FohnAI Ethical AI Practices: Benchmarking and Compliance Report 2025

Prepared for: FohnAI – Investor Due Diligence, Regulatory Compliance & Public Trust
Date: May 2025

FohnAI Ethical AI Commitment 2025 (Overview)

FohnAI’s 2025 Ethical AI Commitment publicly articulates the company’s principles and intended practices for responsible AI development. The commitment emphasizes transparency, fairness, accountability, and societal benefit, pledging alignment with leading ethics frameworks (e.g. EU AI Act, OECD Principles, UNESCO Guidelines). For example, FohnAI leadership has stressed that “companies that prioritize transparency and ethical AI development will have the edge” and that FohnAI is “building … more ethical solutions for our clients”. The commitment outlines that all FohnAI products will undergo rigorous impact assessments, bias mitigation reviews, and human oversight, and that the company will maintain data protection and audit records consistent with global standards. While the public document broadly aligns with industry norms, it provides only high-level goals. Verifiable evidence of implementation (e.g. published audits, independent certifications) is not specified, which is a focus for due diligence.

Key elements of the FohnAI commitment include:

Transparency: Promises to document models and communicate explainability features to users and regulators.
Fairness: Commits to proactive bias testing on datasets and outputs, and inclusive design.
Accountability: Establishes internal review boards and traceability logs for model decisions.
Privacy: Affirms compliance with GDPR/CCPA and use of privacy-by-design, data minimization practices.
Societal Wellbeing: Intends to avoid harmful applications (e.g. surveillance, discrimination) and evaluate social impact.
Governance: Plans to integrate human-in-the-loop processes, third-party audits, and continuous monitoring.

These stated policies broadly mirror accepted principles. To assess compliance, we compare FohnAI’s practices to leading AI companies and to global ethical frameworks below.

Industry Benchmarks: OpenAI, Anthropic, DeepMind & Others

OpenAI (USA): OpenAI’s charter commits to ensure that AGI “benefits all of humanity” and to avoid enabling uses that harm society. Its safety-first approach includes “long-term safety” research and cooperation with other labs. OpenAI publishes transparency reports (e.g. on government data requests) and has an official Trust & Transparency page detailing content moderation and child-safety efforts. Internally, OpenAI implements rigorous model testing, moderation guidelines, and a multi-layered review (red-teaming) to mitigate bias and misuse. Best Practices: Broad benefit charter; public disclosure (transparency reports, research publications); independent safety review boards (e.g. for GPT models).

Anthropic (USA): Anthropic’s safety framework emphasizes “Constitutional AI,” using a set of human-aligned principles to guide model outputs toward helpfulness and harmlessness. This enables explicit, inspectable rules to improve transparency and reduce toxic or biased outputs. In January 2025, Anthropic became one of the first AI labs to earn ISO/IEC 42001:2023 certification for its AI management system. This certification validates that Anthropic has “policies and processes to ensure AI systems are designed, developed, and deployed in an ethical, secure and accountable manner,” including rigorous testing, ongoing monitoring, transparency measures, and defined oversight roles. Best Practices: Scalable oversight via Constitutional AI; formal third-party certification (ISO 42001) covering governance, risk assessment, and transparency.

DeepMind (Google/UK): DeepMind follows Google’s AI Principles (2018), which stress fairness, privacy, safety, and accountability. Google states it employs “appropriate human oversight, due diligence, and feedback mechanisms” to align AI with human rights, and uses rigorous design and testing to “mitigate unintended or harmful outcomes and avoid unfair bias”. Google also promotes privacy-by-design in AI products. While DeepMind’s research focuses on technical solutions (e.g. formal verification, interpretability), its corporate governance includes ethics review committees and internal red-teaming. Best Practices: Global tech leader with established AI ethics policies; extensive R&D on bias auditing and safety; privacy- and security-by-design frameworks; public collaboration (e.g. Frontier Safety research initiatives).

Other Notable Players: Microsoft, IBM and Facebook/Meta also offer benchmarks. For instance, Microsoft publishes Responsible AI resource kits (fairness toolkits, interpretability), and has an external AI ethics committee. IBM’s open-source AI Fairness 360 and AI Explainability 360 toolkits exemplify industry tools for compliance. Meta (now Threads/X) has AI ethics guidelines and supports content transparency (like tagging synthetic media). Collectively, these leaders show: rigorous model audits, active fairness tool adoption, and engagement with regulators (e.g. participating in IEEE and ISO AI standards). FohnAI should monitor these efforts as reference standards.

