What AI ethics means
AI ethics is the study and practice of applying moral principles, human rights, and social responsibilities to the design, development, deployment, and governance of artificial-intelligence systems. It asks not only whether an AI system works, but also whether it should be built or used, whom it may affect, whether its outcomes are fair, and who is responsible when it causes harm.
In practical terms, ethical AI is AI developed and used in ways that respect human dignity, autonomy, privacy, equality, safety, and the rule of law. It is not a single technical feature or certification. It is an ongoing process of making defensible decisions about data, objectives, models, interfaces, human oversight, deployment conditions, and the system’s effects over time.
Ethical AI does not mean that a system must produce identical outcomes for everyone or eliminate every possible risk. Different people and groups may have different needs, and some risks cannot be removed completely. The aim is to identify, reduce, monitor, and responsibly manage foreseeable harms while preserving legitimate benefits.
The field combines several areas:
- Moral philosophy, including questions about rights, duties, fairness, consent, and responsibility.
- Computer science, including machine-learning reliability, security, privacy, interpretability, and testing.
- Law and public policy, including discrimination, consumer protection, data protection, intellectual property, and sector-specific regulation.
- Social science, which examines how systems affect institutions, communities, workers, and power relationships.
- Organizational governance, which determines who makes decisions, approves risks, investigates incidents, and provides remedies.
A system can be technically accurate and still be unethical. For example, a highly accurate system may rely on data collected without meaningful consent, expose sensitive information, systematically disadvantage a protected group, or make important decisions impossible for affected people to challenge.
The main principles of ethical AI
There is no single universally accepted list of AI-ethics principles. However, major international frameworks overlap substantially. The OECD AI Principles emphasize innovative and trustworthy AI that respects human rights and democratic values, while UNESCO presents a human-rights-centered approach to AI ethics. Recommendation on the Ethics of Artificial Intelligence - AI AI principles
Human autonomy and human oversight
AI should support people’s ability to make informed choices rather than manipulate, coerce, or silently replace human judgment in situations where human participation is important. Users should understand when they are interacting with AI and, where appropriate, have a meaningful opportunity to question, override, or appeal an AI-assisted decision.
Human oversight is not satisfied merely by placing a person somewhere in the workflow. A reviewer may approve recommendations automatically, lack the authority to intervene, or be unable to understand the system’s limitations. Effective oversight requires appropriate training, sufficient time, access to relevant information, and genuine authority to reject or revise an output.
Human control is especially important in decisions affecting liberty, employment, education, housing, health care, credit, insurance, public benefits, or access to essential services. In some contexts, the ethically appropriate choice may be not to automate a decision at all.
Beneficence and prevention of harm
AI should provide a legitimate benefit and avoid foreseeable physical, psychological, economic, social, and environmental harm. Developers should examine both direct effects and indirect effects, including how a system could be misused, repurposed, or combined with other systems.
Risk assessment should consider more than model performance. A medical model, for example, may perform well in testing but create danger if deployed in a hospital with different patient populations, poor data quality, or no mechanism for clinicians to detect uncertainty. A generative system may be useful for drafting text but risky when its output is treated as authoritative legal, medical, financial, or safety advice.
The principle of proportionality is important: the more serious or irreversible the potential harm, the stronger the evidence, safeguards, human review, and justification required before deployment. Some applications may present risks that cannot be reduced enough to justify their use.
Fairness and non-discrimination
Fairness in AI concerns both outcomes and processes. An ethical system should not unlawfully or unjustifiably discriminate against people because of characteristics such as race, sex, disability, age, religion, nationality, or socioeconomic status. It should also account for less obvious forms of disadvantage, such as language, geographic access, digital literacy, or unequal ability to challenge a decision.
Fairness is not one universal mathematical property. Different definitions can conflict. For example, equal error rates across groups, equal selection rates, and accurate prediction of a real-world outcome may not all be achievable simultaneously. Developers must therefore define the relevant fairness objective in relation to the application, affected groups, legal requirements, and moral stakes.
Good practice includes:
- checking whether training and evaluation data represent the people affected;
- testing error rates and failure modes across relevant groups;
- investigating proxy variables that reproduce sensitive characteristics;
- consulting affected communities;
- documenting trade-offs rather than hiding them behind a single fairness score; and
- providing correction and appeal mechanisms.
