Fundamentals

Ninety percent of small to medium-sized businesses operate without a dedicated AI strategy, yet nearly all are impacted by AI systems daily, often unknowingly. This isn’t some distant future; it’s the operational reality for countless SMBs right now. Consider the algorithms shaping search engine results that dictate customer discovery, or the automated tools influencing credit scores vital for business loans. These systems, powered by artificial intelligence, are not abstract concepts; they are concrete forces shaping the SMB landscape.

For many SMB owners, the inner workings of these systems remain opaque, a black box influencing their bottom line without clear understanding. How can SMBs navigate this landscape, ensuring these powerful tools serve their interests transparently?

Demystifying Ai For Small Businesses

The term ‘AI’ itself often conjures images of futuristic robots or complex code impenetrable to the average business owner. However, for SMBs, AI frequently manifests in more mundane, accessible forms. Think of customer relationship management (CRM) software that predicts customer churn, or marketing platforms that personalize ad campaigns based on user data. These are AI-driven tools, yes, but they are also practical applications designed to streamline operations and boost efficiency.

Transparency in this context doesn’t demand a PhD in computer science; it starts with understanding what data these systems use, how they process it, and what kind of outputs they generate. It’s about peeling back the layers of technological mystique to reveal the practical mechanics at play.

Transparency for SMBs using AI is less about understanding complex algorithms and more about grasping the practical implications of AI-driven tools on their daily operations and business outcomes.

Why Transparency Matters To Your Bottom Line

Transparency in AI systems is not just an ethical consideration; it’s a strategic business imperative for SMBs. Consider a loan application denied by an AI-powered credit scoring system. Without transparency, the SMB owner is left in the dark, unable to understand the reasons for rejection or identify areas for improvement. This lack of clarity can hinder future growth and access to capital.

Conversely, transparent AI Meaning ● Within the context of SMB growth, automation, and implementation, Transparent AI signifies the design, development, and deployment of artificial intelligence systems that are readily understandable, auditable, and explainable to business users, fostering trust and enabling effective oversight. systems empower SMBs with actionable insights. Imagine a marketing platform that clearly explains why certain ad campaigns are recommended, based on specific data points and predictive models. This transparency allows SMB owners to make informed decisions, optimize their strategies, and ultimately, improve their return on investment. Transparency builds trust, not only with customers but also within the business itself, fostering a culture of data-driven decision-making and continuous improvement.

Simple Steps Towards Ai Clarity

For SMBs taking their first steps towards AI transparency, the journey begins with simple, actionable measures. It’s about adopting a practical, phased approach, rather than attempting an overnight overhaul. Here are initial steps that any SMB can implement:

1. Inventory Your Ai Tools ● Start by identifying all the software and platforms your business uses that might incorporate AI. This includes CRM systems, marketing automation tools, analytics dashboards, and even 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. chatbots. Create a list, even a basic spreadsheet, to keep track of these systems.
2. Ask Basic Questions ● For each AI tool, ask your vendor or service provider fundamental questions. What data does this system collect? How is this data used to generate insights or make decisions? Are there any built-in mechanisms for explaining the system’s outputs? Don’t be afraid to ask for plain language explanations, avoiding technical jargon.
3. Focus On Data Inputs And Outputs ● Transparency at the fundamental level is about understanding the ‘inputs’ and ‘outputs’ of your AI systems. What data goes in, and what results come out? Can you trace the connection between the two? This basic understanding is crucial for identifying potential biases or inaccuracies in the system’s decisions.
4. Document Your Processes ● Even simple documentation can significantly enhance transparency. Keep records of your conversations with vendors, the answers you receive about AI systems, and any internal processes you establish for monitoring AI usage. This documentation serves as a valuable resource for future reference and internal audits.

Understanding Algorithmic Bias In Everyday Tools

Algorithmic bias, a concept often discussed in academic circles, has very real-world implications for SMBs. Imagine an AI-powered hiring tool that inadvertently favors male candidates over equally qualified female candidates, due to biases embedded in its training data. For an SMB striving for diversity and inclusion, such a bias can undermine their values and lead to skewed hiring practices. Similarly, marketing algorithms trained on historical data might perpetuate existing market biases, limiting outreach to certain demographics or geographic areas.