Global AI Ethics Frameworks (EU, UNESCO, OECD, US)

EU AI Act: The EU’s first binding AI regulation (effective 2025) adopts a risk-based model. It bans “unacceptable” uses (e.g. social scoring, biometric mass surveillance) and imposes strict rules on high-risk systems. High-risk AI (e.g. in hiring, healthcare, policing) must undergo risk assessments, use high-quality non-biased data, maintain logs for traceability, and provide detailed technical documentation. Human oversight controls and robust accuracy/safety requirements are mandated. The Act also creates new transparency obligations: users must be informed when they interact with AI (e.g. chatbots must identify themselves as machines), and certain AI-generated content (deepfakes, news) must be clearly labeled. These compliance requirements are summarized in an EU AI Act Compliance Matrix (see Figure below) that maps obligations to providers and deployers of AI systems.

Figure: Example of an EU AI Act compliance matrix (excerpt) showing obligations for providers and deployers of high-risk AI systems. High-risk obligations include risk management, data quality, logging/traceability, documentation, human oversight and robustness.

UNESCO Recommendation (2021): UNESCO’s global Recommendation on the Ethics of AI (adopted by 193 countries) is a non-binding standard emphasizing human rights. Its cornerstone is protection of human dignity, with fundamental principles of transparency, fairness, privacy, and human oversight. The Recommendation identifies ten core principles, including Privacy and Data Protection (AI must safeguard privacy throughout its lifecycle), Accountability (AI systems should be auditable and traceable with oversight, impact assessments and due diligence), and Transparency and Explainability (AI deployment depends on context-appropriate transparency, balanced against privacy/safety). Principle 10 explicitly calls for Fairness and Non-Discrimination, urging AI actors to “promote social justice, fairness, and non-discrimination” and inclusive access to AI benefits. UNESCO further outlines 11 policy areas for action (data governance, education, environment, etc.), shown below:

Figure: UNESCO’s 11 AI ethics policy areas (from its 2021 Recommendation). These range from Ethical Impact Assessment and Data Governance to Education, Health, and Social Well-being.

OECD AI Principles: The OECD Principles (2019, updated 2024) are the first intergovernmental AI standard. They promote innovation and trustworthy AI consistent with human rights and democratic values. Five key principles include Fairness (non-discrimination, privacy, diversity), Transparency (responsible disclosure and explainability to stakeholders), and Accountability (actors must ensure traceability of datasets/processes and manage risks throughout the AI lifecycle). In practice, governments and companies use OECD-aligned tools (e.g. AI risk frameworks) for oversight. FohnAI can cite OECD alignment (e.g. by adopting OECD’s definition of AI systems) to demonstrate interoperability with global policies.

U.S. AI Bill of Rights (2022): This White House blueprint outlines five core protections: (1) Safe and Effective Systems, (2) Algorithmic Discrimination Protections, (3) Data Privacy, (4) Notice and Explanation, and (5) Human Alternatives. Key provisions include: ensuring systems are pre-tested and independently evaluated for safety and bias; explicitly assessing and mitigating discrimination by using diverse data, disparity testing, and equity audits; embedding privacy-by-design (minimal data, user consent); requiring that users be notified when automated systems are used and given clear plain-language explanations of outcomes; and providing human fallback (opt-out and appeal processes) in high-risk scenarios. While not legally binding, this framework reflects U.S. government expectations and guides U.S.-based companies.

Summary: All these frameworks converge on core themes: transparency/explainability, fairness/non-discrimination, safety/robustness, accountability (auditing & oversight), human rights (privacy & dignity), and multi-stakeholder engagement. FohnAI’s practices should be evaluated against these specific requirements. For example, EU rules demand documentation and traceability; UNESCO/OECD call for transparency and inclusivity; the U.S. expects notices and remedies. The next sections benchmark FohnAI’s stated practices in each focus area against leading companies and these standards.

Transparency and Explainability

Transparency – the practice of openly communicating how an AI system works – is universally emphasized. The OECD Principles state that “AI Actors should commit to transparency and responsible disclosure,” providing meaningful information on system capabilities, limitations, and decision logic. Similarly, UNESCO lists Transparency and Explainability as a core principle, noting that ethical AI “depends on” appropriate explainability and contextual disclosure. The EU AI Act requires that AI-generated content (e.g. deepfakes, news bots, chatbots) be clearly labeled so people can recognize machine involvement. The U.S. Bill of Rights goes further: it mandates that automated decision-making tools provide plain-language documentation, including notice that an AI is in use, description of system function, responsible parties, and explanations of outcomes.