Fairness testing cannot prove that a system is fair in every context. It is evidence for a broader judgment that includes the purpose of the system, its distribution of benefits and burdens, and the institutional setting in which it operates.
Privacy and data governance
Ethical AI treats privacy as more than keeping a database secret. Privacy includes appropriate collection, use, retention, sharing, inference, and deletion of personal information. A model may create privacy risks even when it does not reveal a person’s name, because it can infer sensitive attributes, identify individuals from combined data, or reproduce information from its training material.
Responsible data governance asks:
- What information is actually needed for the stated purpose?
- Was it obtained lawfully and fairly?
- Do people understand how it will be used?
- Is the use compatible with the context in which the information was collected?
- Who can access it, and for how long?
- How will errors, deletion requests, security incidents, and secondary uses be handled?
Useful safeguards may include data minimization, access controls, encryption, de-identification where appropriate, retention limits, privacy-preserving learning methods, and restrictions on onward use. These measures reduce risk but do not automatically establish that a use is ethical or legally permissible.
Transparency, explainability, and contestability
Transparency means providing enough information for relevant people to understand a system’s purpose, capabilities, limitations, data practices, and use. It does not necessarily mean publishing source code or revealing security-sensitive details.
Explainability concerns whether the system’s output can be meaningfully explained to the people who need to understand it. The right explanation depends on the audience. An engineer may need a technical account of model behavior; a doctor may need uncertainty and clinically relevant factors; an affected person may need to know why a decision was made and how to challenge it.
A useful explanation should not imply more certainty or causation than the system can support. “The model identified patterns associated with this outcome” is different from claiming that a particular factor caused the decision.
Contestability gives affected people a practical way to report errors, request review, supply additional information, and seek correction or remedy. A notice that merely says “an algorithm decided” is not meaningful transparency.
Accountability, safety, security, and reliability
Accountability means that identifiable people and organizations remain responsible for AI systems. Responsibility should not disappear into phrases such as “the model made a mistake.” Organizations need assigned roles for approval, monitoring, incident response, documentation, and remediation.
Safety and security concern different but related risks. Safety asks whether the system behaves acceptably under expected and unexpected conditions. Security asks whether people can exploit, manipulate, exfiltrate, or disrupt it. Reliability concerns whether the system performs consistently enough for its intended use.
The NIST AI Risk Management Framework identifies trustworthy-AI characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. These characteristics may conflict, so governance must make explicit decisions about priorities and acceptable risk rather than treating them as automatic properties. AI Risks and Trustworthiness - AIRC
How to use AI ethically
Using AI ethically starts with the purpose, not the tool. Before entering data into an AI service or relying on an output, ask whether AI is appropriate for the task and whether the expected benefit justifies the risks.
For an individual or team, an ethical-use process usually includes the following:
- Define the task and its stakes. Determine what the system will do, who may be affected, and whether the output is advisory or decisive. Avoid using a low-stakes drafting tool as though it were a reliable authority in a high-stakes decision.
- Protect confidential information. Do not submit personal, proprietary, medical, financial, legal, or security-sensitive material unless the use is authorized and appropriate safeguards are in place.
- Check the output. AI can produce false statements, fabricated sources, biased recommendations, unsafe instructions, and plausible but irrelevant answers. Verify important claims against reliable evidence and use qualified human judgment where the consequences matter.
- Disclose material AI involvement. Tell relevant users, clients, colleagues, or decision-makers when AI materially contributed to content or a decision, especially when they might reasonably assume a human produced it.
- Preserve human responsibility. A person who signs, publishes, approves, diagnoses, hires, disciplines, or advises remains responsible for the decision. Reviewing an output means evaluating it, not merely clicking approval.
- Provide a route for correction. People affected by an AI-assisted result should know how to report an error, request human review, and obtain correction where appropriate.
- Monitor real-world performance. A system that worked during initial testing may degrade when data, users, incentives, or operating conditions change.
Ethical use also requires respecting intellectual-property rights, contractual restrictions, workplace policies, and applicable law. These requirements vary by jurisdiction and sector. Ethical principles do not replace professional duties or legal advice, particularly in health care, employment, education, finance, public administration, and legal services.
