Endangered species

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Endangered_species

Golden lion tamarin, an endemic and one of the endangered species saved from extinction in Brazil

A visual representation of the declining percentages of endangered plant and animal species in Brazil from 2014 to 2022. The sidebar graph highlights the contrast between plant and animal conservation efforts.

The California condor is a critically endangered species. Note the wing tags used for population monitoring.

An endangered species is a species that is very likely to become extinct in the near future, either worldwide or in a particular region. Endangered species may be at risk due to factors such as habitat loss, poaching, invasive species, and climate change. The International Union for Conservation of Nature (IUCN) Red List lists the global conservation status of many species, and various other agencies assess the status of species within particular areas. Many nations have laws that protect conservation-reliant species which, for example, forbid hunting or harvesting, restrict land development, or create protected areas. Some endangered species are the target of extensive conservation efforts such as captive breeding and habitat restoration.

Human activity is a significant factor in causing species to become endangered.

Conservation status

Main article: Conservation status

Photo of Pusa hispida saimensis, also known as Saimaa ringed seal, from 1956. Living only in Lake Saimaa, Finland, Saimaa ringed seals are among the most endangered seals in the world, having a total population of only about 400 individuals.

The conservation status of a species indicates the likelihood that it will become extinct. Multiple factors are considered when assessing the status of a species; e.g., such statistics as the number remaining, the overall increase or decrease in the population over time, breeding success rates, or known threats. The IUCN Red List of Threatened Species is the best-known worldwide conservation status listing and ranking system.

IUCN Red List of Threatened Species The IUCN Red List of Threatened Species by the International Union for Conservation of Nature is the best known worldwide conservation status listing and ranking system. Species are classified by the IUCN Red List into nine groups set through criteria such as rate of decline, population size, area of geographic distribution, and degree of population and distribution fragmentation.

Also included are species that have gone extinct since 1500 CE. When discussing the IUCN Red List, the official term "threatened" is a grouping of three categories: critically endangered, endangered, and vulnerable.

• Extinct (EX) – There are no known living individuals
• Extinct in the wild (EW) – Known only to survive in captivity, or as a naturalized population outside its historic range
• Critically Endangered (CR) – Highest risk of extinction in the wild
• Endangered (EN) – Higher risk of extinction in the wild
• Vulnerable (VU) – High risk of extinction in the wild
• Near Threatened (NT) – Likely to become endangered in the near future
• Conservation Dependent (CD) – Low risk; is conserved to prevent being near threatened, certain events may lead it to being a higher risk level
• Least concern (LC) – Very low risk; does not qualify for a higher risk category and not likely to be threatened in the near future. Widespread and abundant taxa are included in this category.
• Data deficient (DD) – Not enough data to make an assessment of its risk of extinction
• Not evaluated (NE) – Has not yet been evaluated against the criteria.

Over 50% of the world's species are estimated to be at risk of extinction, but the frontiers between categories such as "endangered", "rare", or "locally extinct" species are often difficult to draw given the general paucity of data on most of these species. This is notably the case in the world's oceans, where distances are vast and endangered species not seen for decades may go extinct unnoticed.

Internationally, 195 countries have signed an accord to create Biodiversity Action Plans that will protect endangered and other threatened species. In the United States, such plans are usually called Species Recovery Plans.

IUCN Red List

Main article: Endangered species (IUCN status)

The Siberian tiger is an Endangered (EN) tiger subspecies. Three tiger subspecies are already extinct (see List of carnivorans by population).

Blue-throated macaw, a critically endangered bird

Brown spider monkey, a critically endangered mammal

Siamese crocodile, a critically endangered reptile

American burying beetle, an endangered species of insect

Kemp's ridley sea turtle, a critically endangered reptile

The Mexican wolf, the most endangered subspecies of the North American grey wolf. Approximately 143 are living in the wild.

Though labeled a list, the IUCN Red List is a system of assessing the global conservation status of species that includes "Data Deficient" (DD) species – species for which more data and assessment is required before their situation may be determined – as well species comprehensively assessed by the IUCN's species assessment process. The species under the index include: mammals, birds, amphibians, cycads, and corals. Those species of "Near Threatened" (NT) and "Least Concern" (LC) status have been assessed and found to have relatively robust and healthy populations, though these may be in decline. Unlike their more general use elsewhere, the List uses the terms "endangered species" and "threatened species" with particular meanings: "Endangered" (EN) species lie between "Vulnerable" (VU) and "Critically Endangered" (CR) species. In 2012, the IUCN Red List listed 3,079 animal and 2,655 plant species as endangered (EN) worldwide.

In Brazil

Brazil is one of the most biodiverse countries in the world, if not the most. It houses not only the Amazon forest but the Atlantic forest, the savanna-like Cerrado among other biomes. Due to the high density of some of its well-preserved rainforests, wildlife trafficking, which along with deforestation is one of the biggest endangerment drivers in Brazil, has become a challenge. Brazil has a broad legal system meant to protect the environment, including its Constitution, as well as several federal, state and local government agencies tasked with protecting the fauna and flora, fining individuals or companies linked to environmental crimes and confiscating illegally taken wildlife. Though such agencies can collect their data, each system operates relatively on its own when it comes to wildlife trafficking. However, both the agencies and the NGO's working in Brazil agree that the birds account for about 80% of trafficked species in the country.

The relation between wildlife smuggling, other environment crimes under the Brazilian law such as deforestation, and endangered species is particularly intricate and troubling since the rarer the animal or plant gets the most targeted and valuable they become in the black market, which leads to more endangered species in its turn.

Additionally, some environment experts and scientists point to the disbanding of environment agencies and the repeal of laws in Brazil under the presidency of Jair Bolsonaro as one of the reasons behind a surge in the number of endangered species. In one occasion during his presidency some fines totaling US$3.1 billion on environment criminals were revoked and at least one fine (related to illegal fishing) imposed on Bolsonaro himself was cancelled and the agent who fined him was demoted.

In the past, Brazil has successfully saved the endemic golden lion tamarin from extinction. Massive campaigns to raise awareness among people by NGO's and governments, which included printing depictions of the golden lion tamarin in the 20 reais Brazilian banknotes (still in circulation), are credited with getting the species out of the critically endangered animals list.

In the United States

There is data from the United States that shows a correlation between human populations and threatened and endangered species. Using species data from the Database on the Economics and Management of Endangered Species database and the period that the Endangered Species Act (ESA) has been in existence, 1970 to 1997, a table was created that suggests a positive relationship between human activity and species endangerment.

Effect of climate change on endangered species

Carbon dioxide in Earth's atmosphere is asserted to be one of the leading causes of animal endangerment. According to the US National Park Service:

If we can sufficiently reduce greenhouse gas emissions, many of them will still have a chance to survive and recover. NASA scientist James Hanson has warned that in order to maintain a climate similar to that under which human civilization developed and similar to that which so many organisms are adapted, we need to quickly reduce the carbon dioxide in our atmosphere to 350 parts per million (ppm). Before the industrial revolution, atmospheric carbon dioxide levels rarely rose above 280 ppm; during the 2014 calendar year, carbon dioxide levels fluctuated between 395 and 402 ppm.

