Fundamentals

Consider the local bakery, struggling to keep pace with online giants. They’re not thinking about algorithmic bias; they’re thinking about keeping the lights on. AI governance, often perceived as a boardroom concern, actually begins with the daily grind of small and medium businesses (SMBs).

It’s not an abstract concept reserved for tech titans; it’s the practical framework for how any business, regardless of size, uses artificial intelligence responsibly and effectively. Ignoring this framework is akin to driving a car without understanding the rules of the road ● accidents are inevitable, and often costly.

Demystifying Governance for Main Street

Governance, in its simplest form, is about establishing rules and responsibilities. Think of it as the operating system for your business’s AI initiatives. For an SMB, AI governance Meaning ● AI Governance, within the SMB sphere, represents the strategic framework and operational processes implemented to manage the risks and maximize the business benefits of Artificial Intelligence. doesn’t necessitate a sprawling bureaucracy or a team of ethicists. Instead, it’s about embedding common-sense principles into how AI tools are selected, implemented, and managed.

This might involve something as straightforward as ensuring customer data used for a marketing campaign is securely stored and used only for its intended purpose. Or, it could mean establishing a clear process for employees to report concerns about AI-driven decisions that seem unfair or inaccurate.

AI governance for SMBs is about building trust ● trust with customers, trust with employees, and trust in the technology itself.

Many SMB owners might view AI as something futuristic, detached from their immediate concerns. They might think governance is a problem for ‘big tech’, not for their corner store or local service business. This perspective is dangerously shortsighted. AI is rapidly permeating everyday business tools, from customer relationship management (CRM) systems that predict customer behavior to accounting software that automates financial reporting.

Even basic website chatbots utilize AI. The question is not whether SMBs will use AI, but how responsibly and ethically they will use it.

Core Elements ● A Practical Starting Point

So, what are the core business elements of AI governance for an SMB just starting out? It boils down to a few key areas, all interconnected and mutually reinforcing.

Data Responsibility

Data fuels AI. For SMBs, data responsibility Meaning ● Data Responsibility, within the SMB sphere, signifies a business's ethical and legal obligation to manage data assets with utmost care, ensuring privacy, security, and regulatory compliance throughout its lifecycle. is paramount. It means understanding what data you collect, where it comes from, how it’s used, and who has access to it. This isn’t about becoming a data scientist overnight.

It’s about asking basic questions ● Do you know what customer information your website collects? Is this information protected from unauthorized access? Are you transparent with customers about how their data is being used? Simple steps, such as implementing strong passwords and regularly backing up data, are foundational elements of data responsibility.

Algorithm Awareness

Algorithms are the engines of AI. SMBs don’t need to understand the intricate mathematics behind machine learning. However, they should have a basic awareness of how algorithms work in the AI tools they use. For example, if you’re using AI-powered recruitment software, understand how it filters candidates.

Does it inadvertently discriminate against certain demographics? Algorithm awareness means asking vendors about the ‘black box’ of their AI systems and demanding transparency in how decisions are made. It’s about ensuring algorithms are tools that augment human judgment, not replace it blindly.

Human Oversight

AI should serve humans, not the other way around. Human oversight Meaning ● Human Oversight, in the context of SMB automation and growth, constitutes the strategic integration of human judgment and intervention into automated systems and processes. is a critical element of AI governance. In an SMB context, this means ensuring humans are always in the loop when AI systems are making decisions that impact customers or employees. For instance, if an AI chatbot handles customer service Meaning ● Customer service, within the context of SMB growth, involves providing assistance and support to customers before, during, and after a purchase, a vital function for business survival. inquiries, there should be a clear path for customers to escalate complex issues to a human agent.

Similarly, if AI is used to evaluate employee performance, human managers should review and validate AI-driven assessments. Human oversight ensures accountability and prevents AI from becoming an unchecked, autonomous force within the business.

Ethical Considerations

Ethics in AI isn’t some lofty philosophical debate for SMBs. It’s about aligning AI use with basic moral principles and business values. Does your use of AI respect customer privacy? Does it promote fairness and avoid discrimination?

Does it contribute to the overall well-being of your community? For a small business, ethical considerations might involve deciding not to use AI for invasive customer surveillance or ensuring AI-driven marketing campaigns are truthful and not manipulative. It’s about building an AI strategy that reflects your business’s commitment to doing the right thing.

