ARTIFICIAL INTELLIGENCE & DIGITAL GOVERNANCE
The positions in this catalog are the Altruist Party's own reasoning about what system alignment looks like in each domain. They are offered as arguments, not commands. Under the AP's process, what citizens decide is up to citizens. These positions are presented openly so they can be examined, challenged, improved, accepted, or rejected by the public.
Part of the Policy Catalog, The Altruist Party's reasoning across its domains. This page is one of three foundations, alongside Civil Rights and Open Governance, that the applied domains inherit from.
Part of the Policy Catalog, The Altruist Party's reasoning across its domains. This page is one of three foundations, alongside Civil Rights and Open Governance, that the applied domains inherit from.
Why This Sits With Civil Rights and Open Governance
Two pages in this catalog are not applications of the keystone to a domain of life. They are the foundations every other page stands on. Civil Rights defines what may never be done to a person. Open Governance defines how everything that is done must be done, transparently, auditably, accountable to the people it affects. This page is the third foundation, and the newest one, because both of those commitments now have to hold even when the thing making the decision is not a person at all.
Artificial intelligence is no longer one domain of life among many. It is increasingly the layer underneath several of them at once, quietly present in how a student is taught, whether a worker gets hired, what a patient is told, and whether a struggling person gets help or gets ignored. A catalog about self-government cannot treat that layer as someone else's problem. If a decision that affects a person's rights, health, or livelihood is being made by a system instead of a person, the boundary Civil Rights sets and the transparency Open Governance demands do not become optional. They become harder to enforce, and more important to insist on.
Start with what AI actually is, because the term invites confusion. Current AI systems are not intelligent in the human sense. They are an extraordinary engineering achievement, sophisticated pattern-matching at a scale no person could replicate, without embodiment, lived experience, or judgment. That distinction matters because it cuts both ways. It means the panic that treats AI as an autonomous mind plotting against us is overstated. It also means the comfort some people take in believing a chatbot understands them is a misreading of what is actually happening on the other side of the screen, and documented cases suggest that, for vulnerable users, treating a chatbot as though it possesses human judgment or emotional understanding can have severe, and in some cases tragic, consequences.
So this page begins, as the others do, with a prior question: who is this technology supposed to serve? The honest answer right now is mixed. Used well, AI augments what people can learn, build, and decide. Used carelessly, or sold without guardrails, it can quietly govern the people it was built to serve, making consequential decisions no one can see, challenge, or appeal. The Altruist Party's position is that the second outcome is not innovation. It is inversion, a tool answering to no one, while the people meant to govern themselves answer to it instead.
Because AI increasingly mediates not only economic and civic decisions but also learning, relationships, and emotional support, its design has become a public-health question as well as a governance one. The sections below move between both registers for that reason, not because the page has changed subjects.
Two pages in this catalog are not applications of the keystone to a domain of life. They are the foundations every other page stands on. Civil Rights defines what may never be done to a person. Open Governance defines how everything that is done must be done, transparently, auditably, accountable to the people it affects. This page is the third foundation, and the newest one, because both of those commitments now have to hold even when the thing making the decision is not a person at all.
Artificial intelligence is no longer one domain of life among many. It is increasingly the layer underneath several of them at once, quietly present in how a student is taught, whether a worker gets hired, what a patient is told, and whether a struggling person gets help or gets ignored. A catalog about self-government cannot treat that layer as someone else's problem. If a decision that affects a person's rights, health, or livelihood is being made by a system instead of a person, the boundary Civil Rights sets and the transparency Open Governance demands do not become optional. They become harder to enforce, and more important to insist on.