A proportional symbol map of each state's endangered species count

Endangered Species Act

Under the Endangered Species Act of 1973 in the United States, species may be listed as "endangered" or "threatened". The Salt Creek tiger beetle is an example of an endangered subspecies protected under the ESA. The US Fish and Wildlife Service, as well as the National Marine Fisheries Service are held responsible for classifying and protecting endangered species. They are also responsible for adding a particular species to the list, which can be a long, controversial process. The act adds numerous other provisions as well, including the designation of endangered species' critical habitats and a requirement to develop a recovery plan for the species. On top of this, the ESA protects endangered species from government policies; for instance, federal agencies must consult the Fish and Wildlife Service or the National Marine Fisheries Service before executing any action that may jeopardize threatened species or their habitats.

Some endangered species laws are controversial. Typical areas of controversy include criteria for placing a species on the endangered species list and rules for removing a species from the list once its population has recovered. Whether restrictions on land development constitute a "taking" of land by the government; the related question of whether private landowners should be compensated for the loss of uses of their areas; and obtaining reasonable exceptions to protection laws. Also lobbying from hunters and various industries like the petroleum industry, construction industry, and logging, has been an obstacle in establishing endangered species laws.

The Bush administration lifted a policy that required federal officials to consult a wildlife expert before taking actions that could damage endangered species. Under the Obama administration, this policy was reinstated.

Being listed as an endangered species can have an indirect negative effect since the perceived increase in rarity could make a species more desirable for collectors and poachers. This effect is potentially reducible, such as in China where commercially farmed turtles may be reducing some of the pressure to poach endangered species.

Another problem with the listing of species is its effect of inciting the use of the "shoot, shovel, and shut-up" method of clearing endangered species from an area of land. Some landowners currently may perceive a diminution in value for their land after finding an endangered animal on it. They have allegedly opted to kill and bury the animals or destroy habitat silently. Thus removing the problem from their land, but at the same time further reducing the population of an endangered species. The effectiveness of the ESA– which coined the term "endangered species"– has been questioned by business advocacy groups and their publications but is nevertheless widely recognized by wildlife scientists who work with the species as an effective recovery tool. Nineteen species have been delisted and recovered and 93% of listed species in the north eastern United States have a recovering or stable population.

Currently, 1,556 endangered species are under protection by government law. This approximation, however, does not take into consideration the species threatened with endangerment that are not included under the protection of laws like the Endangered Species Act. According to NatureServe's global conservation status, approximately thirteen percent of vertebrates (excluding marine fish), seventeen percent of vascular plants, and six to eighteen percent of fungi are considered imperiled. Thus, in total, between seven and eighteen percent of the United States' known animals, fungi and plants are near extinction. This total is substantially more than the number of species protected in the United States under the Endangered Species Act.

Bald eagle

American bison

Ever since humankind began hunting to preserve itself, over-hunting and fishing have been a large and dangerous problem. Of all the species who became extinct due to interference from humankind, the dodo, passenger pigeon, great auk, Tasmanian tiger and Steller's sea cow are some of the more well known examples; with the bald eagle, grizzly bear, American bison, Eastern timber wolf, West African black rhinoceros, ivory belled woodpecker and sea turtle having been poached to near-extinction. Many began as food sources seen as necessary for survival but became the target of sport. However, due to major efforts to prevent extinction, the bald eagle, or Haliaeetus leucocephalus is now under the category of Least Concern on the red list.

A present-day example of the over-hunting of a species can be seen in the oceans as populations of certain whales have been greatly reduced. Large whales like the blue whale, bowhead whale, finback whale, gray whale, sperm whale, and humpback whale are some of the eight whales which are currently still included on the Endangered Species List. Actions have been taken to attempt a reduction in whaling and increase population sizes. The actions include prohibiting all whaling in United States waters, the formation of the CITES treaty which protects all whales, along with the formation of the International Whaling Commission (IWC). But even though all of these movements have been put in place, countries such as Japan continue to hunt and harvest whales under the claim of "scientific purposes". Over-hunting, climatic change and habitat loss leads in landing species in endangered species list. It could mean that extinction rates could increase to a large extent in the future.

In Canada

Endangered species are addressed through Canada's Species at Risk Act. A species is deemed threatened or endangered when it is on the verge of extinction or extirpation. Once a species is deemed threatened or endangered, the Act requires that a recovery plan to be developed that indicates how to stop or reverse the species' population decline. As of 2024, there are 339 Canadian species classified as endangered by the IUCN Red List.

In India

The World Wide Fund-India raises concern in the longevity of the following animal species: the Red Panda, the Bengal Tiger, the Ganges River Dolphin, the Asian Elephant.

India has had significantly high rates of poaching and animal trafficking, threatening many animal species there. Since 1987, over half of trafficked tiger seizures have been within India. The government signed the Wildlife Protection Act and also joined the Convention on the International Trade in 1976, to prevent poaching from harming its wildlife.

Introduced species

Main article: Invasive species

The introduction of non-indigenous species to an area can disrupt the ecosystem to such an extent that native species become endangered. Such introductions may be termed introduced or invasive species. In some cases, the invasive species compete with the native species for food or prey on the natives. In other cases, a stable ecological balance may be upset by predation or other causes leading to unexpected species decline. New species may also carry diseases to which the native species have no exposure or resistance. The continent of Oceania is under much more pressure, as the invasive birds and mammals there have no natural predators. Notably, the threat posed by introduces species may be greater than anticipated due to the threats on endangered species that have not yet had a significant effect, but likely will in the future.

Climate change

See also: Effects of climate change

The World Wildlife Fund (WWF) emphasizes that our planet is warming at a rate faster than any time in the past 10,000 years, necessitating species to adapt to new climate patterns, such as variations in rainfall and longer, warmer summers. For example, the U.S. Fish & Wildlife Service highlighted efforts to understand and mitigate the impact of climate change on species through scientific research, modeling, and conservation actions. This includes evaluating the current condition of species, their genetic variation, and how changes in their environment may affect their survival.

The International Union for Conservation of Nature (IUCN) reports that the approximately 1 °C rise in mean global temperature due to human activities is causing serious impacts on species, including changes in abundance, genetic composition, behavior, and survival. The IUCN stresses the importance of environmental policies aimed at reducing CO ₂ emissions to lessen the impact of climate change on species. Tools like the IUCN Red List and guidelines for assessing species' vulnerability to climate change are vital for conservation efforts.

Image showing one of many fish kills (in this case Tilapia) induced by effects of climate change.