These core elements ● data responsibility, algorithm awareness, human oversight, and ethical considerations ● are not isolated pillars. They are interconnected components of a holistic AI governance framework for SMBs. Implementing them doesn’t require a massive overhaul of business operations.

It starts with small, incremental steps, guided by a commitment to responsible and ethical AI Meaning ● Ethical AI for SMBs means using AI responsibly to build trust, ensure fairness, and drive sustainable growth, not just for profit but for societal benefit. use. The journey of a thousand miles begins with a single step, and for SMBs, that first step in AI governance can be surprisingly simple and immediately beneficial.

Consider a local coffee shop implementing a simple AI-powered loyalty program. Data responsibility means securely storing customer purchase history and contact information. Algorithm awareness involves understanding how the program’s AI personalizes offers and recommendations. Human oversight ensures staff can address customer issues with the program and override AI suggestions when necessary.

Ethical considerations dictate that the program is transparent, respects customer privacy, and offers genuine value. Even in this basic example, the core elements of AI governance are at play, demonstrating their relevance to even the smallest of businesses.

Ignoring these fundamentals is akin to building a house on a shaky foundation. The initial structure might appear sound, but over time, cracks will appear, and the entire edifice could crumble. For SMBs venturing into the world of AI, establishing a solid foundation of governance is not an optional extra; it’s the bedrock of sustainable and responsible AI Meaning ● Responsible AI for SMBs means ethically building and using AI to foster trust, drive growth, and ensure long-term sustainability. adoption. It’s about building for the future, not just for today.

By embracing these core elements, SMBs can transform AI governance from a daunting concept into a practical, value-driven business practice.

What begins as simple data management and ethical reflection evolves into a competitive advantage, a source of customer trust, and a driver of sustainable growth. The fundamentals of AI governance are not a barrier to entry for SMBs; they are the very keys to unlocking AI’s potential responsibly and effectively. It’s time for Main Street to claim its stake in the AI revolution, one governed step at a time.

Intermediate

Beyond the basics, AI governance for SMBs moves into a more nuanced terrain, one demanding a deeper understanding of risk, compliance, and strategic alignment. While the ‘mom and pop’ bakery might grasp data security, the growing regional bakery chain must navigate data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. regulations, algorithmic bias Meaning ● Algorithmic bias in SMBs: unfair outcomes from automated systems due to flawed data or design. in larger-scale operations, and the strategic implications of AI across multiple locations. The stakes elevate, and so must the sophistication of AI governance.

Risk Management and Compliance ● Navigating the Maze

For SMBs in an intermediate stage of AI adoption, risk management Meaning ● Risk management, in the realm of small and medium-sized businesses (SMBs), constitutes a systematic approach to identifying, assessing, and mitigating potential threats to business objectives, growth, and operational stability. becomes a central pillar of governance. This entails identifying, assessing, and mitigating potential risks associated with AI deployment. These risks extend beyond data breaches to encompass algorithmic bias leading to discriminatory outcomes, reputational damage from AI errors, and compliance failures with evolving regulations like GDPR or CCPA. A systematic approach to risk management is no longer optional; it’s a business imperative.

Developing a Risk Framework

SMBs should develop a tailored risk framework for AI. This framework needn’t be overly complex but should systematically address key risk areas. Start by mapping AI applications across the business. Where is AI being used or planned for use?

Marketing? Operations? HR? For each application, identify potential risks.

For example, AI-powered customer service chatbots might misinterpret customer requests, leading to frustration and negative reviews ● a reputational risk. AI-driven inventory management systems might make inaccurate predictions, resulting in stockouts or overstocking ● an operational risk. AI-based hiring tools could perpetuate existing biases in recruitment ● a legal and ethical risk.

Once risks are identified, assess their likelihood and potential impact. A simple risk matrix, categorizing risks as low, medium, or high impact and likelihood, can be a useful tool. Prioritize mitigation efforts based on this assessment. High-impact, high-likelihood risks demand immediate attention.

For instance, if an SMB is using AI to process sensitive customer data and lacks robust security measures, the risk of a data breach is high and the impact potentially catastrophic. Mitigation might involve investing in enhanced cybersecurity, implementing data encryption, and conducting regular security audits.