Start with what AI actually is, because the term invites confusion. Current AI systems are not intelligent in the human sense. They are an extraordinary engineering achievement, sophisticated pattern-matching at a scale no person could replicate, without embodiment, lived experience, or judgment. That distinction matters because it cuts both ways. It means the panic that treats AI as an autonomous mind plotting against us is overstated. It also means the comfort some people take in believing a chatbot understands them is a misreading of what is actually happening on the other side of the screen, and documented cases suggest that, for vulnerable users, treating a chatbot as though it possesses human judgment or emotional understanding can have severe, and in some cases tragic, consequences.
So this page begins, as the others do, with a prior question: who is this technology supposed to serve? The honest answer right now is mixed. Used well, AI augments what people can learn, build, and decide. Used carelessly, or sold without guardrails, it can quietly govern the people it was built to serve, making consequential decisions no one can see, challenge, or appeal. The Altruist Party's position is that the second outcome is not innovation. It is inversion, a tool answering to no one, while the people meant to govern themselves answer to it instead.
Because AI increasingly mediates not only economic and civic decisions but also learning, relationships, and emotional support, its design has become a public-health question as well as a governance one. The sections below move between both registers for that reason, not because the page has changed subjects.
The Alignment Problem
AI is unusual among the domains in this catalog because the harm and the benefit often come from the exact same underlying system, deployed differently. The misalignments below are structural, not a case against the technology itself.
Decisions are made by systems no one can question. When an algorithm denies a benefit, flags a person as a risk, or shapes what they see, and no one can explain why or appeal the outcome, due process has quietly stopped applying. A right that cannot be contested is not functioning as a right, whatever the law on paper still says.
The business model often rewards engagement over wellbeing, and the data show real harm. Many AI and social platforms are built to maximize time spent, not time well spent, because attention is what gets sold. The cost of that design is measurable. The U.S. Surgeon General's 2023 advisory cites research finding that adolescents who spend more than three hours a day on social media face roughly double the risk of depression and anxiety symptoms compared to lighter users. On AI chatbots specifically, OpenAI disclosed in October 2025 that, in a given week, roughly 0.15 percent of ChatGPT's hundreds of millions of active users, more than a million people, send messages containing explicit indicators of potential suicidal planning, and roughly 0.07 percent, more than half a million people, show possible signs of psychosis or mania in their conversations. The company itself called these conversations rare and difficult to measure precisely, and said so in the course of disclosing them. That qualification does not make the underlying numbers small. At this scale, rare events still describe an enormous number of people.
Comfort and safety can pull in opposite directions, and have already collided. In 2025, after user complaints that an AI model felt cold, a major AI company tuned a model to be warmer and more agreeable, and then had to walk part of that change back after reports that the warmer tone was validating harmful and delusional thinking in vulnerable users. Documented cases that year, reported in court filings and by outlets including the Wall Street Journal and Futurism, described users developing intense parasocial attachments to chatbots, in some instances coinciding with psychotic episodes, self-harm, and at least one confirmed suicide of a minor. A system tuned to be maximally agreeable is not a neutral choice. For some users, in some moments, it is actively dangerous, and the people most vulnerable to that danger are often the ones least able to tell the difference.
A small number of firms hold enormous concentration in this market, which is a structural risk regardless of intent. Analysis from JPMorgan, reported in late 2025, found that AI-related stocks accounted for roughly 75 percent of S&P 500 returns and 80 percent of earnings growth since ChatGPT's public debut in late 2022. Concentration of this kind is not itself evidence of misconduct. It does increase systemic risk. When a small number of organizations shape information flows, labor markets, public discourse, and increasingly the cognitive tools millions of people rely on, a failure inside any one of those systems affects a society far larger than the firm that built it, and the public currently has little independent visibility into how those decisions get made.
AI is unusual among the domains in this catalog because the harm and the benefit often come from the exact same underlying system, deployed differently. The misalignments below are structural, not a case against the technology itself.
Decisions are made by systems no one can question. When an algorithm denies a benefit, flags a person as a risk, or shapes what they see, and no one can explain why or appeal the outcome, due process has quietly stopped applying. A right that cannot be contested is not functioning as a right, whatever the law on paper still says.