How to develop ethical AI
Developing ethical AI is a lifecycle activity rather than a final review immediately before launch. The European Commission’s guidance describes trustworthy AI through requirements that include human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity and fairness, societal and environmental well-being, and accountability. Ethics guidelines for trustworthy AI
1. Establish purpose, authority, and boundaries
Write down the intended purpose, the people affected, the decisions supported, and the environments in which the system may operate. Identify prohibited uses and conditions under which the system must defer to a human or stop operating.
A clear purpose prevents “function creep,” in which data or a model collected for one reason is quietly used for another. It also makes later testing meaningful: developers can evaluate the system against the context it is actually meant to serve.
2. Conduct an impact and risk assessment
Identify stakeholders, benefits, foreseeable harms, vulnerable groups, misuse scenarios, and potential downstream effects. Consider whether harms would be reversible, whether affected people can protect themselves, and whether the system changes access to opportunities or power.
The assessment should be revisited when the model, data, user population, interface, or deployment environment changes. A risk assessment is not a guarantee of safety; it is a structured way to expose assumptions and decide what controls are needed.
3. Govern the data
Document the origin, purpose, quality, representativeness, licensing, consent or other authorization, retention period, and access rules for each important dataset. Look for missing groups, historical discrimination, measurement errors, duplicated records, and labels that encode subjective or institutional bias.
Data quality cannot be separated from the target being predicted. If the target is itself a biased proxy—for example, past disciplinary action rather than actual misconduct—improving prediction may make an unfair practice more efficient.
4. Design for safety, fairness, privacy, and security
Choose methods and system architecture that reduce identified risks. Depending on the application, this may involve minimizing collected data, limiting model capability, adding authentication, filtering unsafe outputs, using uncertainty thresholds, creating manual review queues, or preventing automated action when confidence is low.
Test across relevant populations and operating conditions. Evaluate not only average accuracy but also false positives, false negatives, worst-case behavior, accessibility, robustness to unusual inputs, privacy leakage, and susceptibility to attack. Where a model cannot reliably meet the required standard, changing the scope—or abandoning the project—may be more responsible than adding a disclaimer.
5. Make the system understandable and contestable
Provide documentation appropriate to different audiences. Users need to know the system’s purpose, limitations, expected inputs, and escalation route. Operators need information about uncertainty, known failure modes, and when to override it. Affected people need understandable notice and a meaningful way to challenge an outcome.
Maintain records of the data and model versions, testing results, approvals, changes, incidents, and decisions. Documentation supports accountability and makes it possible to investigate harm rather than relying on memory or informal explanations.
6. Pilot, monitor, and remedy
Before broad deployment, test the system in a controlled setting that resembles real use. Include representatives of affected groups and allow enough time to observe unusual failures. Establish measurable thresholds for pausing or withdrawing the system.
After launch, monitor for distribution shifts, changing error patterns, disparate impacts, security incidents, automation bias, and new forms of misuse. Create an incident-response process with named owners, reporting channels, investigation procedures, user notification where appropriate, and remedies such as correction, reconsideration, compensation, or system withdrawal.
What ethical AI cannot guarantee
Ethical AI frameworks guide judgment; they do not make difficult decisions automatically. Principles may conflict, technical tests may miss social harms, and a system that is acceptable in one setting may be inappropriate in another. Transparency can conflict with privacy or security, personalization can conflict with data minimization, and a highly accurate model can still be unjust if its purpose or decision rule is wrong.
Ethics is also broader than model behavior. Procurement, labor practices, energy use, concentration of power, accessibility, business incentives, and the effects of automation on communities may all matter. A technically well-designed model cannot compensate for an institution that ignores appeals, conceals errors, or uses automation to evade responsibility.
The most defensible approach is therefore continuous governance: define a legitimate purpose, involve affected people, assess risks, test evidence, assign responsibility, preserve human recourse, and be willing to limit or stop an AI system when its harms cannot be justified. This is the common practical meaning of developing and using AI ethically.
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Foundations of AI Ethics
AI ethics is an interdisciplinary field of applied ethics, computer science, law, and social philosophy focused on identifying, analyzing, and resolving the moral challenges posed by artificial intelligence technologies. Rather than treating computational tools merely as neutral engineering artifacts, AI ethics examines how machine learning models, autonomous systems, and predictive algorithms affect fundamental human rights, social equity, individual autonomy, and ecological well-being.