Compliance as a Governance Driver

Compliance with data privacy regulations Meaning ● Data Privacy Regulations for SMBs are strategic imperatives, not just compliance, driving growth, trust, and competitive edge in the digital age. and emerging AI-specific laws acts as a significant driver for AI governance. GDPR, CCPA, and similar regulations mandate specific requirements for data handling, transparency, and individual rights related to automated decision-making. SMBs operating internationally or even nationally must understand and adhere to these regulations.

Compliance isn’t just about avoiding fines; it’s about building customer trust and demonstrating responsible data practices. AI governance frameworks Meaning ● AI Governance Frameworks for SMBs: Structured guidelines ensuring responsible, ethical, and strategic AI use for sustainable growth. should incorporate compliance requirements as core elements, ensuring AI systems are designed and operated in accordance with legal obligations.

Consider the use of AI in marketing. Regulations like GDPR require explicit consent for collecting and using personal data for marketing purposes. SMBs using AI-powered marketing automation tools must ensure they have obtained valid consent and provide clear opt-out mechanisms.

Furthermore, they need to be transparent about how AI is used to personalize marketing messages and target specific customer segments. Compliance becomes an integral part of the AI governance framework, shaping data handling practices and algorithmic transparency.

Risk management and compliance are not constraints on AI innovation; they are enablers of sustainable and responsible AI adoption.

By proactively addressing risks and embedding compliance into AI governance, SMBs can build a stronger foundation for leveraging AI’s benefits while minimizing potential downsides. It’s about playing the long game, ensuring AI initiatives are not only innovative but also resilient and trustworthy.

Ethical Frameworks and Value Alignment

Moving beyond basic ethical considerations, intermediate AI governance for SMBs involves developing more structured ethical frameworks Meaning ● Ethical Frameworks are guiding principles for morally sound SMB decisions, ensuring sustainable, reputable, and trusted business practices. and aligning AI initiatives with core business values. This is about moving from reactive ethical considerations to proactive ethical design. It’s about embedding ethical principles into the very DNA of AI systems and business processes.

Developing Ethical Guidelines

SMBs should develop clear ethical guidelines for AI development and deployment. These guidelines should be more than just aspirational statements; they should be practical, actionable, and integrated into AI-related workflows. Start by defining core ethical principles relevant to your business. These might include fairness, transparency, accountability, privacy, and non-discrimination.

Translate these principles into concrete guidelines. For example, the principle of fairness might translate into a guideline that AI systems used for decision-making should be regularly audited for bias and fairness across different demographic groups. The principle of transparency might mean that customers should be informed when AI is being used to make decisions that affect them, and they should have access to explanations of those decisions.

These ethical guidelines should be communicated throughout the organization and integrated into training programs for employees involved in AI-related activities. They should also inform the selection of AI vendors and the development of internal AI systems. When evaluating AI solutions, SMBs should ask vendors about their ethical practices and how their systems address potential ethical concerns. Ethical considerations should be a key criterion in the AI vendor selection process.

Value-Driven AI Deployment

AI governance should ensure that AI deployment is aligned with the overall values and mission of the SMB. AI should be used to advance the business’s core purpose and contribute to positive outcomes for stakeholders ● customers, employees, and the community. This means moving beyond a purely technological or efficiency-driven approach to AI and adopting a more value-driven perspective.

Consider how AI can be used to enhance customer experience, improve employee well-being, or contribute to sustainability goals. AI governance should guide AI deployment towards these value-aligned objectives.

For instance, an SMB committed to sustainability might use AI to optimize energy consumption in its operations, reduce waste in its supply chain, or develop eco-friendly products and services. An SMB that values customer service might use AI to personalize customer interactions, provide proactive support, and build stronger customer relationships. AI governance, in this context, becomes a mechanism for ensuring AI is a force for good, aligned with the business’s ethical compass and contributing to its broader societal impact.

Ethical frameworks and value alignment transform AI governance from a risk mitigation exercise into a strategic opportunity for ethical innovation and competitive differentiation.

By proactively embedding ethics into AI and aligning AI deployment with core values, SMBs can build trust, enhance their reputation, and create a more sustainable and responsible business model in the age of AI. It’s about building an AI future that is not only technologically advanced but also ethically grounded and human-centered.

Organizational Structures and Responsibilities

As AI adoption Meaning ● AI Adoption, within the scope of Small and Medium-sized Businesses, represents the strategic integration of Artificial Intelligence technologies into core business processes. matures, SMBs need to establish clearer organizational structures and responsibilities for AI governance. This is about moving beyond ad-hoc approaches to a more formalized and structured governance framework. It’s about defining roles, assigning responsibilities, and creating processes for effective AI oversight.