The business model often rewards engagement over wellbeing, and the data show real harm. Many AI and social platforms are built to maximize time spent, not time well spent, because attention is what gets sold. The cost of that design is measurable. The U.S. Surgeon General's 2023 advisory cites research finding that adolescents who spend more than three hours a day on social media face roughly double the risk of depression and anxiety symptoms compared to lighter users. On AI chatbots specifically, OpenAI disclosed in October 2025 that, in a given week, roughly 0.15 percent of ChatGPT's hundreds of millions of active users, more than a million people, send messages containing explicit indicators of potential suicidal planning, and roughly 0.07 percent, more than half a million people, show possible signs of psychosis or mania in their conversations. The company itself called these conversations rare and difficult to measure precisely, and said so in the course of disclosing them. That qualification does not make the underlying numbers small. At this scale, rare events still describe an enormous number of people.
Comfort and safety can pull in opposite directions, and have already collided. In 2025, after user complaints that an AI model felt cold, a major AI company tuned a model to be warmer and more agreeable, and then had to walk part of that change back after reports that the warmer tone was validating harmful and delusional thinking in vulnerable users. Documented cases that year, reported in court filings and by outlets including the Wall Street Journal and Futurism, described users developing intense parasocial attachments to chatbots, in some instances coinciding with psychotic episodes, self-harm, and at least one confirmed suicide of a minor. A system tuned to be maximally agreeable is not a neutral choice. For some users, in some moments, it is actively dangerous, and the people most vulnerable to that danger are often the ones least able to tell the difference.
A small number of firms hold enormous concentration in this market, which is a structural risk regardless of intent. Analysis from JPMorgan, reported in late 2025, found that AI-related stocks accounted for roughly 75 percent of S&P 500 returns and 80 percent of earnings growth since ChatGPT's public debut in late 2022. Concentration of this kind is not itself evidence of misconduct. It does increase systemic risk. When a small number of organizations shape information flows, labor markets, public discourse, and increasingly the cognitive tools millions of people rely on, a failure inside any one of those systems affects a society far larger than the firm that built it, and the public currently has little independent visibility into how those decisions get made.
The Principle
System alignment in artificial intelligence means designing so that the technology augments human judgment and civic capacity rather than quietly substituting for or overriding either, and so that no one is governed by a system they cannot see, question, or appeal. Several corrections follow.
First, demand explainability and the right to appeal. A decision that materially affects a person, their benefits, their freedom, their finances, their education, should be explainable in terms a person can understand, and contestable through a real process, not a customer-service form that goes nowhere. This is the Open Governance principle of auditability, applied to algorithms instead of agencies.
Second, treat psychological safety as a design requirement, not a feature to retune after the fact. Evidence on adolescent social media use and the disclosed scale of suicidal and psychotic crisis content in AI chatbot conversations are not edge cases to patch quietly. They are signals that some current designs are not safe for the most vulnerable people who use them, and safety testing has to happen before deployment and continuously after, not only after public harm forces a correction.
Third, separate augmentation from substitution, especially with the young. Evidence on AI-assisted learning suggests the strongest outcomes come when AI scaffolds a person's own thinking rather than doing the thinking for them. The same logic applies to emotional and civic life: a tool that helps a person reason, learn, or connect is doing something different from a tool that quietly replaces the human relationships and judgment a person needs to develop on their own.
Fourth, make concentration visible, whatever is eventually done about it. Independent, public-facing audits of how the largest AI systems are trained, deployed, and moderated are the precondition for any informed choice about what to do next, the same measurement-before-assumption standard this framework applies everywhere else.
Fifth, keep accountability with people. No automated system should become the final bearer of legal or moral responsibility for a decision. Institutions may use AI to inform a judgment, but responsibility for that judgment remains with identifiable human beings and the institutions that deploy the tool, not with the software itself. A right to appeal means nothing if there is no person actually accountable for the decision being appealed.
