AI Governance & Ethics

Risk Category & Mitigation Strategies

Overview:

This document breaks down the real risks that come with deploying GenAI in creative environments. It draws on the 2023 executive order focused on safe and trustworthy AI, mapping risks like bias, consent, data misuse, and labor displacement. I outlined how each can be addressed through policy, tooling, or structural safeguards. The focus is on practical steps, because if you're building with AI, you're already making governance decisions. My thoughts on the 2025 policy reversal and its impact on this work follow below.

Competitive Landscape

Risk Category

Potential Risk

Impact

Mitigation Strategies

(Microsoft Tools)

Data Privacy

Collection or reuse of personal or proprietary data (e.g., actor likeness, audience metrics) without proper consent or safeguards.

Violation of GDPR, U.S. state laws (e.g., CCPA); legal liability and reputational damage.

Implement privacy-by-design practices as outlined in Microsoft’s Responsible AI Standard; ensure transparency through data documentation and apply Microsoft’s Azure AI Content Safety tools for data filtering and monitoring.

Accuracy

AI-generated content (scripts, storyboards, assets) includes factual inaccuracies or misaligned creative references.

Can lead to production errors, legal missteps (e.g., unintended misrepresentation), or audience trust erosion.

Use Microsoft’s Prompt Flow to evaluate prompt inputs for groundedness and source alignment; include human-in-the-loop reviews; maintain audit logs and editorial controls for high-risk AI-generated outputs.

Bias and Fairness

Generative AI models reproduce gender, racial, or cultural stereotypes in characters or scripts.

Reputational risk, ethical backlash, potential regulatory scrutiny (e.g., under EU AI Act's high-risk classification).

Apply fairness evaluation metrics from Microsoft’s Responsible AI Dashboard; integrate bias audits into the pipeline using Fairlearn or InterpretML (open-source tools backed by Microsoft); enforce internal representation benchmarks in training data.

Ethical Conciderations

AI used to simulate deceased actors’ voices or appearances without informed consent or public disclosure.

Public outrage, regulatory violations (especially in the EU), harm to creative integrity and legacy representation.

Enforce Microsoft’s transparency principles: label AI-generated content using C2PA provenance standards; require written consent for AI likeness use; create an AI Ethics Review Panel (inspired by Microsoft’s Sensitive Use Review process).

Labor-force Ethical Considerations

Exploiting employee creative output to train AI systems, then replacing workers once their contributions are encoded.

Talent loss, reputational damage, union pushback, public backlash over “AI-assisted layoffs.”

Follow Microsoft’s approach to informed consent for data use and Human-AI collaboration design (not replacement); establish artist-AI data contracts and create ethical review boards to define employee contribution policies.

Source and Tools (Microsoft Specific)

Microsoft’s Responsible AI Standard (v2)

Defines privacy-by-design, human oversight, risk assessment, and accountability practices.
Source:

Responsible AI at Microsoft

Azure AI Content Safety

Helps detect unsafe or sensitive content in AI outputs (text and images), and is integrated into Microsoft's AI services.
Source:

Azure AI Content Safety

Prompt Flow

A tool in Azure Machine Learning that lets developers evaluate prompt effectiveness and measure performance against criteria like groundedness.
Source:

What is Prompt Flow

Fairlearn and InterpretML

Open-source tools supported by Microsoft to evaluate and mitigate bias in ML models.
Source:

Fairlearn GitHub InterpretML GitHub

Responsible AI Dashboard

Part of Azure Machine Learning, provides fairness, error, and model explanation tools in one UI.
Source:

Responsible AI Toolbox

C2PA (Coalition for Content Provenance and Authenticity)

Microsoft is a co-founder. Enables AI-generated content to be labeled with metadata for transparency and traceability.
Source:

C2PA Official Site

Microsoft’s Sensitive Use Review Process

Used internally by Microsoft to evaluate high-risk or novel AI applications before deployment.
Source:

"Governing AI: A Blueprint for the Future"

Regulatory Frameworks Referenced

EU AI Act (proposed)

Categorizes AI applications into risk tiers (minimal to high risk); applies strict obligations for high-risk systems like biometric recognition or emotionally manipulative AI.
Source:

European Commission AI Act Proposal (EU AI Act)

U.S. Executive Order on Safe, Secure, and Trustworthy AI (Oct 2023)

Sets out requirements for red teaming, watermarking, and content disclosures for frontier models and AI in high-impact domains.
Source:

Executive Order 14110 (October 2023)

Between Regulation and Acceleration

Executive Order 14110: Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (Biden, 2023)

This order established a comprehensive framework for AI governance, emphasizing:

Risk Mitigation: Mandated safety testing for high-risk AI systems, with developers required to report results to the federal government under the Defense Production Act.
Civil Rights Protections: Directed agencies to enforce existing laws to prevent AI-induced discrimination and bias.
Transparency and Accountability: Promoted the development of standards for AI-generated content, including watermarking, to ensure clear attribution.
Workforce Impact: Called for assessments of AI's effects on labor markets and strategies to mitigate potential job displacement.
Federal Coordination: Required federal agencies to appoint Chief AI Officers and develop strategies for responsible AI use.