Defining Roles and Responsibilities

In the early stages, AI governance might be the responsibility of a single individual or a small team. As AI becomes more pervasive, SMBs need to distribute governance responsibilities across the organization. This doesn’t necessarily mean creating a dedicated ‘AI governance department’. It’s about integrating governance responsibilities into existing roles and functions.

For example, the IT department might be responsible for data security and infrastructure related to AI systems. The marketing department might be responsible for ensuring ethical and compliant use of AI in marketing Meaning ● AI in Marketing empowers SMBs to understand customers deeply, personalize experiences, and optimize campaigns ethically for sustainable growth. campaigns. HR might be responsible for addressing ethical considerations in AI-driven HR processes.

Clearly define roles and responsibilities for different aspects of AI governance. Who is responsible for data privacy compliance? Who is responsible for monitoring algorithmic bias? Who is responsible for responding to ethical concerns raised by employees or customers?

Document these responsibilities and communicate them across the organization. This clarity of roles and responsibilities is essential for effective AI governance and accountability.

Establishing Governance Processes

Beyond roles and responsibilities, SMBs need to establish clear processes for AI governance. This includes processes for AI project approval, risk assessment, ethical review, incident response, and ongoing monitoring and evaluation of AI systems. These processes should be documented, communicated, and regularly reviewed and updated. A well-defined AI governance process ensures consistency, transparency, and accountability in AI-related activities.

For instance, the AI project approval process might involve a checklist of governance considerations that must be addressed before a project can proceed. The ethical review process might involve a review board or committee that assesses the ethical implications of new AI applications. The incident response process should outline steps to be taken in case of AI-related incidents, such as data breaches, algorithmic errors, or ethical violations. These processes provide a structured framework for managing AI governance and ensuring it is integrated into the business’s operational fabric.

Organizational structures and responsibilities transform AI governance from a reactive function into a proactive and integrated business capability.

By establishing clear roles, responsibilities, and processes, SMBs can build a more robust and scalable AI governance framework. This framework enables them to manage the complexities of AI adoption effectively, mitigate risks proactively, and ensure AI contributes to their business objectives in a responsible and ethical manner. It’s about building an organizational culture of AI responsibility, where governance is not an afterthought but an integral part of how the business operates.

As SMBs navigate the intermediate stages of AI governance, they move from basic awareness to strategic implementation. Risk management, ethical frameworks, and organizational structures become critical elements, shaping a more mature and sophisticated approach to AI. This evolution is not just about managing AI; it’s about harnessing its power responsibly and ethically, driving sustainable growth Meaning ● Sustainable SMB growth is balanced expansion, mitigating risks, valuing stakeholders, and leveraging automation for long-term resilience and positive impact. and building trust in an increasingly AI-driven world.

Advanced

At the apex of AI governance for SMBs lies a realm of strategic integration, competitive differentiation, and proactive ethical leadership. The regional bakery chain, now national, confronts the complexities of AI across a vast network, grappling with algorithmic accountability Meaning ● Taking responsibility for algorithm-driven outcomes in SMBs, ensuring fairness, transparency, and ethical practices. at scale, navigating intricate regulatory landscapes spanning multiple jurisdictions, and leveraging AI governance as a strategic asset. The challenge shifts from basic compliance to establishing AI governance as a source of sustained competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. and ethical preeminence.

Strategic Integration of AI Governance ● Beyond Compliance

Advanced AI governance transcends mere compliance; it becomes deeply interwoven with the SMB’s overarching business strategy. This integration signifies a shift from viewing governance as a reactive necessity to recognizing it as a proactive driver of innovation, efficiency, and competitive advantage. It’s about embedding governance principles into the strategic DNA of the organization, shaping its AI trajectory and overall business direction.

Governance as a Strategic Enabler

In this advanced stage, AI governance is not perceived as a constraint but as a strategic enabler. It provides a framework for responsible AI innovation, fostering trust among stakeholders ● customers, employees, investors, and regulators ● and unlocking the full potential of AI. A robust governance framework reduces risks, enhances transparency, and builds confidence, creating a conducive environment for AI adoption and expansion. It becomes a catalyst for strategic AI initiatives, rather than a barrier.