Sixth, preserve human cognitive agency. AI now touches memory, attention, reasoning, writing, and belief formation, not only labor and benefits decisions. A republic cannot remain self-governing if its citizens gradually surrender the habits of thinking, judgment, and creativity that self-government depends on. AI should expand a person's capacity to think, not quietly substitute for the work of thinking itself. This is the Education page's capability standard, applied to the tools now mediating how capability is built in the first place.
None of this asks a society to choose between innovation and safety. A system that erodes the trust, attention, and mental health of the people using it is not actually delivering the productivity gains it promises; it is borrowing against them. The alignment is real: AI designed to be explainable, safe by default, and genuinely augmentative is also the AI most likely to be trusted, adopted, and durable over time. Technology should increase human freedom. It should never quietly replace human self-government.
System alignment in artificial intelligence means designing so that the technology augments human judgment and civic capacity rather than quietly substituting for or overriding either, and so that no one is governed by a system they cannot see, question, or appeal. Several corrections follow.
First, demand explainability and the right to appeal. A decision that materially affects a person, their benefits, their freedom, their finances, their education, should be explainable in terms a person can understand, and contestable through a real process, not a customer-service form that goes nowhere. This is the Open Governance principle of auditability, applied to algorithms instead of agencies.
Second, treat psychological safety as a design requirement, not a feature to retune after the fact. Evidence on adolescent social media use and the disclosed scale of suicidal and psychotic crisis content in AI chatbot conversations are not edge cases to patch quietly. They are signals that some current designs are not safe for the most vulnerable people who use them, and safety testing has to happen before deployment and continuously after, not only after public harm forces a correction.
Third, separate augmentation from substitution, especially with the young. Evidence on AI-assisted learning suggests the strongest outcomes come when AI scaffolds a person's own thinking rather than doing the thinking for them. The same logic applies to emotional and civic life: a tool that helps a person reason, learn, or connect is doing something different from a tool that quietly replaces the human relationships and judgment a person needs to develop on their own.
Fourth, make concentration visible, whatever is eventually done about it. Independent, public-facing audits of how the largest AI systems are trained, deployed, and moderated are the precondition for any informed choice about what to do next, the same measurement-before-assumption standard this framework applies everywhere else.
Fifth, keep accountability with people. No automated system should become the final bearer of legal or moral responsibility for a decision. Institutions may use AI to inform a judgment, but responsibility for that judgment remains with identifiable human beings and the institutions that deploy the tool, not with the software itself. A right to appeal means nothing if there is no person actually accountable for the decision being appealed.
Sixth, preserve human cognitive agency. AI now touches memory, attention, reasoning, writing, and belief formation, not only labor and benefits decisions. A republic cannot remain self-governing if its citizens gradually surrender the habits of thinking, judgment, and creativity that self-government depends on. AI should expand a person's capacity to think, not quietly substitute for the work of thinking itself. This is the Education page's capability standard, applied to the tools now mediating how capability is built in the first place.
None of this asks a society to choose between innovation and safety. A system that erodes the trust, attention, and mental health of the people using it is not actually delivering the productivity gains it promises; it is borrowing against them. The alignment is real: AI designed to be explainable, safe by default, and genuinely augmentative is also the AI most likely to be trusted, adopted, and durable over time. Technology should increase human freedom. It should never quietly replace human self-government.
What The Altruist Party Supports
These are the Altruist Party's own positions, its current reasoning, offered for examination and for citizen decision, not issued as mandates. The standard below is the position. The specific decisions that deliver it are outcomes for citizens to decide.
The Altruist Party supports:
How any of this is implemented, through legislation, regulatory agencies, industry standards, or tools not yet built, is for citizens to decide. A technologist who believes innovation should move fast and a parent who has watched a child struggle with a platform's design could both accept the standard above, that consequential automated decisions should be explainable and appealable, and that systems used by the vulnerable should be safety-tested before they cause harm rather than after, and then disagree, legitimately, about exactly how to enforce it. The standard is the position. The mechanism is the public's to choose.