In addition, climate change can lead to species decreasing in areas where they once thrived, by being forced to migrate or even going extinct from inhospitable conditions, invasive species, and fragmentation. A study cited by WWF found that one in six species is at risk of extinction due to climate change if no action is taken. The phenomenon of species shifting their ranges in response to changing climates, finding new or shrinking habitats, illustrates the direct impact of global warming on biodiversity. Another major concern is rising ocean acidity caused from excess CO₂ in the atmosphere. This creates acidic conditions in the ocean which creates an inhospitable environment for fish, plants, and other keystone species such as coral reefs

For example, the Emperor Penguins, which rely on Antarctic sea ice for breeding, shelter, and food, are directly threatened by the melting of ice sheets. Similarly, the Mount Rainier white-tailed ptarmigan, adapted to alpine mountaintops, faces habitat loss due to climate changes in snowfall patterns and rising temperatures.

Another example is in the case of the Salton Sea in California. This area is a critical habitat for many endangered and watched species, as well as many migratory birds. Due to environmental shifts from climate change and the addition of agriculture in the surrounding plains, the system has become almost irreparably damaged. The warming temperatures has caused mass evaporation, leaving the Sea much more saline and with much more exposed playa. This not only damages air quality but also has caused fish kills to accumulate as shown pictured below. This has made the system inhospitable to the birds and endangered species relying upon it.

Conservation

The dhole, Asia's most endangered top predator, is on the edge of extinction.

Captive breeding

Main article: Captive breeding

Captive breeding is the process of breeding rare or endangered species in human controlled environments with restricted settings, such as wildlife reserves, zoos, and other conservation facilities. Captive breeding is meant to save species from extinction and so stabilise the population of the species that it will not disappear.

This technique has worked for many species for some time, with probably the oldest known such instances of captive mating being attributed to menageries of European and Asian rulers, an example being the Père David's deer. However, captive breeding techniques are usually difficult to implement for such highly mobile species as some migratory birds (e.g. cranes) and fishes (e.g. hilsa). Additionally, if the captive breeding population is too small, then inbreeding may occur due to a reduced gene pool and reduce resistance.

"Endangered" in relation to "threatened" under the ESA

In 1981, the Association of Zoos and Aquariums (AZA) created a Species Survival Plan (SSP) to help preserve specific endangered and threatened species through captive breeding. With over 450 SSP Plans, some endangered species are covered by the AZA with plans to cover population management goals and recommendations for breeding for a diverse and healthy population, created by Taxon Advisory Groups. These programs are commonly created as a last resort effort. SSP Programs regularly participate in species recovery, veterinary care for wildlife disease outbreaks, and some other wildlife conservation efforts. The AZA's Species Survival Plan also has breeding and transfer programs, both within and outside of AZA – certified zoos and aquariums. Some animals that are part of SSP programs are giant pandas, lowland gorillas, and California condors.

Private farming

Black rhino

Southern bluefin tuna

Whereas poaching substantially reduces endangered animal populations, legal, for-profit, private farming does the opposite. It has substantially increased the populations of the southern black rhinoceros and southern white rhinoceros. Richard Emslie, a scientific officer at the IUCN, said of such programs, "Effective law enforcement has become much easier now that the animals are largely privately owned... We have been able to bring local communities into conservation programs. There are increasingly strong economic incentives attached to looking after rhinos rather than simply poaching: from Eco-tourism or selling them on for a profit. So many owners are keeping them secure. The private sector has been key to helping our work."

Conservation experts view the effect of China's turtle farming on the wild turtle populations of China and South-Eastern Asia– many of which are endangered– as "poorly understood". Although they commend the gradual replacement of turtles caught wild with farm-raised turtles in the marketplace– the percentage of farm-raised individuals in the "visible" trade grew from around 30% in 2000 to around 70% in 2007– they worry that many wild animals are caught to provide farmers with breeding stock. The conservation expert Peter Paul van Dijk noted that turtle farmers often believe that animals caught wild are superior breeding stock. Turtle farmers may, therefore, seek and catch the last remaining wild specimens of some endangered turtle species.

In 2015, researchers in Australia managed to coax southern bluefin tuna to breed in landlocked tanks, raising the possibility that fish farming may be able to save the species from overfishing.

Conservation baselines and proposed paradigm shifts

Some scientists have proposed that redefining the baseline for assessing recovery of an endangered species may be necessary as a last resort in the face of extinction, as the increased flexibility provides more realistic recovery goals in the face of extinction. This strategy, however, needs to be pursued with significant care and is typically not considered before attempting recovery within the natural range when possible, due to carrying additional risks and potential unintended consequences. These changes can come in many forms, including relocating a species to a new and better suited habitat, allowing the species to breed with other similar species, or accepting that the modern stable population may be less than it was in the past. An example of these strategies being put into action is through the Florida panther. To save the species, cougars from Texas were introduced, turning a large subset of the panther population into panther–cougar hybrids that were well suited to the environment, facilitating the recovery of the population.

Success stories

Hawaiian monk seal

Hawaiian monk seals are one of the most endangered seal species in the world. Conservation initiatives have focused on mitigating human-seal conflicts, rehabilitating injured seals, and extensive monitoring to ensure their survival. These efforts have led to a gradual increase in their population.

American bald eagle

Once on the brink of extinction in the contiguous United States with only 417 known nesting pairs in 1963 due to pesticide use and habitat destruction, the bald eagle population has made a remarkable recovery. By 2020, the number of nesting pairs had surged to 71,400. Major strategies employed to help raise populations included the provision of eaglets from stable regions to more threatened regions, and major land purchases to act as sanctuaries for the eagles. Thanks to habitat protection, legal protection, and DDT ban efforts, the bald eagle was removed from the list of threatened and endangered species.

Gray wolf

Starting in 1995 and 1996, 31 gray wolves from western Canada were relocated to Yellowstone, where they were temporarily kept in acclimation pens before being released into the wild. This careful reintroduction aimed to restore a key predator to the ecosystem, which had profound effects on the park's wildlife dynamics. After being nearly eradicated in the lower 48 states by the early 20th century, reintroduction and protective measures have allowed their populations to rebound significantly. By 2017, gray wolves were delisted in Montana, Idaho, and Wyoming, indicating a recovery to a point where they were no longer considered endangered in these areas.^[

Channel Island fox

Beginning in 1999, the Channel Islands National Park launched an ambitious recovery program for the island fox, incorporating several strategies: captive breeding and reintroduction, removal of predatory golden eagles, re-establishment of bald eagles, and eradication of non-native ungulates. The U.S. Department of the Interior officially recognized the recovery as the fastest for any Endangered Species Act-listed mammal in the U.S., announcing the delisting of three island fox subspecies in 2016. This recovery, from near extinction in the late 1990s to robust populations by the mid-2010s, underscores the power of partnership-driven conservation.

Purple Emperor butterfly

The Purple Emperor, native to the UK, has seen a significant comeback in recent years, expanding from southern England to a large portion of the British Isles. Conservation groups have aided this process through the planting of goat willows in many forests, an important food source for the butterfly larvae. The territorial nature of the species makes this process more complicated, requiring many willow trees be planted over a large area, but groups such as Butterfly Conservation have made the recovery process possible.