Industry Practices: Leading AI labs have instituted various transparency measures. OpenAI has a dedicated Trust & Transparency portal where it publishes government data-request reports, moderation policies, and safety evaluations. OpenAI’s Charter also pledges to share research openly. Anthropic’s “Constitutional AI” is in part a transparency tool: by encoding their guiding principles explicitly, they make the model’s value alignment inspectable and understandable. Anthropic also commits to publishing impact assessments of its models and allowing external audits. Google’s AI groups release model cards and data sheets for major models, documenting training data and known limitations, and Google publishes AI research and safety benchmarks.

FohnAI’s Position: FohnAI’s public commitment emphasizes user explainability and documentation (“provide stakeholders with clear information on system purpose and outputs”). However, specifics are sparse. For full compliance, FohnAI should adopt or cite concrete measures like releasing model cards, transparency reports, and user-facing explainers. For example, ensuring that any generative content is watermarked or labeled (as per the EU Act) would demonstrate adherence. Providing an accessible explanation interface or API for model decisions, and publishing plain-language FAQs on system use, would align with the U.S. Bill of Rights guidance.

Best Practices & Gaps: Industry leaders show that transparency often comes via external reporting and tool-driven explainability (e.g. saliency maps, provenance tracking). FohnAI differs if it does not yet routinely disclose such information. The gap is opportunity: investing in automated documentation tools (logging data lineage and model decisions) and making summaries public (e.g. summaries of audits) would significantly boost trust.

Fairness and Bias Mitigation

Preventing unfair bias in AI outputs is a top concern. The UNESCO recommendation explicitly demands that AI “promote social justice, fairness, and non-discrimination” and ensure all people benefit inclusively. The OECD likewise requires AI actors to uphold non-discrimination and equality, embedding diversity and social justice into systems. The EU AI Act tackles bias by mandating high data quality and diversity for high-risk systems, explicitly requiring measures “to minimize risks of discriminatory outcomes” in training data. The U.S. Bill of Rights’ discrimination protection pillar calls for continuous equity assessments (e.g. disparity testing, bias reviews) and transparent reporting on fairness measures.

Industry Practices: The best AI developers use large diverse datasets and fairness toolkits. For instance, Anthropic’s research shows Constitutional AI can reduce toxic or biased outputs without loss of helpfulness. Anthropic also audits its models with internal testers representing diverse demographics. Google has “Fairness” as a category in its Responsible AI toolkit and performs bias testing (the cited Google AI Principles stress avoiding unfair bias). OpenAI conducts adversarial testing to uncover biases, and as a concrete measure publishes usage policies barring biased use (e.g. forbidding political persuasion). Other companies (e.g. Microsoft) perform third-party algorithmic impact assessments and use de-biasing algorithms (e.g. PAI score, Fairlearn).

FohnAI’s Position: FohnAI’s commitment mentions “auditing for bias” but details are not public. To align with best practices, FohnAI should institute bias audits at development and deployment stages (as suggested by frameworks). This could involve statistical checks on output fairness, stakeholder reviews, and diverse test cases. If FohnAI trains models on user-provided data, it must ensure samples are representative or apply techniques like re-weighting to prevent under/over-representation of groups. For external compliance, FohnAI could perform and share results of automated bias tests or engage independent auditors, matching Anthropic’s ISO-backed approach.

Best Practices & Gaps: Industry players often make fairness assessments explicit (e.g. Google’s “counterfactual fairness” tests, Microsoft’s AI fairness curriculum). If FohnAI lacks published fairness metrics or test results, it stands behind these leaders. Mitigating this gap would involve adopting known tools (IBM AIF360, Google’s TCAV, etc.) and documenting the mitigation steps. The companies that excel not only develop such tools but also enforce internal review boards and clear escalation paths for bias issues – a practice FohnAI may need to formalize.

Accountability and Human Oversight

Accountability in AI means tracing responsibility and ensuring humans can intervene. The OECD Principles require AI actors to be accountable for system functioning and to maintain traceability of datasets and decisions. UNESCO likewise insists AI systems be “auditable and traceable” with impact assessments and oversight in place. The EU AI Act embeds accountability by demanding detailed technical documentation and continuous monitoring for high-risk systems. The U.S. Bill of Rights mandates accessible human alternatives: users must be able to opt out or appeal, and systems must have human fallback for sensitive decisions.

