition, evolutionary biology, philosophy of mind, systematic review, or evidence synthesis.
Some experience working across disciplines or evaluating heterogeneous forms of evidence.

Questions for applicants

Imagine that an AI-generated literature synthesis presents a confident conclusion, but the evidence is sparse, indirect, and partly contradictory. How would you audit the conclusion?
Which animal taxon or taxonomic group would you be most interested in examining in this project, and why?

Support offered

I can offer:

weekly 45–60-minute mentoring meetings;
access to the Ψ Interspecific Affect GPT and relevant Welfare Footprint Framework materials;
scientific and methodological guidance on interspecific affective-capacity inference;
help selecting taxa and defining a feasible ten-week scope;
feedback on evidence tables, audit records, uncertainty statements, and draft conclusions;
guidance on distinguishing descriptive scientific claims, methodological choices, and normative assumptions;
support identifying unsupported inferences, missing evidence, and taxonomic overgeneralisation;
assistance refining the tool and its evaluation protocol in response to findings;
regular asynchronous feedback on substantive work; and
support developing the final results into a technical report, manuscript or preprint, and/or an accessible public-facing article.

Wladimir Alonso

Welfare Footprint Institute

Wladimir is an evolutionary biologist, epidemiologist, and animal-welfare researcher. He is Co-Founder and Innovation Director of the Welfare Footprint Institute. His work focuses on developing scientifically explicit methods and AI-augmented tools to assess affective states and quantify cumulative Pain and Pleasure within defined species, populations, and production systems. He co-developed the Welfare Footprint Framework and leads work on AI-augmented evidence synthesis, interspecific affective-capacity comparisons, and tools intended to make welfare analyses more transparent, auditable, and decision-relevant. He has more than two decades of interdisciplinary research experience and has mentored researchers and delivered scientific workshops internationally. His current interests include comparative affective capacity, animal sentience, neglected animal groups, evidence synthesis, and the use of AI as an auditable instrument for scientific reasoning.