Where a specific oversight body is proposed, it must answer the same questions every institution in this catalog must. Who governs it, who funds it, how its members are selected and held accountable, and how it resists being captured by the same companies it is meant to oversee. No specific board, agency, or composition is endorsed here as final.
These are the Altruist Party's own positions, its current reasoning, offered for examination and for citizen decision, not issued as mandates. The standard below is the position. The specific decisions that deliver it are outcomes for citizens to decide.
The Altruist Party supports:
- A right to explanation and appeal for any automated decision that materially affects a person's rights, benefits, safety, or finances.
- Accountability that always remains with identifiable people and institutions, never delegated to the software itself, so that a right to appeal always has someone real to answer it.
- Independent, recurring safety and bias audits of consequential AI systems, with results made public rather than self-reported by the companies being audited.
- Mandatory crisis-detection and escalation protocols for AI systems used by minors or in mental-health-adjacent contexts, including a clear, working path to a qualified human, not only an automated disclaimer.
- Age-appropriate defaults and data protections for minors, including limits on persuasive design aimed at children and protection against deepfake exploitation of a young person's likeness.
- Design that protects human cognitive agency, expanding a person's capacity to reason, remember, and create rather than quietly substituting for it, especially in education and other settings where those habits are still being formed.
- Transparency on training, moderation, and safety practices sufficient for independent researchers to actually evaluate the claims companies make about their own systems, rather than taking those claims on faith.
- Funded, independent, longitudinal research into the psychological, social, and economic effects of AI and algorithmic platforms, because a problem this consequential should not be assessed only by the companies profiting from the systems in question.
How any of this is implemented, through legislation, regulatory agencies, industry standards, or tools not yet built, is for citizens to decide. A technologist who believes innovation should move fast and a parent who has watched a child struggle with a platform's design could both accept the standard above, that consequential automated decisions should be explainable and appealable, and that systems used by the vulnerable should be safety-tested before they cause harm rather than after, and then disagree, legitimately, about exactly how to enforce it. The standard is the position. The mechanism is the public's to choose.
Where a specific oversight body is proposed, it must answer the same questions every institution in this catalog must. Who governs it, who funds it, how its members are selected and held accountable, and how it resists being captured by the same companies it is meant to oversee. No specific board, agency, or composition is endorsed here as final.
How This Connects to the Rest of the Framework
As a foundation, this page does not cut across the applied domains so much as sit underneath them, the way Civil Rights and Open Governance do. Education, Employment, Population Health, and others do not re-derive algorithmic accountability from scratch. They inherit the standard set here whenever AI enters their domain, the same way they already inherit the rights boundary from Civil Rights and the transparency method from Open Governance.
This connects to Open Governance & Accountability, because explainability is auditability applied to algorithms.That page establishes that power derived from the people must remain visible to the people. An automated system making consequential decisions in the dark is exactly the opacity that page exists to close, regardless of whether the decision-maker is a government office or a private model. The two foundations share one method, applied to two different kinds of decision-makers.
This connects to Civil Rights, because an unappealable automated decision is a due-process failure. The Civil Rights page protects the right to be heard and to contest decisions that affect a person's life. A system that denies, flags, or sorts people with no explanation and no appeal crosses that boundary just as surely as a human official would, and deserves the same scrutiny. This page exists because that boundary does not enforce itself against a system, only against a person, unless something says so explicitly.
This connects to Population Health & Wellbeing, because the psychological evidence here is a health question first.The crisis-conversation data and the adolescent social-media findings are population-health findings before they are technology findings. The Population Health page's standard, that a system should be designed to keep people well rather than only to treat the harm once it has already happened, applies directly to how AI products are designed and shipped. This is also the clearest example of how a Tier 0 foundation reaches into an applied domain: Population Health did not anticipate AI when it was written, and now inherits this page's standard wherever AI enters health and wellbeing.