Industry Practices: Top AI companies integrate human governance layers. OpenAI uses review boards (including external ethics experts) to vet new models. It also embeds humans in the content moderation loop to catch unsafe outputs. Anthropic’s ISO 42001 compliance means it has defined roles and responsibilities for each stage of development (e.g. risk officers, compliance leads). Google has long promoted “human-in-the-loop” (HITL) controls, requiring human oversight on self-driving cars and healthcare AIs. Furthermore, many firms conduct independent audits or publish “algorithms impact assessments” before deployment. Microsoft and IBM also emphasize “explainable AI” tools that allow operators to review decisions.

FohnAI’s Position: FohnAI’s commitment cites an internal ethics committee and logging of model decisions. To fully meet expectations, FohnAI should implement traceability mechanisms such as immutable logging of training data versions and model outputs, so that any output can be traced back to its provenance. Human oversight should be embedded: for example, hiring domain experts for high-stakes AI deployments, and requiring manual approval gates. Explicitly providing a clear escalation process (as the US guidance suggests) would improve trust – e.g. a dedicated contact for users to contest AI decisions. Demonstrating independent auditing (internal or third-party) would show accountability beyond policy statements.

Best Practices & Gaps: The best practices include not only logging, but actually publishing how accountability is enforced. For example, Google’s Frontier Safety Team stress-tests models and publicly releases findings. If FohnAI hasn’t yet established external audits or public accountability reports, it lags behind. Implementing certifications (like ISO 42001’s emphasis on accountable processes) would signal robust governance. In the absence of formal oversight, FohnAI should consider multi-disciplinary review boards and transparent reporting of review outcomes (even at a summary level) to fill the gap.

Privacy and Data Protection

Privacy is a fundamental right in all frameworks. The OECD explicitly links privacy with fairness and democratic values. UNESCO’s 3rd principle demands that “Privacy must be protected and promoted throughout the AI lifecycle,” backed by appropriate data protection frameworks. The U.S. Bill of Rights requires that users have agency over data collection, with AI designers seeking consent and avoiding unnecessary data use. The EU AI Act requires conformity with GDPR: for instance, sensitive biometric or personal data cannot be used in unauthorized ways (hence the ban on emotion/biometric scanning in certain contexts). Overall, these frameworks expect privacy-by-design (minimizing data collected), user control (opt-in and deletion rights), and security safeguards (encryption, anonymization).

Industry Practices: Industry leaders routinely apply strong data governance. Google mandates data encryption in transit and secure storage, and limits data collection to what is needed for a service. OpenAI’s transparency reports detail how they handle user data requests, showing strict criteria and third-party oversight (e.g. only 57 non-content requests were processed in H2 2024). Anthropic’s ISO 42001 framework specifically calls out “privacy safeguards” and likely aligns with ISO/IEC 27001 security standards as well. Tech companies often embed Differential Privacy or Federated Learning when feasible, and regularly review data handling practices with privacy impact assessments.

FohnAI’s Position: FohnAI declares GDPR/CCPA compliance and plans to implement default privacy settings. It must ensure that data minimization is enforced (collecting only what models strictly need) and that consent flows are transparent (e.g. clear user interfaces, privacy notices). Publishing a data usage policy or impact assessment would align with UNESCO/US calls for transparency. For regulatory compliance, FohnAI should demonstrate alignment with GDPR’s accountability principle (keeping data records) and possibly undergo data protection audits. Any use of sensitive data (e.g. health, biometrics) should trigger privacy risk checks.

Best Practices & Gaps: Leading firms undergo regular privacy audits and even obtain certifications (like ISO 27701 for privacy). If FohnAI has not done so, pursuing formal certification or at least external audit of its data practices would strengthen its credibility. Additionally, adopting privacy-enhancing technologies (PETs) – such as anonymization, secure multi-party computation, or on-device processing – can mitigate risk. Without clear evidence of such measures, privacy-conscious stakeholders may question the commitment. Publicly releasing privacy compliance summaries (similar to OpenAI’s data request transparency) would increase trust.

Societal Impact and Responsibility

Assessing and mitigating broader societal impacts is the final core area. The UNESCO Recommendation’s first core value is Human Rights and Dignity, and it expects AI to advance “inclusive growth” and protect democratic values. The OECD principle of Inclusive Growth and Well-being calls for AI to reduce inequalities and benefit society. Similarly, OpenAI’s charter explicitly commits to “broadly distributed benefits” and to use its influence to avoid harm or concentration of power. The U.S. framework’s “Safe and Effective” principle likewise emphasizes that automated systems should do no harm and be proactively protected against misuse.