This order aimed to support AI progress while putting real guardrails in place. It focused on balancing innovation with responsibility, especially around civil rights and public trust.

Executive Order 14179: Removing Barriers to American Leadership in Artificial Intelligence (Trump, 2025)

In contrast, this order rescinded EO 14110 and shifted focus toward:

Deregulation: Eliminated what it characterized as "unnecessary bureaucratic restrictions," aiming to accelerate AI development and deployment.
Innovation Emphasis: Prioritized rapid AI advancement to maintain global competitiveness, with less emphasis on preemptive risk assessments.
Ideological Neutrality: Stressed the development of AI systems "free from ideological bias," reflecting concerns about perceived social agendas in AI applications.
Agency Autonomy: Directed federal agencies to revise or rescind policies from EO 14110 that were seen as hindering innovation.

This order signaled a significant policy shift, focusing on reducing regulatory hurdles to promote AI leadership.

Implications for AI Governance and Ethics

The transition from EO 14110 to EO 14179 reflects a move away from shared guardrails toward decentralized responsibility. This shift favors speed and autonomy, but raises new questions about how ethical practices will be upheld in the absence of federal standards. Key implications include:

Oversight Models: Without mandated safety testing or reporting structures, accountability now falls to the teams building the tools. That freedom can speed things up, but it also opens the door for gaps that affect real users.
Fairness and Inclusion: The removal of civil rights guidance means there’s no longer a default set of expectations around bias and discrimination. Whether those values make it into the product depends entirely on the team’s choices.
International Standing: A weaker public stance on ethics may impact how the U.S. is viewed by partners who expect more structure and accountability. Speed may win headlines, but trust is what sustains long-term collaboration.

For AI product teams, this shift makes one thing clear. If you want to build responsibly, the framework is going to have to come from you. There’s no longer a default.

"I think AI has the potential to create infinitely stable dictatorships" - Ilya Sutskever, Co Founder OpenAI

Kotter's 8 Steps for Leading Change

Overview:

This deliverable uses Kotter’s 8-step framework to show how a company can take on GenAI adoption without losing sight of creative authorship, process integrity, or real accountability. It lays out a practical plan for how to build internal structure, support contributors, and scale responsibly over time. Each step is grounded in the decisions that teams actually face when integrating these tools into production.

1. Create a Sense of Urgency

CompanyX is at a critical point in its digital evolution. Generative AI presents an immediate opportunity to transform how we create, deliver, and personalize content across all areas of the business, from storytelling and marketing to production and audience engagement. While these technologies can unlock new levels of efficiency and creative possibility, they also introduce legal, ethical, and reputational risks if not deployed responsibly. The urgency comes not only from the pace of AI development, but from shifting public expectations, tightening regulations, and growing competitive pressure. To lead with integrity and stay ahead of the market, CompanyX must act now to implement governance structures, define ethical boundaries, and support creative contributors in shaping the future of AI, rather than being displaced by it.

2. Build a Guiding Coalition

CompanyX will bring together a core group of people from across the business who actually understand the real-world impact of AI. That means leaders from creative development, legal, technology, and marketing, but also people who are closer to the work itself. Artists. Writers. Engineers. People who have already worked with these tools and know what’s at stake. This group will be responsible for shaping how AI is used, how it’s governed, and how we protect the integrity of our creative process while moving forward. It will be anchored by the Responsible AI Office, with senior sponsorship from our heads of Technology, Legal, and Creative. This coalition won’t just sign off on plans. It will help shape policy, guide ethics reviews, and serve as a check on where things are moving too fast or not fast enough.

3. Form a Strategic Vision

The future we’re building toward doesn’t replace creativity…It amplifies it. Right now, there’s a lot of uncertainty about how AI will impact the people who actually make things. Artists. Writers. Performers. Engineers. The strategic vision is to put those people at the center of how we adopt and govern these tools. That means shifting from a model that treats AI as a way to cut costs to a model that uses AI to support original thinking, streamline the repetitive, and create space for better work.