Consider how governance can enable strategic AI deployment in customer experience. By establishing clear ethical guidelines and data privacy protocols, an SMB can confidently deploy AI-powered personalization technologies to enhance customer interactions without compromising trust. Governance provides the guardrails for responsible innovation, allowing the business to push the boundaries of AI application while mitigating potential ethical and reputational risks. It’s about using governance to unlock strategic opportunities, not just to avoid pitfalls.

Competitive Differentiation through Governance

Advanced AI governance can become a source of competitive differentiation. In a marketplace increasingly sensitive to ethical and responsible AI, SMBs with robust governance frameworks gain a distinct advantage. Customers are more likely to trust and engage with businesses that demonstrate a commitment to ethical AI practices. Investors are increasingly scrutinizing ESG (Environmental, Social, and Governance) factors, including AI governance, when making investment decisions.

Regulators are placing greater emphasis on AI accountability and transparency. A strong AI governance framework can enhance brand reputation, attract investors, and foster customer loyalty, creating a competitive edge.

SMBs can actively communicate their AI governance practices to stakeholders, highlighting their commitment to responsible AI. This transparency builds trust and differentiates them from competitors who may lack a comparable governance framework. Certifications and independent audits of AI governance practices can further enhance credibility and provide tangible evidence of commitment. In an era where ethical AI is becoming a key differentiator, advanced governance is not just a cost of doing business; it’s a strategic investment in competitive advantage.

Strategic integration of AI governance transforms it from a cost center to a profit center, a source of competitive advantage and sustainable growth.

By strategically embedding governance into their AI initiatives, SMBs can unlock new avenues for innovation, build stronger stakeholder relationships, and differentiate themselves in the marketplace. It’s about recognizing that responsible AI is not just ethically sound; it’s strategically smart.

Algorithmic Accountability and Explainability at Scale

As AI systems become more complex and pervasive, advanced AI governance must address algorithmic accountability and explainability at scale. This entails establishing mechanisms to ensure AI systems are not only effective but also fair, transparent, and accountable, even when operating across vast datasets and intricate decision-making processes. It’s about moving beyond basic algorithm awareness to sophisticated algorithmic oversight.

Developing Explainable AI (XAI) Capabilities

Explainable AI (XAI) becomes a critical capability in advanced AI governance. SMBs need to invest in tools and techniques that enable them to understand how their AI systems arrive at decisions, particularly in high-stakes applications. This is not just about technical explainability for data scientists; it’s about business-level explainability for decision-makers and stakeholders. Can business users understand why an AI system made a particular recommendation?

Can customers understand why they were denied a loan based on an AI credit scoring model? XAI is about bridging the gap between complex AI algorithms and human understanding.

Implementing XAI might involve using interpretable machine learning models, developing explanation interfaces for AI systems, and establishing processes for documenting and communicating AI decision-making logic. It also requires training employees to understand and interpret AI explanations. XAI is not a one-size-fits-all solution; the level of explainability required will vary depending on the application and the context. However, in advanced AI governance, a commitment to explainability is paramount, particularly in areas where AI decisions have significant impact on individuals or businesses.

Establishing Algorithmic Audit and Monitoring

Algorithmic accountability requires robust audit and monitoring mechanisms. SMBs need to establish processes for regularly auditing their AI systems to assess their performance, fairness, and compliance with ethical guidelines and regulations. This goes beyond simple performance monitoring; it involves scrutinizing algorithms for bias, discrimination, and unintended consequences. Algorithmic audits should be conducted by independent experts or internal audit teams with specialized expertise in AI ethics Meaning ● AI Ethics for SMBs: Ensuring responsible, fair, and beneficial AI adoption for sustainable growth and trust. and governance.

Monitoring should be ongoing and proactive, not just reactive to incidents. Establish key performance indicators (KPIs) for AI governance, including metrics related to fairness, transparency, and accountability. Regularly track these KPIs and identify any deviations or anomalies that require investigation.

Algorithmic audit and monitoring provide assurance that AI systems are operating as intended and in accordance with ethical and governance principles. They are essential for maintaining algorithmic accountability at scale.

Algorithmic accountability and explainability transform AI governance from a reactive control mechanism into a proactive assurance function, building trust and confidence in AI systems.

By investing in XAI capabilities and establishing robust audit and monitoring processes, SMBs can ensure their AI systems are not only powerful but also trustworthy and accountable. It’s about building a culture of algorithmic responsibility, where transparency and fairness are integral to AI development and deployment.