Predation problem

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Predation_problem 

A snowy owl carries a killed American black duck

The predation problem or predation argument refers to the consideration of the harms experienced by animals due to predation as a moral problem, that humans may or may not have an obligation to work towards preventing. Discourse on this topic has, by and large, been held within the disciplines of animal and environmental ethics. The issue has particularly been discussed in relation to animal rights and wild animal suffering. Some critics have considered an obligation to prevent predation as untenable or absurd and have used the position as a reductio ad absurdum to reject the concept of animal rights altogether. Others have criticized any obligation implied by the animal rights position as environmentally harmful.

Responses from animal ethicists and rights advocates have been varied. Some have rejected the claim that animal rights as a position implies that we are obligated to prevent predation, while others have argued that the animal rights position does imply that predation is something that we should try to avert. Others have asserted that it is not something that we should do anything about now due to the risk that we could inadvertently cause significant harm, but that it is something that we may be able to effectively take action on in the future with improved knowledge and technologies.

Historical views

Problem of evil

See also: Problem of evil § Problem of evil and animal suffering, and Evolutionary theodicy

Predation has historically been viewed as a natural evil within the context of the problem of evil and has been considered a moral concern for Christians who have engaged with theodicy. Natural evils have been sometimes thought of as something that humans should work towards alleviating, or as part of a greater good which justifies the existence of this type of evil. Thomas Aquinas advocated the latter view, arguing that "defects" in nature such as predation led to the "good of another, or even to the universal good" and that if "all evil were prevented, much good would be absent from the universe". Within Christian and Hebrew Scripture, there are several prophecies which describe a future Heaven or Earth where predation is no longer a feature of nature, including Isaiah's prophecy that "[t]he wolf shall live with the lamb, the leopard shall lie down with the kid, the calf and the lion and the fatling together, and a little child shall lead them."

In his notebooks (written between 1487 and 1505), Leonardo da Vinci suggested that natural suffering and death, including plagues and predation, are necessary for maintaining balance and renewal in the world, even if they seem unjust or cruel. David Hume made several observations about predation and suffering experienced by wild animals in Dialogues Concerning Natural Religion (1779), stating that the "stronger prey upon the weaker, and keep them in perpetual terror and anxiety".

William Paley, in Natural Theology, described predation as being the most challenging of God's work to establish the utility of, nevertheless, he defended predation as the means to deal with the potentially catastrophic effects of animals producing more offspring than can possibly survive.

The debate around predation and the problem of evil was significantly increased by the popularization of Charles Darwin's theory of natural selection. Some earlier Christians argued that violence in nature was a result of the fall of man, but evidence that predation has existed for millions of years before the evolution of humans and the concept of sin, indicates that while life has existed, there has never been a time when nature has been free from violence. Darwin himself questioned how the fact that the Ichneumonidae prey on the bodies of living caterpillars could be reconciled with the idea of an omnibenevolent God.

Criticism of moral judgements towards predatory animals

Plutarch criticised the labelling of carnivorous animals such as lions, tigers and snakes as barbarous because for them killing is a necessity while for humans who can live off of "nature's beneficent fruits" killing is a "luxury and crime".

The writer Edward Augustus Kendall discussed predation in his book of moral fables The Canary Bird (1799), in which he argued that predatory behavior by animals should not be judged by human moral standards and that "a prejudice against particular creatures, for fancied acts of cruelty is absurd".

Philosophical pessimism

Giacomo Leopardi, the Italian poet and philosopher, in Operette morali (1827) engaged in a dialogue with Nature in "Dialogue between Nature and an Icelander", which uses the inevitability of predation—such as a squirrel fleeing from a rattlesnake, only to run into the snake's open mouth—as a moral indictment on nature's cannibalism of its own offspring. The inevitability of such cycles of destruction and creation was a cause for Leopardi's philosophical pessimism. In Zibaldone, published posthumously in 1898, Leopardi argued that predation is the ultimate indication of the evil design of nature.

Similar to Leopardi, the German philosopher Arthur Schopenhauer, in 1851, used the pain experienced by an animal being devoured by another as a refutation against the idea that the "pleasure in the world outweighs the pain".

Animal rights

Lewis Gompertz, an early animal rights advocate, and one of the first contemporary authors to address the problem of wild animal suffering, in the fifth chapter of his 1824 book Moral Inquiries on the Situation of Man and of Brutes, engaged in a dialogue, in which he asserted that animals devouring each other can be judged as wrong by the rules that we use to govern human lives and stated that "should I witness the attempt in any animal of destroying another, I would endeavour to frustrate it; though this might probably be wrong." He went on to argue that the extinction of carnivorous species would not be bad, claiming that the species of one animal is not more important than an equal number of another and that it would be possible for some carnivorous animals, like wolves, to instead sustain themselves on vegetables.

The American zoologist and animal rights philosopher J. Howard Moore in the pamphlet Why I Am a Vegetarian, published in 1895, described the carnivora as "relentless brutes", whose existence is a travesty for ethics, justice and mercy. In Better-World Philosophy (1899), Moore argued that carnivorousness was the result of excessive egoism, a product of natural selection, stating "Life riots on life—tooth and talon, beak and paw". He went on to claim that the irredeemable nature of carnivorous species meant that they could not be reconciled with each other in his ideal arrangement of the universe, which he called a "Confederation of the Consciousnesses". In The New Ethics (1907), Moore labelled carnivorous species as "criminal" races whose "existence is a continual menace to the peace and well-being of the world" because the "fullness of their lives is dependent upon the emptiness and destruction of others".

In 1903, the Scottish philosopher David G. Ritchie in response to Henry S. Salt's 1892 book Animals' Rights, claimed that giving animals rights would imply that we must "protect the weak among them against the strong" and to achieve this, carnivorous animals should be put to death or slowly starved by "permanent captivity and vegetarian diet". He considered this proposal absurd, stating that the "declaration of the rights of every creeping thing [is] to remain a mere hypocritical formula to gratify pug-loving sentimentalists".

Contemporary views

Animal ethics

In 1973, Australian philosopher Peter Singer argued that if humans were to try to prevent predation, such as from stopping lions killing gazelles, that it would likely increase the "net amount of animal suffering", but asserted that if hypothetically we could reduce suffering in the long-term, then it would be right to intervene.

The English philosopher Stephen R. L. Clark's "The Rights of Wild Things" (1979) is considered to be one of the first ethics papers to explicitly engage with predation as a problem. In the paper, Clark argues that the concept that humans are obligated to aid animals against predators is not absurd, but that it follows only in the abstract, not in practice.

Animal rights philosopher, Tom Regan in his 1983 book, The Case for Animal Rights, argued that humans have no obligation to prevent predation because carnivorous animals are not moral agents and as a result cannot violate the rights of the animals that they predate. Along these lines, Julius Kapembwa argues that "intervention in predation is neither required nor permitted by animal rights theory".

Steve Sapontzis, in his 1984 paper "Predation" argues against the idea that the problem of predation is a reductio ad absurdum for animal rights, instead, he claims that if we accept the view that we have an obligation to reduce avoidable animal suffering, then predation is something that we should work towards preventing if we can do so without inflicting greater suffering. Sapontzis concludes that whether humans choose to fulfil this particular obligation, or attempt to reduce other forms of avoidable suffering, is a question of where humans can do the most good.