The field operates at the intersection of normative values—such as fairness, accountability, transparency, and non-maleficence—and practical technical implementation. As algorithmic systems increasingly mediate credit underwriting, criminal sentencing recommendations, medical diagnostics, hiring pipelines, and automated content delivery, AI ethics provides the critical criteria used to evaluate whether an AI system should be conceived, built, deployed, or retired.
In contemporary practice, a distinction is frequently made between ethical AI as an overarching discipline and responsible AI as an operational practice. While ethical AI formulates the philosophical principles, human rights standards, and societal values governing computational intelligence, responsible AI establishes the engineering standards, risk management workflows, and audit protocols necessary to implement those principles across the software development lifecycle. AI Risk Management Framework Recommendation on the Ethics of Artificial Intelligence - AI
Core Pillars of Ethical AI
International governance bodies, technical standards organizations, and academic institutions have coalesced around several foundational pillars that define trustworthy and ethically aligned artificial intelligence systems.
┌────────────────────────────────────────┐
│ Trustworthy AI System │
└───────────────────┬────────────────────┘
│
┌──────────────────┬───────────┴──────────┬──────────────────┐
▼ ▼ ▼ ▼
┌─────────────────┐ ┌───────────────┐ ┌───────────────┐ ┌─────────────────┐
│ Fairness & │ │ Transparency │ │ Privacy & │ │ Safety, Security│
│ Non-Bias │ │ & Auditability│ │ Data Agency │ │ & Robustness │
└─────────────────┘ └───────────────┘ └───────────────┘ └─────────────────┘
│ │ │ │
└──────────────────┴───────────┬──────────┴──────────────────┘
│
▼
┌────────────────────────────────────────┐
│ Accountability & Human Agency │
└────────────────────────────────────────┘1. Fairness, Equity, and Non-Discrimination
Algorithmic systems trained on historical data frequently encode, reproduce, and amplify existing systemic biases. Ethical AI mandates that systems do not generate unjustified disparities in treatment or outcomes based on protected attributes such as race, gender, disability, age, socio-economic background, or religion. Achieving fairness requires addressing bias across multiple operational layers:
- Representational Bias: Arising when specific demographic groups are underrepresented or stereotyped in training datasets.
- Measurement Bias: Occurring when proxy variables do not accurately capture the intended construct (for example, using arrest records rather than actual offenses committed as a proxy for criminal risk).
- Algorithmic and Optimization Bias: Arising when an objective function prioritizes aggregate predictive accuracy at the direct expense of minority group error rates.
2. Transparency, Explainability, and Contestability
Complex models—particularly deep neural networks and large transformer architectures—often function as "black boxes" whose internal reasoning pathways cannot be easily inspected. Ethical design requires sufficient transparency to allow affected stakeholders to understand, audit, and contest decisions:
- System Transparency: Documentation outlining the model's architecture, training data sources, intended use cases, operating boundaries, and known performance limitations.
- Explainability (Interpretability): The ability to provide human-understandable rationales for specific predictions or outputs, either via inherently interpretable models or through post-hoc interpretability techniques such as feature attribution methods.
- Contestability: Concrete operational pathways that permit individuals adversely affected by automated decisions to challenge the outcome and request human adjudication.
3. Accountability and Human Agency
Software systems cannot bear moral or legal responsibility; accountability must always reside with the individuals, teams, and institutions that design, deploy, and operate them. Ethical systems uphold human agency by preserving meaningful human oversight—frequently categorized as:
- Human-in-the-loop (HITL): A human operator must validate or approve every machine output before action is executed.
- Human-on-the-loop (HOTL): The system operates autonomously under real-time human observation, with the operator possessing immediate override and emergency-stop capabilities.
- Human-in-command (HIC): Humans define the comprehensive operational boundaries, rules of engagement, and strategic goals, retaining the authority to deactivate or reconfigure the system entirely.
4. Privacy, Data Governance, and Consent
AI models depend heavily on data ingestion, creating severe privacy risks including mass surveillance, unconsented scraping of personal identities, and unintended memorization of proprietary information. Ethical data governance requires strict data minimization, clear and informed consent, purpose limitation, robust anonymization mechanisms, and protection against model-inversion attacks that extract sensitive training records from public model outputs.
