Animal Behavior Intelligence: Using Video Computing and AI to Better Understand Animals – Thomas J Nagel

What if we could give every animal a voice; not by teaching them to speak, but by learning to better understand what they are already telling us?

At Freedom, we believe technology should serve a simple purpose: help us save more lives and improve the lives of the animals we rescue.

One of the most promising areas we are exploring is Animal Behavior Intelligence—the use of video computing, artificial intelligence, and human expertise to better understand animal behavior. This is not about replacing people, it’s about giving people better information.

Animals Are Constantly Communicating

Animals communicate through movement, posture, facial expressions, vocalizations, proximity to other animals, interactions with people, feeding behavior, sleep patterns, play, avoidance, aggression, curiosity, and countless other behaviors.

Humans can observe these behaviors, but observation has limitations. A caregiver may see an animal for several hours a day. A veterinarian may see an animal during a clinical examination. An adoption coordinator may observe an animal during a short interaction.
But animals behave differently depending on their environment, the people around them, the presence of other animals, their health, stress level, and countless other variables.

What happens when we begin observing continuously?

That is where video computing becomes interesting.

From Video to Behavioral Data

Modern computer vision systems can analyze video and identify patterns in movement and interaction. Instead of simply recording hours of footage, a system could potentially transform portions of that video into structured behavioral information.

For example, an Animal Behavior Intelligence system could examine: Movement and activity levels, Rest and sleep patterns, Social interaction, Play behavior, Feeding behavior, Interaction with caregivers, Environmental responses, Vocalization patterns, Changes in posture or movement, or Potential indicators of stress or discomfort.

The objective is not to label an animal based on a single observation; the objective is to identify patterns over time.

The Power of Longitudinal Observation

One of the most important possibilities is the ability to compare behavior across time. Imagine an animal arriving at a shelter.

On Day One, the animal may be frightened and withdrawn.
On Day Seven, the animal begins interacting with caregivers.
On Day Thirty, the animal begins playing with other animals.
On Day Sixty, the animal actively seeks human interaction.

A traditional record might contain a few written observations: “Shy at intake.”, “Improving.”, “Friendly with staff.” Those observations are valuable. But a video-based system could potentially provide a much richer behavioral history. It could help us ask:

How quickly did the animal acclimate?
What environments produced positive behavioral changes?
Which interactions appeared stressful?
Which caregivers produced the strongest engagement?
How does the animal behave around other animals?

Over time, these observations can become a behavioral dataset. And datasets allow us to discover patterns.

Human Intelligence + Artificial Intelligence

Freedom does not envision Animal Behavior Intelligence as an autonomous decision-making system. Instead, we envision a partnership between human expertise and artificial intelligence.

Humans understand context, Veterinarians understand health, Animal behavior professionals understand behavioral science, Caregivers understand individual animals, and AI can help process enormous quantities of information.

Each has strengths the other does not, with a potential workflow that might look like this:


Video Observation → AI-Assisted Analysis → Human Review → Behavioral Profile → Care Planning → Placement Decision

The AI identifies patterns, Humans interpret those patterns within context, Veterinary professionals evaluate medical considerations, and ultimately, people make the decisions.

AI Should Assist—Not Diagnose. This distinction is extremely important, an AI system should not look at a video and declare: “This animal has a medical condition.” Nor should it automatically decide: “This animal belongs in this sanctuary.“

Those are decisions requiring qualified human judgment. Instead, the system might identify an unusual behavioral change and flag it for review: “Activity level appears significantly different from the animal’s historical baseline.“

That information could prompt a caregiver or veterinarian to investigate. The AI becomes an early-warning and decision-support system, rather than an authority.

Building Animal Behavioral Profile

Over time, an animal could develop a behavioral profile based on observations rather than assumptions. For example:

Behavioral Characteristics: Human socialization, Animal socialization, Activity level, Play behavior, Environmental sensitivity, Stress responses, Confidence, Curiosity, Independence and Adaptability.
Environmental Characteristics; Indoor/outdoor preferences, Noise sensitivity, Crowding tolerance, Climate considerations, Space requirements, and Compatibility with different environments
Social Characteristics: Compatibility with other animals, Preference for individual or group environments, or Caregiver interaction patterns

The result would not be a simplistic label such as “friendly” or “aggressive.” It would be a much more nuanced picture of an individual animal.

From Behavioral Intelligence to Better Placement

This is where Animal Behavior Intelligence connects directly to the broader Freedom model. We believe the future of animal placement should be based on compatibility, not simply availability. An animal may be adoptable, but that does not mean every home is the right home.

A sanctuary may have space, but that does not necessarily mean it is the right sanctuary.

A caregiver may have experience, but another caregiver may be a better behavioral match.

A future Freedom system could potentially combine behavioral information with other data such as:

Available capacity
Medical requirements
Climate
Social compatibility
Caregiver experience
Veterinary resources
Sanctuary characteristics
Transportation requirements
Historical placement outcomes
Environmental conditions

AI could then help identify potential matches. Humans would make the final decision. The goal would be to move from: “Where do we have an opening?” to: “Where does this animal have the best opportunity to thrive?“

Learning From Outcomes

Perhaps the most powerful part of the concept is that the system could learn from outcomes. Suppose hundreds or thousands of animals are eventually represented within the system. We could begin asking questions that are difficult to answer through individual observation alone.

Which behavioral characteristics correlate with successful adoption?
Which environmental conditions reduce stress?
Which types of placements have the highest long-term success?
How long does it typically take different animals to acclimate?
Which interventions produce measurable behavioral improvement?
What factors predict placement disruption?

The objective is not simply to collect data. The objective is to learn from experience. Every successful placement could teach us something. Every unsuccessful placement could teach us something. Every behavioral intervention could generate another piece of evidence.

Over time, the system could become smarter—not because the animals are being reduced to numbers, but because we are becoming better at understanding them.

Privacy, Ethics and Animal Welfare

Technology must have boundaries. Freedom believes any implementation of Animal Behavior Intelligence should be built around several principles:

Animal Welfare First, Technology must improve animal welfare rather than simply create interesting data.
Human Oversight, AI-generated observations should be reviewed by qualified people.
Veterinary Oversight, Potential health-related concerns should be evaluated by veterinary professionals.
Transparency, We should understand what the system is measuring, how conclusions are generated, and where uncertainty exists.
Privacy, Video systems must be designed responsibly to protect people as well as animals.
Continuous Evaluation, AI systems should be regularly tested for accuracy, bias, false positives, and unintended consequences.

Most importantly: The animal is not the dataset. The dataset exists to help us understand the animal.

A Different Way of Thinking About Technology

Freedom is not interested in using AI simply because AI is exciting. We are interested because animal welfare has an enormous information problem. There are millions of animals. There are limited caregivers. Limited shelters. Limited veterinary resources. Limited sanctuary capacity. Limited transportation resources. And limited time.

Yet every animal is an individual. Technology gives us an opportunity to connect those two realities. We can build systems capable of processing enormous amounts of information while still keeping the individual animal at the center of the process.

That is the promise of Animal Behavior Intelligence.

The Beginning, Not the End

Freedom is exploring what becomes possible when video computing, artificial intelligence, behavioral science, veterinary knowledge, and human compassion are brought together.

We do not expect to have all the answers. We expect to experiment. We expect to learn. We expect some ideas to work better than others. And we expect the system to evolve as we gather evidence.

The ultimate goal is simple: Understand animals better. Care for them better. Place them better. Save more lives.

Technology is not the mission, the animals are the mission; AI is simply one more tool we can use to serve them.