In a 2003 paper, the economist Tyler Cowen advocates, from a utility, rights and holistic perspective, for the policing of nature to reduce the predatory activity of certain animals to help their victims.

The transhumanist philosopher David Pearce, in his 2009 essay, "Reprogramming Predators", claims that predation is an immense source of suffering in the world and that a "biosphere without suffering is technically feasible". He argues for the phased extinction of carnivorous species using immunocontraceptives or "reprogramming" them using gene editing so that their descendants become herbivores. Pearce lists and argues against a number of justifications used by people who think that suffering caused by predation does not matter and that it should be conserved in its current state, including a "television-based conception of the living world", "[s]elective realism" and "[a]daptive empathy deficits".

In 2010, Jeff McMahan published "The Meat Eaters", an op-ed for the New York Times on predation as a moral issue, in which he argued that preventing the massive amounts of suffering and death caused by predation would be a good thing and that the extinction of carnivorous species could be instrumentally good if this could be achieved without inflicting "ecological upheaval involving more harm than would be prevented by the end of predation". McMahan received a number of objections to his arguments and responded to these in another op-ed published in the same year, "Predators: A Response". He later published his arguments as a chapter titled "The Moral Problem of Predation", in the 2015 book Philosophy Comes to Dinner.

Peter Vallentyne argues that it is permissible for humans to intervene to help prey in limited ways, if the cost to humans is minimal, but that we should not eliminate predators. In the same way that we aid humans in need, when the cost to humans is minimal, humans might help wild animals in limited circumstances.

Martha Nussbaum asserts that the predation problem and what should be done to solve it should be the subject of serious discussion, also arguing that there should be research into future solutions. Nussbaum draws attention to a need to convince people that predation is a problem and to challenge the common conception of predation as exciting and enthralling, which she believes has a negative impact on human culture. She goes on to challenge the idea of animals, who are preyed upon, as existing to be food for other animals, rather than being made to live for their own lives. Nussbaum concludes that humans, who have extensive control over animal lives and habitats, need to face up to their responsibilities towards wild animals and work towards their flourishing, rather than harming them.

Some ethicists have made concrete proposals for reducing or preventing predation, including stopping the reintroduction of predators in locations where they have previously gone extinct, and removing predators from wild areas.

Environmental ethics

In 1984, the British ecologist Felicity A. Huntingford published "Some ethical issues raised by studies of predation and aggression", in which she discusses ethical issues and implications regarding the staging of artificial encounters for studies of predator-prey interactions.

In the context of ecology, predation is widely regarded as playing an important and necessary role in ecosystems. This has led some writers, such as Michael Pollan, to reject predation as being a moral problem at all, stating "predation is not a matter of morality or politics; it, also, is a matter of symbiosis". Under Aldo Leopold's land ethic, native predators, as components of biotic communities, are considered important to conserve.

The environmental philosopher J. Baird Callicott asserts that the implication of animal rights theory, namely that we should protect animals from predators, would "[n]ot only [result in] the (humane) eradication of predators destroy the community, it would destroy the species which are the intended beneficiaries of this misplaced morality. Many prey species depend upon predators to optimize their populations." Holmes Rolston III views predation as an essential natural process and driver of evolution, that is a "sad good" to be respected and valued. Ty Raterman, an environmentalist, has argued that predation is something that can be lamented without implying that we have an obligation to prevent it.

The environmental ethicist William Lynn has argued that from a welfare perspective predation "is necessary for the well-being of predators and prey" and essential for the maintenance of the integrity of the ecological communities. Larry Rasmussen, a Christian environmental ethicist, has argued that predation is "not a pattern of morality we praise and advocate".

Other uses of the term

"Predation problem" can also refer to the predation of animals who belong to species considered valuable to humans for economic reasons or conservation, such as domestic sheep predation by coyotes, farmed salmon predation by seals, the predation of animals who are hunted for sport or food and cat predation of wild animals; culling or removal of predatory animals may be carried out to reduce such incidents.

Artificial intelligence in mental health

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Artificial_intelligence_in_mental_health 

Artificial intelligence in mental health refers to the application of artificial intelligence (AI), computational technologies and algorithms to support the understanding, diagnosis, and treatment of mental health disorders. In the context of mental health, AI is considered a component of digital healthcare, with the objective of improving accessibility and accuracy and addressing the growing prevalence of mental health concerns. Applications of AI in this field include the identification and diagnosis of mental disorders, analysis of electronic health records, development of personalized treatment plans, and analytics for suicide prevention. There is also research into, and private companies offering, AI therapists that provide talk therapies such as cognitive behavioral therapy. Despite its many potential benefits, the implementation of AI in mental healthcare presents significant challenges and ethical considerations, and its adoption remains limited as researchers and practitioners work to address existing barriers. There are concerns over data privacy and training data diversity.

Background

In 2019, 1 in every 8 people, or 970 million people around the world were living with a mental disorder, with anxiety and depressive disorders being the most common. In 2020, the number of people living with anxiety and depressive disorders rose significantly because of the COVID-19 pandemic. Additionally, the prevalence of mental health and addiction disorders exhibits a nearly equal distribution across genders, emphasizing the widespread nature of the issue.

The use of AI in mental health aims to support responsive and sustainable interventions against the global challenge posed by mental health disorders. Some issues common to the mental health industry are provider shortages, inefficient diagnoses, and ineffective treatments. The global market for AI-driven mental health applications is projected to grow significantly, with estimates suggesting an increase from US$0.92 billion in 2023 to US$14.89 billion by 2033. This growth indicates a growing interest in AI's ability to address critical challenges in mental healthcare provision through the development and implementation of innovative solutions.

AI-driven approaches

Several AI technologies, including machine learning (ML), natural language processing (NLP), deep learning (DL), computer vision (CV) and LLMs and generative AI are currently applied in various mental health contexts. These technologies enable early detection of mental health conditions, personalized treatment recommendations, and real-time monitoring of patient well-being.

Machine learning

Machine learning is an AI technique that enables computers to identify patterns in large datasets and make predictions based on those patterns. Unlike traditional medical research, which begins with a hypothesis, ML models analyze existing data to uncover correlations and develop predictive algorithms. ML in psychiatry is limited by data availability and quality. Many psychiatric diagnoses rely on subjective assessments, interviews, and behavioral observations, making structured data collection difficult. Some researchers have applied transfer learning, a technique that adapts ML models trained in other fields, to overcome these challenges in mental health applications.

Deep learning

Deep learning, a subset of ML, involves neural networks with many layers of neurons, that can grasp complex patterns, similarly to human brains. It is particularly useful for identifying subtle patterns in speech, imaging, and physiological data. Deep learning techniques have been applied in neuroimaging research to identify abnormalities in brain scans associated with conditions such as schizophrenia, depression, and PTSD. However, deep learning models require extensive, high-quality datasets to function effectively. The limited availability of large, diverse mental health datasets poses a challenge, as patient privacy regulations restrict access to medical records. Additionally, deep learning models often operate as "black boxes", meaning their decision-making processes are not easily interpretable by clinicians, raising concerns about transparency and clinical trust.