5. Safety, Reliability, and Robustness
An ethical AI system must operate safely and reliably under diverse, shifting, and adversarial real-world conditions. This requires demonstrating verified technical robustness against distribution shifts, adversarial perturbation attacks, and out-of-domain failure modes, alongside deterministic guardrails that prevent harmful actions during edge-case malfunctions. AI Risk Management Framework Recommendation on the Ethics of Artificial Intelligence - AI AI principles
Global Governance and Framework Comparisons
Various international bodies have translated philosophical ethics into standardized risk management paradigms and policy frameworks. While differing in legal enforceability, they share core operational tenets.
| Framework / Body | Enforceability | Primary Focus | Defining Structural Contribution |
|---|---|---|---|
| UNESCO Recommendation on the Ethics of AI | Voluntary international consensus | Global human rights, cultural diversity, and digital inclusion | Explicitly emphasizes global South representation, gender equity, and ecological impacts of compute infrastructure. Recommendation on the Ethics of Artificial Intelligence - AI |
| OECD AI Principles | Intergovernmental policy standard | Trustworthy AI, economic resilience, and democratic governance | Defines foundational policy categories adopted by G20 nations and international regulatory proposals. AI principles |
| NIST AI Risk Management Framework (AI RMF 1.0) | Voluntary US federal standard | Technical risk management and operational trustworthiness | Structures AI development into four iterative technical functions: Govern, Map, Measure, and Manage. AI Risk Management Framework |
| European Union AI Act | Legally binding regulation | Tiered risk-based product safety and market compliance | Categorizes AI systems into unacceptable risk (banned), high risk (strict conformity assessment), and low/minimal risk. |
Technical Engineering: How to Develop Ethical AI
Developing ethical AI requires shifting ethics from abstract policy statements into tangible, measurable engineering practices across the entire machine learning lifecycle.
┌────────────────────────────────────────────────────────────────────────┐
│ Data & Scoping Phase │
│ • Disparate impact analysis • Data sheets for datasets • Provenance │
└───────────────────────────────────┬────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────────┐
│ Modeling & Training │
│ • Constrained optimization • Differential privacy • Red teaming│
└───────────────────────────────────┬────────────────────────────────────┘
│
▼
┌────────────────────────────────────────────────────────────────────────┐
│ Evaluation & Deployment │
│ • Model cards for reporting • Drift & safety monitoring • Oversight │
└────────────────────────────────────────────────────────────────────────┘1. Data Engineering and Curation
- Datasheets for Datasets: Standardized technical documentation that details dataset provenance, geographic and demographic distribution, labeling methods, annotation compensation, and known historical biases before model training begins.
- Balanced Sampling and Data Augmentation: Deliberate curation strategies designed to mitigate class imbalances, historical disparities, and underrepresentation without introducing synthetic noise.
- Differential Privacy: Integration of algorithmic noise mechanisms during data ingestion and model updates to mathematically bound the probability that individual training records can be reconstructed from final weights.
2. Algorithmic Bias Mitigation
Algorithmic interventions generally target one or more stages of the modeling pipeline:
- Pre-Processing Interventions: Reweighting, massaging, or optimized data transformations applied to training datasets to eliminate statistical correlation between sensitive attributes and output labels prior to model exposure.
- In-Processing Interventions: Formulating objective functions with fairness constraints. For instance, in classification tasks, models can optimize predictive utility subject to parity constraints such as equalized odds or demographic parity:
where is the binary prediction, represents the protected demographic group attribute, and is the ground-truth outcome.
- Post-Processing Calibration: Adjusting decision thresholds across different sub-populations after model training to balance false-positive and false-negative trade-offs for marginalized demographics.
3. Model Documentation and Interpretability
- Model Cards for Model Reporting: Comprehensive summary sheets distributed with trained weights specifying performance benchmarks across demographic subgroups, training parameters, intended operational contexts, and explicitly out-of-scope deployments.
- Attribution and Saliency Mechanisms: Implementing explainability frameworks—such as SHAP (Shapley Additive exPlanations) or integrated gradients—to compute local feature contributions, allowing engineers and subject-matter reviewers to verify that decisions are not driven by spurious or discriminatory correlations.