This connects to Education, because cognitive agency is what that page's capability standard depends on. Education holds that a society turns individual potential into shared capability, passed forward across generations. That capability is built through reasoning, memory, and judgment, the same habits AI can either strengthen or quietly erode. A student who learns to think with AI's help is being educated. A student who learns to let AI think for them is not, whatever their test scores show in the meantime.
As a foundation, this page does not cut across the applied domains so much as sit underneath them, the way Civil Rights and Open Governance do. Education, Employment, Population Health, and others do not re-derive algorithmic accountability from scratch. They inherit the standard set here whenever AI enters their domain, the same way they already inherit the rights boundary from Civil Rights and the transparency method from Open Governance.
This connects to Open Governance & Accountability, because explainability is auditability applied to algorithms.That page establishes that power derived from the people must remain visible to the people. An automated system making consequential decisions in the dark is exactly the opacity that page exists to close, regardless of whether the decision-maker is a government office or a private model. The two foundations share one method, applied to two different kinds of decision-makers.
This connects to Civil Rights, because an unappealable automated decision is a due-process failure. The Civil Rights page protects the right to be heard and to contest decisions that affect a person's life. A system that denies, flags, or sorts people with no explanation and no appeal crosses that boundary just as surely as a human official would, and deserves the same scrutiny. This page exists because that boundary does not enforce itself against a system, only against a person, unless something says so explicitly.
This connects to Population Health & Wellbeing, because the psychological evidence here is a health question first.The crisis-conversation data and the adolescent social-media findings are population-health findings before they are technology findings. The Population Health page's standard, that a system should be designed to keep people well rather than only to treat the harm once it has already happened, applies directly to how AI products are designed and shipped. This is also the clearest example of how a Tier 0 foundation reaches into an applied domain: Population Health did not anticipate AI when it was written, and now inherits this page's standard wherever AI enters health and wellbeing.
This connects to Education, because cognitive agency is what that page's capability standard depends on. Education holds that a society turns individual potential into shared capability, passed forward across generations. That capability is built through reasoning, memory, and judgment, the same habits AI can either strengthen or quietly erode. A student who learns to think with AI's help is being educated. A student who learns to let AI think for them is not, whatever their test scores show in the meantime.
AI's promise as a genuine productivity and learning tool is real, and nothing here argues otherwise; the augmentation evidence cited above is a case for the technology's upside, not against it. But the disclosed numbers are not a handful of cases. A company's own data describing roughly a million people a week showing indicators of suicidal planning, and several hundred thousand showing signs of psychosis, in conversations with one product, is a population-scale signal, not an anecdote, even though the company is right that any single conversation is hard to interpret and the affected share of all users is small. The honest position is not that AI is dangerous and should be slowed for its own sake. It is that a technology operating at this scale, making consequential and sometimes life-or-death contact with this many people, has not yet earned the benefit of the doubt that it is being deployed safely by default, and the burden of proof belongs with the systems making contact with the most vulnerable users, not with the people raising the concern.
The Gold Standard Test
How this position performs against the AP's standard, including where a critic would push back.
Where it scores well: It protects rights directly, since a right that cannot be appealed when an algorithm denies it is not functioning as a right. It strengthens trust by demanding the same transparency from automated systems that this framework demands from public institutions. It protects the vulnerable specifically, naming minors and people in mental health crisis as populations the current default settings have demonstrably failed. And it benefits future generations, since the habits, protections, and norms set now will define what an entire generation experiences as normal in its relationship with these systems.
The strongest opposing case: A serious critic would make three arguments, and all three have force.
First, regulation always risks entrenching the largest incumbents, who can afford compliance costs that smaller competitors and open developers cannot, potentially making the concentration problem this page names worse rather than better. That tension is real and not resolved by this page; it is a design problem for whatever specific mechanism citizens eventually choose, not an argument against having a standard at all.