Natural language processing

Natural language processing allows AI systems to analyze and interpret human language, including speech, text, and tone of voice. In mental health, NLP is used to extract meaningful insights from conversations, clinical notes, and patient-reported symptoms. NLP can assess sentiment, speech patterns, and linguistic cues to detect signs of mental distress. This is crucial because many of the diagnoses and DSM-5 mental health disorders are diagnosed via speech in doctor-patient interviews, utilizing the clinician's skill for behavioral pattern recognition and translating it into medically relevant information to be documented and used for diagnoses. As research continues, NLP models must address ethical concerns related to patient privacy, consent, and potential biases in language interpretation.

Advancements in NLP such as sentiment analysis identifies distinctions in tone and speech to detect anxiety and depression. "Woebot", uses sentiment analysis to scrutinize and detect patterns for depression or despair and suggests professional help to patients. Similarly, "Cogito", an AI platform uses voice analysis to find changes in pitch and loudness to identify symptoms of depression or anxiety. The application of NLP can contribute to early diagnosis and improved treatment strategies.

Computer vision

Computer vision enables AI to analyze visual data, such as facial expressions, body language, and micro expressions, to assess emotional and psychological states. This technology is increasingly used in mental health research to detect signs of depression, anxiety, and PTSD through facial analysis. Computer vision tools have been explored for their ability to detect nonverbal cues, such as hesitation or changes in eye contact, which may correlate with emotional distress. Despite its potential, computer vision in mental health raises ethical and accuracy concerns. Facial recognition algorithms can be influenced by cultural and racial biases, leading to potential misinterpretations of emotional expressions. Additionally, concerns about informed consent and data privacy must be addressed before widespread clinical adoption.

LLMs and generative AI

Research studies and social media posts indicate that some individuals seek therapeutic or emotional support from LLMs. A survey in early 2025 by Sentio University found that 48.7 percent of 499 U.S. adults with self-reported mental health conditions who used LLMs had turned to them for support with anxiety, depression, loneliness, or related issues. LLMs can offer lower-cost and increased accessibility compared to traditional mental health services. LLMs are known to generate hallucinations, which are plausible but inaccurate statements that may mislead users in sensitive contexts. Additional research has found that LLMs can display stigmatizing responses or inappropriately validate maladaptive thoughts, underscoring limits in replicating the judgment and relational capacities of trained clinicians. Crisis evaluations suggest that some systems do not consistently perform essential safety tasks, including suicide risk assessment or referral to appropriate services. Research on empathy expressed by LLMs is mixed, with a systematic review reporting that in some studies their responses are rated as more empathic than those of clinicians, and other work in medical ethics warning that such systems lack genuine emotional intelligence and can reproduce inequities in health care.

Applications

Diagnosis

AI with the use of NLP and ML can be used to help diagnose individuals with mental health disorders. It can be used to differentiate closely similar disorders based on their initial presentation to inform timely treatment before disease progression. For example, it may be able to differentiate unipolar from bipolar depression by analyzing imaging and medical scans. AI can examine different biomarkers to help determine not only the disorder a patient may have, but the type and level of care needed as well. AI also has the potential to identify novel diseases that were overlooked due to the heterogeneity of presentation of a single disorder. Doctors may overlook the presentation of a disorder because while many people get diagnosed with depression, that depression may take on different forms and be enacted in different behaviors. AI can parse through the variability found in human expression data and potentially identify different types of depression.

Prognosis

AI can be used to create accurate predictions for disease progression once diagnosed. AI algorithms can also use data-driven approaches to build new clinical risk prediction models without relying primarily on current theories of psychopathology. However, internal and external validation of an AI algorithm is essential for its clinical utility. In fact, some studies have used neuroimaging, electronic health records, genetic data, and speech data to predict how depression would present in patients, their risk for suicidality or substance abuse, or functional outcomes. The prognosis seems to be highly promising, though it comes with important challenges and ethical considerations such as:

Early detention AI can analyze patterns in speech, writing, facial expressions, and social media behavior to detect early signs of depression, anxiety, PTSD, and even schizophrenia.

Treatment

In psychiatry, in many cases multiple drugs are trialed with the patients until the correct combination or regimen is reached to effectively treat their ailment—AI systems have been investigated for their potential to predict treatment response based on observed data collected from various sources. This application of AI has the potential to reduce the time, effort, and resources required while alleviating the burden on both patients and clinicians.

Benefits

Artificial intelligence offers several potential advantages in the field of mental health care:

• Enhanced diagnostic accuracy: AI systems are capable of analyzing large datasets including brain imaging, genetic testing, and behavioral data to detect biomarkers associated with mental health conditions. This may contribute to more accurate and timely diagnoses.
• Personalized treatment planning: AI algorithms can process information from electronic health records (EHRs), neuroimaging, and genomic data to identify the most effective treatment strategies tailored to individual patients.
• Improved access to care: AI technologies can facilitate the delivery of mental health services such as cognitive behavioral therapy (CBT) through virtual platforms. This may increase access to care, particularly in underserved or remote areas.
• Early detection and monitoring: AI tools can assist clinicians in recognizing early warning signs of mental health disorders, enabling proactive interventions and potentially reducing the risk of acute episodes or hospitalizations.
• Use of chatbots and virtual assistants: AI-powered systems can support administrative functions, including appointment scheduling, patient triage, and organizing medical history. This may improve operational efficiency and enhance patient engagement.
• Predictive analytics for suicide prevention: AI models can analyze behavioral, clinical, and social data to identify individuals at elevated risk of suicide, enabling targeted prevention strategies and informing public health policies.

Challenges

Despite its potential, the application of AI in mental health presents a number of ethical, practical, and technical challenges:

• Informed consent and transparency: The complexity and opacity of AI systems particularly in how they process data and generate outputs require clinicians to clearly communicate potential limitations, biases, and uncertainties to patients as part of the informed consent process.
• Right to explanation: Patients may request explanations regarding AI-generated diagnoses or treatment recommendations. Healthcare providers have a responsibility to ensure that these explanations are available and comprehensible.
• Privacy and data protection: The use of AI in mental health care must balance data utility with the protection of sensitive personal information. Ensuring robust privacy safeguards is essential to building trust among users.
• Lack of diversity in training data: AI models often rely on datasets that may not be representative of diverse populations. This can lead to biased outcomes and reduced effectiveness in diagnosing or treating individuals from underrepresented groups.
• Provider skepticism and implementation barriers: Clinicians and health care organizations may be hesitant to adopt AI tools due to a lack of familiarity, concerns about reliability, or uncertainty about integration into existing care workflows.
• Responsibility and the "Tarasoff duty": In cases where AI identifies a patient as a potential risk to themselves or others, it remains unclear who holds the legal and ethical responsibility to act particularly in jurisdictions with mandatory duty-to-warn obligations.
• Data quality and accessibility: High-quality mental health data is often difficult to obtain due to ethical constraints and privacy concerns. Limited access to diverse and comprehensive datasets may hinder the accuracy and real-world applicability of AI systems.
• Bias in data: Bias in data algorithms means placing preferences of certain groups of people over others which is unfair. AI models are constructed with such biases leading to wrong treatment, incorrect diagnoses and harmful medical outcomes. Because of such bias, groups from diverse backgrounds could be at risk of being underrepresented. Most AI systems are trained on western populations data that can also be a cause of algorithmic bias. If AI systems cannot be trained on inclusive data, it risks increasing racial disparities and mental health issues.