Recognizing that AI systems are not inherently neutral, but rather reflect the data they are trained on, is a crucial step towards transparency. SMBs don’t need to become experts in bias detection, but they should be aware of the potential for bias and ask vendors about the steps they take to mitigate it. Simple questions about data sources and bias mitigation Meaning ● Bias Mitigation, within the landscape of SMB growth strategies, automation adoption, and successful implementation initiatives, denotes the proactive identification and strategic reduction of prejudiced outcomes and unfair algorithmic decision-making inherent within business processes and automated systems. strategies can prompt vendors to address these critical issues.

Algorithmic bias isn’t an abstract concept; it’s a practical business risk that SMBs need to understand and mitigate to ensure fair and equitable outcomes in their operations.

Building Trust With Customers Through Transparency

In an era of increasing data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. concerns, customer trust is paramount. Transparency in AI Meaning ● Transparency in AI, within the SMB context, signifies making AI systems' decision-making processes understandable and explainable to stakeholders, including employees, customers, and regulatory bodies. systems can be a powerful tool for building and maintaining this trust. Consider an e-commerce SMB using AI-powered recommendation engines. Instead of simply presenting product recommendations as a black box, the SMB could provide customers with a clear explanation ● “These recommendations are based on your past purchase history and browsing behavior.” This simple statement of transparency acknowledges the use of AI while reassuring customers that their data is being used responsibly and in their interest.

Transparency can also extend to customer service chatbots. Clearly stating that a customer is interacting with a chatbot, rather than a human agent, manages expectations and avoids misleading customers. Openness about AI usage fosters a sense of honesty and builds stronger customer relationships.

The Role Of Employee Training In Ai Awareness

Transparency isn’t solely about external communication; it also begins internally, within the SMB itself. Employee training Meaning ● Employee Training in SMBs is a structured process to equip employees with necessary skills and knowledge for current and future roles, driving business growth. plays a vital role in fostering AI awareness and transparency. Equipping employees with a basic understanding of the AI systems they use daily empowers them to identify potential issues, ask informed questions, and contribute to a culture of transparency. Training doesn’t need to be technical; it can focus on the practical aspects of AI tools, their intended purpose, and the importance of data privacy and ethical considerations.

For example, sales teams using AI-powered CRM systems should understand how customer data is collected and used, and the importance of adhering to data privacy regulations. Customer service representatives interacting with AI chatbots should be trained to handle situations where the chatbot’s limitations become apparent and human intervention is required. Investing in employee training fosters a more informed and responsible workforce, capable of navigating the AI landscape with greater clarity and confidence.

Starting Small, Thinking Big About Ai Transparency

Ensuring transparency in AI systems for SMBs is not a one-time project; it’s an ongoing process of learning, adaptation, and continuous improvement. Start with the fundamentals ● inventory your AI tools, ask basic questions, focus on data inputs and outputs, and document your processes. Recognize the potential for algorithmic bias Meaning ● Algorithmic bias in SMBs: unfair outcomes from automated systems due to flawed data or design. and take steps to mitigate it. Build trust with customers through open communication about AI usage.

Invest in employee training to foster internal AI awareness. These initial steps lay the groundwork for a more transparent and responsible approach to AI adoption. As your SMB grows and your AI usage becomes more sophisticated, you can build upon these fundamentals, moving towards more advanced transparency measures. The key is to start small, think big, and embrace transparency as a core business value.

Intermediate

Seventy-two percent of consumers express concern about companies using AI in unethical ways, a sentiment that directly impacts SMBs relying on customer trust. This apprehension isn’t just about data breaches or privacy violations; it extends to the perceived ‘black box’ nature of AI decision-making. For SMBs operating in increasingly competitive markets, this consumer unease presents a tangible business challenge. Consider the local bakery using AI-powered inventory management to reduce waste; if customers perceive this system as opaque or manipulative, it can erode brand loyalty.