To get there, CompanyX will invest in three areas. First, we will build clear internal policies for how AI can be used, especially around creative contributions and data sources. Second, we will develop licensing frameworks that allow artists and other contributors to opt in, get credited, and get paid when their work informs or trains AI. Third, we will create dedicated review processes for high-impact use cases, with real checkpoints and human oversight baked in.

This ensures that we are protecting the integrity of the work and building systems that respect the creative process and the people behind it.

4. Enlist a Volunteer Army

We will start with the people who are closest to the work. That includes artists, engineers, writers, and operations leads who are already using AI tools or are directly affected by them. They understand the stakes better than anyone else in the organization.
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Their input will be used to shape pilot programs, guide use case approvals, and pressure-test what is being built. We will bring them into real conversations, not just focus groups. They will help define what gets tested, what gets paused, and what moves forward.
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This effort will grow by showing people their ideas are taken seriously. We will highlight contributions, share progress, and keep communication open. The more we involve people early, the more grounded and practical this rollout will be.

5. Enable Action by Removing Barriers

A lot of teams are waiting for direction before they act. Others are working with AI tools but don't know where the lines are. That slows everything down. We need to give people the clarity and support to move forward with confidence.
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That starts with defining where AI can be used, where it can’t, and what needs review before deployment. It also means giving people access to shared resources, reference use cases, and actual points of contact for questions. Teams should not have to guess whether something is okay. They should know who to ask and how to get approval.
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We also need to make sure this work is not buried under layers of policy that no one has time to read. Guidelines should be clear. Oversight should be structured. Reviews should be fast and fair. When something needs to be paused or reworked, it should happen with input from the people who understand the risk.

The goal is to create a system that people can actually use.

6. Generate Short-Term Wins

We’ll start by building internal LLMs using datasets that CompanyX already owns and can verify. These models will be trained on curated content that meets all governance and ethics standards. This will allow teams to experiment with tools that are fully in our control, without relying on public models or vendor black boxes.
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These early systems can support specific workflows like internal search, production asset tagging, or creative reference. They will also help us test infrastructure, privacy controls, and review processes at a scale we can manage. As results come in, we’ll document what works and where gaps need to be filled. Teams will be encouraged to share outcomes with each other to build shared knowledge.
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Progress will come from creating tools that solve real problems and meet clear internal standards. That’s how we’ll build momentum without cutting corners.

7. Sustain Acceleration

Once we see early signs of what works, we’ll keep pushing by investing in the systems that make those wins easier to repeat. That includes better access to internal data, clear standards for model development, and support for teams who want to scale what they’ve started.
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We’ll establish a review process that moves fast but still holds the line on quality and accountability. Teams that build useful tools will get the resources they need to improve and expand them. When something is worth scaling, it will be backed by policy, infrastructure, and real support.
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We’re not looking to roll out GenAI everywhere at once. We’re looking to expand where it makes sense, where it helps the work, and where we can show clear results. The more teams that engage with this early, the more we’ll learn about how to do it well.

8. Institute Change

This work becomes real when it shows up in how we operate, how we hire, and how we make decisions. Teams using AI responsibly will be expected to document their process, share results, and follow review steps that are clear and consistent. That structure needs to be in place from the beginning and stay in place as the work scales.
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We’ll update internal training to reflect real use cases from within CompanyX, not just outside examples. We’ll build checkpoints into major decisions that involve AI tools, including creative development, marketing, and workforce planning. These decisions will be reviewed by teams that understand the tools, and the policies.
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Recognition will come from doing the work the right way. That includes protecting data, crediting contributors, and showing where an AI system helped without overstating what it can do. New behavior becomes culture when people see that it’s backed by structure and leadership, and guidance.

AI Risk Assessment CompanyX

Use Case Overview:

CompanyX is adopting generative AI across multiple areas of its global media business to enhance content development, streamline operations, and support creative teams at scale. Applications include generating early visual concepts, assisting with scriptwriting and dialogue passes, enabling dynamic content tagging for archives and streaming, and supporting real-time personalization in digital experiences. AI systems are also being piloted in marketing, customer support, and internal talent workflows, including scheduling and casting support. These systems are trained on a mix of proprietary creative assets, historical media libraries, production data, and externally licensed datasets. While the goal is to accelerate workflows and reduce manual overhead, CompanyX is also exploring how creative contributors, both past and present, can be fairly credited and compensated when their work informs AI training and generation.