Current AI trends in mental health

As of 2020, the Food and Drug Administration (FDA) had not yet approved any artificial intelligence-based tools for use in psychiatry. However, in 2022, the FDA granted authorization for the initial testing of an AI-driven mental health assessment tool known as the AI-Generated Clinical Outcome Assessment (AI-COA). This system employs multimodal behavioral signal processing and machine learning to track mental health symptoms and assess the severity of anxiety and depression. AI-COA was incorporated into a pilot program to evaluate its clinical effectiveness. As of 2025, it has not received full regulatory approval.

Mental health tech startups

Mental health tech startups continue to lead investment activity in digital health despite the ongoing impacts of macroeconomic factors like inflation, supply chain disruptions, and interest rates.

According to CB Insights, State of Mental Health Tech 2021 Report, mental health tech companies raised $5.5 billion worldwide (324 deals), a 139% increase from the previous year that recorded 258 deals.

A number of startups that are using AI in mental healthcare have closed notable deals in 2022 as well. Among them is the AI chatbot Wysa ($20 million in funding), BlueSkeye that is working on improving early diagnosis (£3.4 million), the Upheal smart notebook for mental health professionals ($10 million in funding), and the AI-based mental health companion clare&me (€1 million). Founded in 2021, Earkick serves as an 'AI therapist' for mental health support.

Alongside patient-facing applications, clinician-facing AI platforms have also emerged to support mental healthcare delivery. These tools are designed to assist practitioners with tasks such as documentation and workflow management rather than providing direct therapy. One example is Heidi Health, an AI-assisted clinical documentation system used by mental health practitioners to support the creation of structured clinical notes.

Emotional AI and predictive detection

An analysis of the investment landscape and ongoing research suggests that we are likely to see the emergence of more emotionally intelligent AI bots and new mental health applications driven by AI prediction and detection capabilities.

For instance, researchers at Vanderbilt University Medical Center in Tennessee, US, have developed an ML algorithm that uses a person's hospital admission data, including age, gender, and past medical diagnoses, to make an 80% accurate prediction of whether this individual is likely to take their own life. And researchers at the University of Florida are about to test their new AI platform aimed at making an accurate diagnosis in patients with early Parkinson's disease. Research is also underway to develop a tool combining explainable AI and deep learning to prescribe personalized treatment plans for children with schizophrenia.

AI systems could predict and plan treatments accurately and effectively for all fields of medicine at levels similar to that of physicians and general clinical practices. For example, one AI model demonstrated higher diagnostic accuracy for depression and post-traumatic stress disorder compared to general practitioners in controlled studies.

AI systems that analyze social media data are being developed to detect mental health risks more efficiently and cost-effectively across broader populations. Ethical concerns include uneven performance between digital services, the possibility that biases could affect decision-making, and trust, privacy, and doctor-patient relationship issues.

In January 2024, Cedars-Sinai physician-scientists developed a first-of-its-kind program that uses immersive virtual reality and generative AI to provide mental health support. The program is called XAIA which employs a large language model programmed to resemble a human therapist.

The University of Southern California has researched the effectiveness of a virtual therapist named Ellie. Through a webcam and microphone, this AI is able to process and analyze the emotional cues derived from the patient's face and the variation in expressions and tone of voice.

A team of Stanford psychologists and AI experts created "Woebot". Woebot is an app that makes therapy sessions available 24/7. WoeBot tracks its users' mood through brief daily chat conversations and offers curated videos or word games to assist users in managing their mental health. A Scandinavian team of software engineers and a clinical psychologist created "Heartfelt Services". Heartfelt Services is an application meant to simulate conventional talk therapy with an AI therapist.

Incorporating AI with EHR records, genomic data and clinical prescriptions can contribute to precision treatment. "Oura Ring", a wearable technology, scans the individual's heart rate and sleep routine in real time to give tailored suggestions. Such AI-based application has an increasing potential in combating the stigma of mental health.

Outcome comparisons: AI vs traditional therapy

Research shows that AI-driven mental health tools, particularly those using cognitive behavioral therapy (CBT), can improve symptoms of anxiety and depression, especially for mild to moderate cases. For example, chatbot-based interventions like Woebot significantly reduced depressive symptoms in young adults within two weeks, with results comparable to brief human-delivered interventions. A 2022 meta-analysis of digital mental health tools, including AI-enhanced apps, found moderate effectiveness in reducing symptoms when user engagement was high, and interventions were evidence-based.

However, traditional therapy remains more effective for complex or high-risk mental health conditions that require emotional nuance and relational depth, such as PTSD, severe depression, or suicidality. The therapeutic alliance, or the relationship between patient and clinician, is frequently cited in clinical literature as a significant factor in treatment outcomes, accounting for up to 30% of positive outcomes. While AI tools are capable of detecting patterns in behavior and speech, they are currently limited in replicating emotional nuance and the social context sensitivity typically provided by human clinicians. As such, most experts view AI in mental health as a complementary tool, best used for screening, monitoring, or augmenting care between human-led sessions.

While AI systems excel at processing large datasets and providing consistent, round-the-clock support, their rigidity and limitations in contextual understanding remain significant barriers. Human therapists can adapt in real time to tone, body language, and life circumstances—something machine learning models have yet to master. Nonetheless, integrated models that pair AI-driven symptom tracking with clinician oversight are showing promise. These hybrid approaches may increase access, reduce administrative burden, and support early detection, allowing human clinicians to focus on relational care. Current research suggests that AI in mental health care is more likely to augment rather than replace clinician-led therapy, particularly by supporting data analysis and continuous monitoring.