Dynamic Governance and Adaptive Frameworks

The landscape of AI technology and regulation is constantly evolving. Advanced AI governance requires dynamic and adaptive frameworks that can keep pace with this rapid change. This means moving beyond static policies and procedures to establishing flexible governance mechanisms that can adapt to new technologies, emerging risks, and evolving societal expectations. It’s about building governance for a world of constant AI flux.

Implementing Agile Governance Processes

Agile governance processes are essential for adapting to the dynamic AI landscape. Traditional, rigid governance frameworks can become quickly outdated in the face of rapid technological advancements. Agile governance Meaning ● Dynamic capability for SMBs to proactively steer agile initiatives for strategic value and innovation. involves iterative development, continuous feedback loops, and a willingness to adapt governance policies and procedures as needed. This requires a shift from a ‘set-and-forget’ approach to a ‘continuously improve’ mindset.

Implement agile methodologies in AI governance. Regularly review and update governance policies and procedures based on new technological developments, regulatory changes, and stakeholder feedback. Establish feedback mechanisms to gather input from employees, customers, and external experts on AI governance practices.

Foster a culture of continuous learning and adaptation in AI governance. Agile governance ensures that the governance framework remains relevant and effective in a rapidly changing AI environment.

Building Adaptive Governance Structures

Adaptive governance structures are needed to support dynamic governance Meaning ● Dynamic Governance for SMBs is a flexible leadership and operational system enabling swift response to change and fostering sustained growth. processes. This might involve creating cross-functional AI governance committees with representatives from different departments, empowering these committees to make timely decisions and adapt governance policies as needed. It could also involve leveraging AI itself to enhance governance processes, such as using AI-powered monitoring tools to detect anomalies and emerging risks in AI systems. Adaptive governance Meaning ● Adaptive Governance, within the realm of Small and Medium-sized Businesses, signifies a business management framework capable of dynamically adjusting strategies, processes, and resource allocation in response to evolving market conditions, technological advancements, and internal operational shifts, this business capability allows a firm to achieve stability. structures are designed to be flexible, responsive, and resilient in the face of change.

Consider establishing an AI ethics advisory board composed of internal and external experts to provide ongoing guidance on ethical and governance issues. This board can serve as a sounding board for new AI initiatives, provide independent reviews of governance policies, and help the SMB navigate complex ethical dilemmas. Adaptive governance structures ensure that the governance framework is not only dynamic but also informed by diverse perspectives and expertise.

Dynamic governance and adaptive frameworks transform AI governance from a static rulebook into a living, evolving system that keeps pace with the speed of AI innovation.

By implementing agile processes and building adaptive structures, SMBs can create AI governance frameworks that are not only robust but also resilient and future-proof. It’s about building governance for the long term, ensuring it can adapt and evolve as AI technology continues to advance and reshape the business landscape. Advanced AI governance is not a destination; it’s a continuous journey of adaptation and improvement, ensuring responsible and ethical AI in a world of constant change.

In the advanced stages of AI governance, SMBs move beyond basic risk mitigation and compliance to strategic integration, algorithmic accountability, and dynamic adaptation. Governance becomes a strategic asset, a source of competitive differentiation, and a driver of responsible AI innovation. This advanced approach is not just about managing AI; it’s about leading in the age of AI, setting new standards for ethical and responsible AI practices, and shaping a future where AI benefits businesses and society alike.

References

• Dignum, Virginia. “Responsible Artificial Intelligence ● How to Develop and Use AI in a Responsible Way.” Springer, 2019.
• Jobin, Anna, et al. “The Global Landscape of AI Ethics Guidelines.” Nature Machine Intelligence, vol. 1, no. 9, 2019, pp. 389-99.
• Mittelstadt, Brent Daniel, et al. “The Ethics of Algorithms ● Mapping the Debate.” Big Data & Society, vol. 3, no. 2, 2016, pp. 1-21.
• OECD. “OECD Principles on AI.” OECD, 2019, oecd.org/going-digital/ai/principles/.
• World Economic Forum. “AI Governance ● A Holistic Approach to Implement Ethics and Trust in AI Systems.” World Economic Forum, 2021, weforum.org/reports/ai-governance-a-holistic-approach-to-implement-ethics-and-trust-in-ai-systems.