Industry Practices: In practice, leading companies perform ethical impact assessments for high-risk projects. For example, many tech firms have established internal ethics review boards that consider societal factors like labor displacement, misinformation, or equity. Google has published surveys of how AI may affect jobs and the environment. Anthropic’s ISO 42001 status includes “ethical and secure” AI use, and it has set up an Economic Advisory Council to study AI’s societal effects (e.g. on jobs/inflation). Some companies have created partnerships (e.g. UNESCO’s Business Council co-chaired by Microsoft) to develop common standards for ethical AI impact.

FohnAI’s Position: FohnAI’s commitment states a goal of beneficial impact, but concrete mechanisms are not detailed. To demonstrate societal responsibility, FohnAI should conduct multi-stakeholder consultations (e.g. user focus groups, ethicist reviews) for major products. For AI systems in sensitive domains (healthcare, finance), it should follow domain-specific guidelines (e.g. medical AI standards). Publicly, FohnAI could publish a Social Impact Report summarizing potential positive and negative effects of its AI systems, similar to a corporate sustainability report. Aligning with UNESCO’s call for gender and diversity inclusion, FohnAI should ensure diverse teams contribute to AI design.

Best Practices & Gaps: The best practice is to move beyond internal statements to actionable policies – for example, integrating AI impact assessments into product development (as UNESCO suggests with its Ethical Impact Assessment methodology). FohnAI could partner with NGOs or academia to audit societal outcomes (e.g. bias in credit decisions). Not having explicit impact-mitigation steps or external review could be a gap. Framing this as an opportunity, FohnAI might pioneer an independent ethics board or community liaison program to monitor real-world consequences, setting it apart from less proactive competitors.

Implementation Strategies

To translate principles into practice, companies deploy specific tools and processes. The following strategies have emerged as industry and framework best practices:

Governance Frameworks & Certification: Adopt an AI governance framework (e.g. ISO/IEC 42001 or NIST AI RMF) to ensure consistent oversight. Anthropic’s ISO 42001 certification illustrates a mature AI management system. FohnAI should consider similar certification or developing its own AI management plan aligned with these standards.
Internal Audits and Third-Party Oversight: Establish regular audits of AI systems and processes. This includes algorithmic impact assessments (as promoted by UNESCO and OSTP) that document system purpose, stakeholders, risks and mitigations. Involve third-party auditors or ethics advisors for independent review, following models like public audit boards in finance.
Bias and Robustness Toolkits: Integrate technical tools for fairness and explainability. For example, use counterfactual fairness tests, bias measurement libraries, and saliency/explainability toolkits. Frameworks and academic research (e.g. OECD catalogue of tools) provide many open-source solutions. Likewise, rigorous adversarial testing (so-called “red-teaming”) helps uncover failure modes before deployment.
Human-in-the-Loop & Training: Embed humans at key points in the AI lifecycle. For example, include human review for high-risk outputs or have AI assistants that can be overridden by operators. Provide specialized training for those humans (as OSTP suggests for sensitive domains). FohnAI should ensure team members are trained in AI risk awareness, and possibly hire ethicists or social scientists.
Transparency Mechanisms: Create user and stakeholder reporting. This can include model cards, data sheets, and public summary reports. Use dashboards that log model performance metrics (fairness, accuracy, drift) in real time. As the EU Act requires record-keeping, these logs must be maintained for compliance checks.
Stakeholder Engagement: Open lines of communication with regulators, customers, and the public. For example, form an advisory committee (as some companies do) that includes external experts. Participating in multi-stakeholder consortia (e.g. the UNESCO Business Council or industry ethics forums) keeps FohnAI abreast of evolving norms.

These strategies align with both industry practice and regulatory guidance. Table: Implementation Matrix could be constructed, but key examples include ISO/IEC 42001 (governance policies), AI model cards (Google’s practice), algorithmic impact assessments (advocated by OSTP), and robust logging/traceability mandated by EU law. By institutionalizing such tools and processes, FohnAI can move from policy to practice and build compliance evidence.

Gaps, Differentiators & Opportunities for FohnAI

Differentiators/Strengths: FohnAI’s explicit public commitment itself is a differentiator in some markets. Its stated focus on transparency and ethics positions it alongside leaders. The company’s apparent willingness to align with major frameworks (as claimed) is a positive sign for investors and regulators. Also, by mentioning high-level goals like human oversight and audits, FohnAI signals intent. If it truly implements these, it could match or exceed the standards of smaller startups without formal policies.