AI System Details

Type of AI Systems:

Generative AI (Large Language Models - LLMs)
Generative AI (Image and Visual Content Models/GANs)
Recommendation Systems / Personalization Engines
Classification Models / Tagging Algorithms
Conversational AI / Chatbots / Voice Assistants
Optimization Algorithms (Scheduling and Staffing)
Content Moderation Models
Sentiment Analysis / Social Listening Models

Technology Provider

CompanyX sources its generative AI tools from a range of external technology providers. These include OpenAI for large language models accessed through Microsoft Azure, along with other model developers and service vendors that supply tools for image generation, content tagging, translation, and marketing automation. Some departments use AI features embedded within third-party software platforms, such as creative suites, CRM systems, or analytics tools. All technology providers are evaluated for alignment with CompanyX's internal standards, including data handling practices, licensing terms, and adherence to responsible AI development principles.

Internal or Third-Party Deployment:

AI systems at CompanyX are deployed through a combination of internal infrastructure and third-party cloud platforms. Models that handle sensitive creative content or internal data are hosted in secure, company-managed environments with strict access controls. Public-facing or large-scale models, including those used for customer interactions, personalization, or content recommendations, are typically accessed through external cloud platforms. Some business units operate hybrid deployments, where third-party tools are integrated into internal workflows. In all cases, operational responsibility is clearly assigned, and deployment decisions are governed by internal compliance policies, usage controls, and ongoing audit requirements.

Department / Team Owner:

Ownership of AI systems at CompanyX is distributed across multiple departments depending on the use case. Enterprise-wide oversight is coordinated by a central Responsible AI Office, which sets governance standards, reviews sensitive applications, and ensures compliance with legal and ethical guidelines. Within business units, ownership is assigned to functional teams such as Studio Technology, Consumer Products Innovation, Marketing Analytics, or Parks and Experiences Technology. Each team is accountable for the safe and compliant use of AI within their domain and collaborates with the Responsible AI Office to ensure systems meet internal standards and applicable regulations. For vendor-integrated tools or cloud-hosted models, ownership also includes vendor management and usage auditing responsibilities.

Stakeholders Impacted

Creative Professionals:

Includes animators, illustrators, visual development artists, writers, storyboard artists, voice actors, on-screen talent, musicians, composers, and production designers. These individuals may have their work used to train models or find their creative roles reshaped by automation and AI-assisted tools.

Operational Employees:

Staff involved in scheduling, project coordination, customer service, localization, and logistics whose workflows may be restructured or optimized through AI.

Technology Teams:

Engineers, machine learning specialists, product managers, and platform teams who design, test, and maintain AI systems across the company.

Vendors and Contractors:

External contributors, such as third-party studios, music licensors, localization firms, and marketing agencies who may interact with AI-driven tools or have their outputs evaluated or replaced by automated systems.

Customers and Guests:

Individuals who engage with CompanyX products, services, or experiences that are informed or customized by AI, including content recommendations, personalized media, or park interactions.

Audience and Public Viewers:

Members of the general public who consume content that may involve AI-generated performances, likenesses, or editorial decisions.

Legal, Ethics, and Compliance Teams:

Stakeholders who evaluate internal use cases for compliance with regulations such as GDPR, the AI Act, and U.S. federal or state privacy laws.

Executives and Strategic Leadership:

Company leaders who make investment decisions, set ethical guidelines, and own the long-term vision for AI integration.

Risk Categories Checklist

☑ Ethical

AI may replicate or amplify systemic bias in casting, character representation, or creative outputs. There are also concerns about exploiting labor, such as using an artist’s or actor’s work to train models without their knowledge or permission.

☑ Legal

AI-generated content may raise intellectual property issues involving likeness, voice, music, or visual assets. Global regulations, including the EU AI Act and U.S. privacy laws, may apply if systems are not auditable or lack meaningful human oversight.

☑ Operational

Introducing AI into production, scheduling, or marketing may create dependencies on systems that are difficult to interpret, prone to hallucination, or fragile under real-world use. If teams are not aligned or trained properly, AI could disrupt workflows instead of improving them.

☑ Reputational

There is a risk of public backlash if creative data is used without consent or if generative systems are linked to job displacement. Controversial or tone-deaf uses of AI, such as synthetic performances or culturally insensitive content, could damage public trust.

☑ Data Privacy / Security

Using personal data to train or deploy AI systems introduces risk under laws like GDPR, CCPA, and other region-specific frameworks. Failure to safeguard user or employee data could result in regulatory fines and harm to brand credibility.