Criticism

Although artificial intelligence in mental health is a growing field with significant potential, several concerns and criticisms remain regarding its application:

• Data limitations: A significant barrier to developing effective AI tools in mental health care is the limited availability of high-quality, representative data. Mental health data is often sensitive, difficult to standardize, and subject to privacy restrictions, which can hinder the training of robust and generalizable AI models.
• Algorithmic bias: AI systems may inherit and amplify biases present in the datasets they are trained on. This can result in inaccurate assessments or unequal treatment, particularly for underrepresented or marginalized groups. It is important for developments in mental healthcare to be ethically valid. Major ethical concerns are breach of data privacy, bias in data algorithms, unlawful data access and stigma around mental health treatment. Algorithmic biases can result in misdiagnoses and incorrect treatment which are dangerous. One way to mitigate this is by ensuring that medical data is not segregated based on patient demographics. Another is to get rid of the binary gendering method and ensuring higher ups are informed of any developments in AI tech to avoid bias in the models. Creating a justified system where AI advances ethically, with its real-world applications helping instead of replacing medical professionals needs to be a priority.
• Privacy and data security: The implementation of AI in mental health typically requires the collection and analysis of large amounts of personal and sensitive information. This raises ethical concerns regarding user consent, data protection, and potential misuse of information.
• Risk of harmful advice: Some AI-based mental health tools have been criticized for offering inappropriate or harmful guidance. For example, there have been reports of chatbots giving users dangerous and even deadly recommendations, including one case in which a man died by suicide after a chatbot allegedly encouraged self-sacrifice, and multiple suicide cases in which ChatGPT reportedly encouraged victims to take their own lives, supplied victims with information on suicide methods, and/or urged victims to keep their suicidal ideations secret. In response to such incidents, several AI mental health applications have been taken offline or reevaluated for safety.
• Therapeutic relationship: Decades of psychological research have shown that the quality of the therapeutic relationship empathy, trust, and human connection is one of the most important predictors of treatment outcomes. Some researchers have questioned whether AI systems can replicate the relational dynamics shown to contribute to positive treatment outcomes. Medical professionals are expected to be empathetic and compassionate when interacting with their patients. However, certain authors have said that people interact with chatbots, fully aware that they are incapable of being genuinely empathetic like a human being and do not expect them to be sentient in their responses. Other authors have implied that it is illogical to expect patients to be emotionally vulnerable and open to chatbots. Only medical professionals have the human "touch" that helps them understand the "x factor" of their patients that machines cannot do. The possibility that therapists and medical professionals could be too emotionally exhausted at the end of the day to show their patients the compassion they are entitled to also exists. AI models and chatbots could have the advantage here. Maintaining a balance between the use of AI models and employing health professionals is important.
• Lack of emotional understanding: Unlike human therapists, AI systems do not possess lived experiences or emotional awareness that make them limited. These limitations have prompted debate about the role of AI in addressing emotionally complex mental health needs. Some experts argue that AI cannot substitute for human-centered therapy, particularly in cases requiring deep emotional engagement.
• Risk of psychosis: ChatGPT usage has driven some users to experience delusions. The realism of the interaction can leave a user believing that a real person is chatting with them, fueling cognitive dissonance. Some ChatGPT conversations endorsed conspiracies and mystical beliefs, and in some cases lead to suicide. Delusions and psychosis induced by AI usage has been referred to as chatbot psychosis.

Ethical issues

AI in mental health is progressing with personalized care to incorporate voice, speech and biometric data. But to prevent algorithmic bias, models need to be culturally inclusive too. Ethical issues, practical uses and bias in generative models need to be addressed to promote fair and reliable mental healthcare.

Although significant progress is still required, the integration of AI in mental health underscores the need for legal and regulatory frameworks to guide its development and implementation. Achieving a balance between human interaction and AI in healthcare is challenging, as there is a risk that increased automation may lead to a more mechanized approach, potentially diminishing the human touch that has traditionally characterized the field. Furthermore, granting patients a feeling of security and safety is a priority considering AI's reliance on individual data to perform and respond to inputs. Some experts caution that efforts to increase accessibility through automation may unintentionally affect aspects of the patient experience, such as trust or perceived support. To avoid veering in the wrong direction, more research should continue to develop a deeper understanding of where the incorporation of AI produces advantages and disadvantages.

Data privacy and confidentiality are one of the most common security threats to medical data. Chatbots are known to be used as virtual assistants for patients but the sensitive data they collect may not be protected because the US law does not consider them as medical devices. Pharmaceutical companies use this loophole to access sensitive information and use it for their own purpose which results, in a lack of trust in chatbots and patients can hesitate in providing information essential to their treatment. Conversational Artificial Intelligence stores and remembers every conversation with a patient with complete accuracy, smartphones also collect data from search history and track app activity. If such private information is leaked it could further increase the stigma around mental health. The danger of cybercrimes and the government's unprotected access to our data, all raise serious concerns about data security.

Additionally, a lack of clarity and openness with AI models can lead to a loss of trust from the patient for their medical advisors or doctors as the regular person is unaware of how they reach conclusions into giving certain medical advice. Access to such information is necessary to build trust. However, many of these models act like "black boxes", providing very little insight into how they work. AI specialists have thus highlighted ethical standards, diverse data and the correct usage of AI tools in mental healthcare.

Bias and discrimination

Artificial intelligence has shown promise in transforming mental health care through tools that support diagnosis, symptom tracking, and personalized interventions. However, significant concerns remain about the ways these systems may inadvertently reinforce existing disparities in care. Because AI models rely heavily on training data, they are particularly vulnerable to bias if that data fails to reflect the full range of racial, cultural, gender, and socioeconomic diversity found in the general population.

For example, a 2024 study from the University of California found that AI systems analyzing social media data to detect depression exhibited significantly reduced accuracy for Black Americans compared to white users, due to differences in language patterns and cultural expression that were not adequately represented in the training data. Similarly, natural language processing (NLP) models used in mental health settings may misinterpret dialects or culturally specific forms of communication, leading to misdiagnoses or missed signs of distress. These kinds of errors can compound existing disparities, particularly for marginalized populations that already face reduced access to mental health services.

Biases can also emerge during the design and deployment phases of AI development. Algorithms may inherit the implicit biases of their creators or reflect structural inequalities present in health systems and society at large. These issues have led to increased calls for fairness, transparency, and equity in the development of mental health technologies.

In response, researchers and healthcare institutions are taking steps to address bias and promote more equitable outcomes. Key strategies include:

• Inclusive data practices: Developers are working to curate and utilize datasets that reflect diverse populations in terms of race, ethnicity, gender identity, and socioeconomic background. This approach helps improve the generalizability and fairness of AI models.
• Bias assessment and auditing: Frameworks are being introduced to identify and mitigate algorithmic bias across the lifecycle of AI tools. This includes both internal validation (within training data) and external validation across new, diverse populations.
• Community and stakeholder engagement: Some projects now prioritize involving patients, clinicians, and representatives from underrepresented communities in the design, testing, and implementation phases. This helps ensure cultural relevance and supports greater trust in AI-assisted tools.
• Transparency and explainability: New efforts focus on building "explainable AI" systems that provide interpretable results and justifications for clinical decisions, allowing patients and providers to better understand and challenge AI-generated outcomes.

These efforts are still in early stages, but they reflect a growing recognition that equity must be a foundational principle in the deployment of AI in mental health care. When designed thoughtfully, AI systems could eventually help reduce disparities in care by identifying underserved populations, tailoring interventions, and increasing access in remote or marginalized communities. Continued investment in ethical design, oversight, and participatory development will be essential to ensure that AI tools do not replicate historical injustices but instead help move mental health care toward greater equity.