Gaps: However, the commitment lacks specificity. Unlike Anthropic or Google, FohnAI has not yet released detailed policies or certifications. There’s no public evidence of independent audits, detailed bias reports, or conformity assessments. Frameworks like the EU Act demand documented risk management – if FohnAI has only internal procedures, it should make summaries available. On transparency, FohnAI has not published usage disclosures or model documentation. These gaps can create doubt for due diligence: stakeholders cannot verify compliance without data. Additionally, FohnAI’s societal impact measures are only aspirational; it has yet to demonstrate how it assesses long-term effects or engages with diverse community voices.

Opportunities: These gaps suggest several opportunities. FohnAI can distinguish itself by adopting industry-leading practices ahead of regulatory mandates. For instance, pursuing ISO 42001 would provide third-party validation of governance (as Anthropic did). Publishing a quarterly AI Ethics Transparency Report (covering topics like fairness metrics, incident logs, user complaints, data requests) would showcase accountability. Aligning explicitly with upcoming standards (e.g. EU Act compliance checklist) and advertising this could win public trust. FohnAI could also highlight any unique approaches it uses (e.g. novel privacy-Preserving techniques, a proprietary bias mitigation algorithm, or community review panels) to stand out.

In sum, FohnAI’s existing ethical stance is on par with other new entrants, but concrete actions lag behind the best of breed. Turning commitments into published artifacts (audits, reports, certifications) will reveal FohnAI’s competitive advantage and compliance readiness.

Recommendations & Future Outlook

To strengthen regulatory compliance and stakeholder confidence, FohnAI should undertake the following forward-looking actions:

Codify Policies into Practice: Develop detailed AI governance policies that operationalize the commitment. For example, create a Responsible AI Policy document covering each focus area, referencing specific standards (EU Act articles, ISO 42001 sections, etc.). Publicize key excerpts to demonstrate seriousness.
Implement Independent Audits: Schedule regular third-party audits of FohnAI’s AI lifecycle (data, models, deployment). Obtain certifications (like ISO 42001 for AI or ISO 27701 for privacy). Publish audit summaries or allow regulators access to reports, signaling transparency.
Enhance Documentation & Reporting: Begin publishing model cards, data cards, and transparency reports. For every product release, include a plain-language explanation of how it works and what data it used (in line with the US “Notice and Explanation” principle). Maintain and share internal audit logs and version control records to satisfy EU/OECD traceability requirements.
Strengthen Bias & Impact Assessments: Institute systematic bias testing (e.g. counterfactual fairness, subgroup accuracy checks) and publish metrics. Conduct ethical impact assessments with diverse stakeholders to identify social risks early. Ensure diverse teams and inclusion of non-technical perspectives in design (reflecting UNESCO’s call for inclusiveness).
Formalize Human Oversight: Define clear human-in-the-loop protocols. For high-stakes AI, require sign-off by designated human operators or ethics officers before deployment. Provide channels for users to appeal automated decisions (as advised by the US framework). Include oversight details in training for staff.
Engage with Policymakers and Standards Bodies: Continue monitoring regulatory developments (e.g. US AI “white papers,” future OECD updates, possible UK/Asia regulations). Participate in standards development (e.g. IEEE P7000 series, CEN-CENELEC AI standards). Align product roadmaps to upcoming rules – for example, ensuring any AI introduced in EU by 2025 already meets ‘high-risk’ criteria.
Boost Public Trust: Consider convening an external ethics advisory board or “AI ombudsperson” program. Publish periodic Ethics Impact Reports summarizing AI performance, incidents and remedial actions. Engage with media and customers about FohnAI’s ethics efforts to build brand reputation.

By taking these steps, FohnAI will not only comply with current regulations (EU AI Act, privacy laws, etc.) but also position itself ahead of emerging requirements. Such proactive alignment will reassure investors of robust risk management and build public trust in FohnAI’s long-term vision.

Sources: We relied on public documents from AI industry leaders and global frameworks. For example, OECD Principles on transparency and fairness, UNESCO’s Ethics Recommendation, the EU AI Act’s official guidance, and the U.S. AI Bill of Rights blueprint. Company commitments and policies were drawn from OpenAI’s Charter and transparency pages, Anthropic’s blog posts and ISO certification announcement, and Google’s AI Principles. These references highlight the state-of-the-art in ethical AI practices for benchmarking.