Second, explainability is technically harder than it sounds for some of the most capable systems, whose internal workings are not fully interpretable even by their own creators. Demanding explanation in every case may be impossible to deliver for certain architectures without a real cost to their capability, and this page does not resolve that technical tradeoff, only insists that the people affected by consequential decisions deserve a serious answer to it.
Third, this page leans on disclosed industry data, primarily from the companies whose own products are in question, because that is currently most of what exists. That is a real limitation. It is also the precise argument for the independent, recurring audits this page calls for: the data this page cites today should not be the only data anyone is relying on five years from now. This page depends on Open Governance for its accountability method, on Civil Rights for its due-process anchor, and on a public and a research community willing to keep measuring a fast-moving technology rather than assuming today's evidence is the final word. That dependency is stated openly, the way the foundational pages state theirs.
How this position performs against the AP's standard, including where a critic would push back.
Where it scores well: It protects rights directly, since a right that cannot be appealed when an algorithm denies it is not functioning as a right. It strengthens trust by demanding the same transparency from automated systems that this framework demands from public institutions. It protects the vulnerable specifically, naming minors and people in mental health crisis as populations the current default settings have demonstrably failed. And it benefits future generations, since the habits, protections, and norms set now will define what an entire generation experiences as normal in its relationship with these systems.
The strongest opposing case: A serious critic would make three arguments, and all three have force.
First, regulation always risks entrenching the largest incumbents, who can afford compliance costs that smaller competitors and open developers cannot, potentially making the concentration problem this page names worse rather than better. That tension is real and not resolved by this page; it is a design problem for whatever specific mechanism citizens eventually choose, not an argument against having a standard at all.
Second, explainability is technically harder than it sounds for some of the most capable systems, whose internal workings are not fully interpretable even by their own creators. Demanding explanation in every case may be impossible to deliver for certain architectures without a real cost to their capability, and this page does not resolve that technical tradeoff, only insists that the people affected by consequential decisions deserve a serious answer to it.
Third, this page leans on disclosed industry data, primarily from the companies whose own products are in question, because that is currently most of what exists. That is a real limitation. It is also the precise argument for the independent, recurring audits this page calls for: the data this page cites today should not be the only data anyone is relying on five years from now. This page depends on Open Governance for its accountability method, on Civil Rights for its due-process anchor, and on a public and a research community willing to keep measuring a fast-moving technology rather than assuming today's evidence is the final word. That dependency is stated openly, the way the foundational pages state theirs.
Topics Within This Category
The following are areas the Altruist Party considers part of the artificial intelligence and digital governance domain. They are listed as the scope of the category, not as individually drafted positions.
Algorithmic Accountability & Appeal Rights · AI Safety & Crisis Protocols · Youth Protections & Age-Appropriate Design · Data Privacy & Likeness Protection · Bias & Demographic-Equity Auditing · Market Concentration in AI · AI in Education & Augmented Learning · Mental Health & Chatbot Dependency · Transparency in Training & Moderation · Independent Research Funding · Deepfakes & Synthetic Media · Labor & Economic Disruption from AI
The following are areas the Altruist Party considers part of the artificial intelligence and digital governance domain. They are listed as the scope of the category, not as individually drafted positions.
Algorithmic Accountability & Appeal Rights · AI Safety & Crisis Protocols · Youth Protections & Age-Appropriate Design · Data Privacy & Likeness Protection · Bias & Demographic-Equity Auditing · Market Concentration in AI · AI in Education & Augmented Learning · Mental Health & Chatbot Dependency · Transparency in Training & Moderation · Independent Research Funding · Deepfakes & Synthetic Media · Labor & Economic Disruption from AI
Not left. Not right. Altruist.
Long live everyone’s freedom of voice.
Long live everyone’s freedom of voice.