☑ Societal / Cultural

CompanyX operates on a global stage. If generative models reinforce cultural stereotypes or produce content that lacks sensitivity to diverse audiences, the company could face criticism or be seen as perpetuating harm rather than promoting inclusive storytelling.

☑ Environmental

Running large models at scale requires significant energy and compute power. If CompanyX increases AI use without monitoring its environmental footprint, the initiative could conflict with existing sustainability goals or public commitments to climate action.

Identified Risks

Risk Category

Stakeholders Affected

Likelihood

Risk Description

AI-generated characters or scripts unintentionally reinforce racial, gender, or cultural stereotypes

Ethical

Creative professionals, audience, public

Medium

Use of legacy creative assets to train AI without explicit consent from the original contributors

Legal, Ethical

Artists, writers, performers, legal teams

High

AI scheduling system misaligns with union agreements or legal limits on part-time labor

Operational, Legal

Operational staff, legal teams, unions

Medium

Public controversy over the use of synthetic performances or likeness in entertainment media

Reputational

Audience, talent, marketing, public relations

Medium

Personal data used in personalization models is not properly anonymized or consented

Data Privacy / Security

Customers, compliance teams

Impact

High

Medium

Generative AI produces culturally insensitive content for international markets

Societal / Cultural

Global audience, localization teams

Medium

AI systems increase compute demands and energy use, straining data center sustainability efforts

Environmental

Infrastructure teams, sustainability leadership

High

Mitigation Strategies

Risk Category

Mitigation Action

Owner / Team

Timeline

Risk Description

AI-generated characters or scripts unintentionally reinforce racial, gender, or cultural stereotypes

Implement bias audits using tools such as Microsoft Fairlearn and involve diverse review panels

Creative Development + Responsible AI Team

Q2 2025

Use of legacy creative assets to train AI without explicit consent from the original contributors

Develop a contributor licensing policy and consent workflow tied to dataset curation

Legal + Studio Asset Management

Q2 2025

AI scheduling system misaligns with union agreements or legal limits on part-time labor

Integrate labor contract compliance checkpoints into AI scheduling systems

HR Tech + Legal Ops

Q1 2025

Public controversy over the use of synthetic performances or likeness in entertainment media

Enforce transparency and labeling standards based on C2PA guidelines and internal ethical review

Public Affairs + Ethics Board

Q2 2025

Personal data used in personalization models is not properly anonymized or consented

Deploy Azure AI Content Safety and ensure anonymization pipelines are enforced before model training

Data Governance + IT Security

Q2 2025

Generative AI produces culturally insensitive content for international markets

Incorporate international sensitivity reviewers during model fine-tuning and testing

Localization + DEI Council

Ongoing (starting Q2 2025)

AI systems increase compute demands and energy use, straining data center sustainability efforts

Audit compute usage per model and align with sustainability targets using Microsoft sustainability tools

Infrastructure + Sustainability Lead

Annual Review (beginning FY 2025)

Monitoring & Evaluation Plan

Post-deployment risk will be monitored through a combination of technical oversight, stakeholder review, and scheduled audits. Each department deploying AI systems is responsible for maintaining documentation, performance logs, and incident reports tied to their specific use case. The Responsible AI Office will coordinate quarterly reviews to assess compliance with internal standards, evaluate any reported issues, and track ongoing risk trends. In addition, automated monitoring tools such as Azure AI Content Safety and model usage dashboards will be used to flag anomalies, bias drift, or performance degradation. Any escalated risks or unresolved issues will be brought to the Responsible AI Council for further evaluation and corrective action.

Compliance & Governance References

Applicable Policies / Regulations

General Data Protection Regulation (GDPR) – EU
California Consumer Privacy Act (CCPA)
New York City Biometric Identifier Information Law
U.S. Executive Order on Safe, Secure, and Trustworthy AI (2023)
EU Artificial Intelligence Act (proposed)
Equal Employment Opportunity Commission (EEOC) AI Enforcement Guidance
National Institute of Standards and Technology (NIST) AI Risk Management Framework (2023)

Internal Policy Alignment

CompanyX Responsible AI Principles
Data Privacy and Handling Guidelines
Internal Model Use and Review Protocol
Creative Contributor Licensing Framework (in development)
Departmental AI Use Case Review Process
Responsible AI Council Charter

Documentation Maintained

Data Protection Impact Assessments (DPIAs)
AI Use Case Intake Forms
Sensitive Use Case Review Logs
Audit Trails and System Access Logs
Transparency Notes for Public-Facing AI Systems
Red Teaming Reports and Post-Deployment Monitoring Logs

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