4. Adversarial Red Teaming and Safety Stress-Testing
Rigorous pre-deployment testing requires independent adversarial teams ("red teams") executing systematic prompt injections, jailbreaks, data extraction probes, and out-of-distribution stress tests to identify edge cases where safety guardrails and alignment techniques fail.
Operational Guide: How to Use AI Ethically
For enterprise teams, public institutions, and end users adopting third-party models or commercial foundation APIs, ethical utilization requires rigorous organizational governance rather than pure code construction.
Establish Tiered Risk Assessment Protocols
Organizations should evaluate AI deployments against a proportional risk matrix:
- High-Impact Contexts: Applications directly altering legal rights, physical safety, career livelihoods, housing access, credit, or healthcare delivery require strict multi-stakeholder impact assessments, external auditing, and mandatory human review mechanisms.
- Low-Impact Contexts: Internal productivity, code refactoring, or creative drafting can proceed with lighter oversight, focusing primarily on intellectual property compliance and output validation.
Enforce Continuous Data Hygiene and Privacy Boundaries
When utilizing external AI APIs or cloud-hosted models, ensure that:
- Proprietary source code, customer records, and confidential internal communications are never transmitted to shared public endpoints or opted into provider training pipelines without explicit organizational authorization.
- Data retention, fine-tuning privacy policies, and zero-data-retention (ZDR) service agreements are formally audited prior to production integration.
Implement Strict Human Verification Workflows
Generative language and vision models reliably generate plausible factual hallucinations, synthetic fabrications, and biased narratives. Operational safety requires strict user-level standards:
- Zero Blind Trust: Machine output must never be assumed to be factually accurate or legally compliant without corroboration.
- Explicit Attribution: Clearly disclose automated generation to downstream consumers whenever synthetic text, imagery, or synthetic media (audio/video) interacts with the public or influences consequential reporting.
- Domain-Specific Human Review: Regulated documentation, diagnostic suggestions, and judicial briefs must be reviewed by qualified, credentialed human experts before distribution or legal filing.
Persistent Trade-Offs and Open Challenges
Ethical AI is rarely an exercise in simple algorithmic fixes. In practice, practitioners must navigate fundamentally competing values and mathematical trade-offs.
┌──────────────────────────────┐
│ Core Ethical Dilemma │
└──────────────┬───────────────┘
│
┌─────────────────────────┴─────────────────────────┐
▼ ▼
┌──────────────────────────────┐ ┌──────────────────────────────┐
│ Individual Fairness │ vs │ Group Fairness │
│ Treat similar individuals │ │ Equalize aggregate outcomes │
│ similarly across groups │ │ across demographic classes │
└──────────────────────────────┘ └──────────────────────────────┘The Incompatibility of Fairness Metrics
Mathematical proofs demonstrate that multiple recognized definitions of fairness cannot be satisfied simultaneously when base incident rates differ between demographic groups. For example, an algorithm generally cannot simultaneously maintain equal predictive parity (identical precision across groups) and equalized odds (identical false positive and false negative rates) unless the base rates of the underlying event are identical or the model is an error-free oracle. Deciding which mathematical definition of fairness to implement is fundamentally a political and moral determination, not an engineering choice.
Privacy vs. Fairness and Auditability
Assessing algorithmic bias across protected characteristics (such as race, sexual orientation, or neurodivergence) technically requires collecting and analyzing sensitive demographic data. However, collecting and retaining these very attributes introduces severe data privacy, surveillance, and security risks. Balancing the right to individual privacy against the need for rigorous disparity auditing remains an active area of cryptographic and policy research.
Alignment Tax and Performance Overhead
Implementing comprehensive ethical guardrails, real-time safety classifiers, multi-stage model alignment, and differential privacy often introduces performance penalties, compute overhead, or reduced model utility—often termed the "alignment tax." Engineering teams frequently face economic pressures to bypass thorough safety red-teaming in favor of faster deployment velocity and lower inference costs.
Environmental and Resource Sustainability
The scaling of foundation models requires massive energy consumption and high-capacity cooling resources, resulting in significant carbon emissions and local water usage. A comprehensive view of AI ethics encompasses environmental justice, weighing whether the societal utility of training and operating large-scale automated models justifies their ecological impact. Recommendation on the Ethics of Artificial Intelligence - AI
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