Transparency at this stage becomes less about basic awareness and more about demonstrating accountability 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. practices. How can SMBs move beyond fundamental understanding to implement intermediate strategies that build demonstrable transparency into their AI systems?

Vendor Due Diligence For Ai Transparency

Selecting AI vendors becomes a critical juncture for SMBs seeking transparency at an intermediate level. Moving beyond basic inquiries, due diligence now involves a deeper examination of vendor practices and commitments to transparency. This isn’t simply about comparing features and pricing; it’s about scrutinizing the vendor’s approach to data governance, algorithmic explainability, and bias mitigation. SMBs should actively seek vendors who can provide clear documentation on their AI models, data sources, and validation processes.

Requesting access to vendor transparency reports, if available, can offer valuable insights. Engage in detailed conversations about data security protocols and compliance with relevant regulations, such as GDPR or CCPA. Vendor contracts should explicitly address transparency expectations, outlining responsibilities for data access, auditability, and ongoing system monitoring. This proactive vendor due diligence ensures that transparency is baked into the AI infrastructure from the outset, rather than being an afterthought.

Intermediate AI transparency Meaning ● AI Transparency, within the realm of Small and Medium-sized Businesses, signifies the extent to which an AI system's decision-making processes are understandable and explainable to stakeholders, enabling scrutiny of algorithmic biases. for SMBs requires proactive vendor due diligence, scrutinizing their data governance, algorithmic explainability, and bias mitigation practices before system adoption.

Implementing Explainable Ai (Xai) Principles

Explainable AI (XAI) moves beyond simply understanding data inputs and outputs; it delves into the ‘why’ behind AI decisions. For SMBs at an intermediate stage, implementing XAI principles involves adopting tools and techniques that provide insights into the reasoning process of AI systems. This might involve using AI platforms that offer feature importance rankings, highlighting which data points are most influential in driving predictions or recommendations. For example, an SMB using AI for loan application processing could leverage XAI to understand why a particular application was flagged as high-risk, identifying specific factors like credit history or debt-to-income ratio.

XAI can also involve using simpler, more interpretable AI models, even if they sacrifice some predictive accuracy. Linear regression or decision trees, for instance, are inherently more transparent than complex neural networks. The goal is to strike a balance between AI performance and explainability, prioritizing transparency in areas where it is most critical for building trust and accountability.

Data Governance Frameworks For Ai Systems

Transparency is inextricably linked to data governance. For SMBs at an intermediate level, establishing robust data governance Meaning ● Data Governance for SMBs strategically manages data to achieve business goals, foster innovation, and gain a competitive edge. frameworks becomes essential for ensuring AI transparency. This involves defining clear policies and procedures for data collection, storage, processing, and usage within AI systems. Data lineage tracking becomes important, allowing SMBs to trace the origin and flow of data used in AI models.

Implementing data access controls ensures that sensitive data is only accessible to authorized personnel, enhancing data security and privacy. Regular data audits should be conducted to verify data quality and identify any anomalies or inconsistencies that could impact AI system performance or fairness. A well-defined data governance framework not only supports AI transparency but also strengthens overall data management practices, reducing risks and improving data-driven decision-making across the organization.

Transparency In Algorithmic Decision-Making Processes

Moving beyond the technical aspects, transparency at the intermediate level extends to the algorithmic decision-making processes themselves. This involves documenting and communicating how AI systems are integrated into business workflows and decision-making protocols. For example, if an SMB uses AI to automate customer service inquiries, the process should be clearly defined ● when is a chatbot used, when is a human agent involved, and what are the escalation procedures? Transparency also requires establishing clear lines of responsibility for AI system oversight and accountability.

Who is responsible for monitoring AI performance, addressing biases, and ensuring ethical AI usage? Defining roles and responsibilities fosters a culture of accountability and ensures that AI systems are not operating in a vacuum. Regularly reviewing and updating these decision-making processes is crucial to adapt to evolving AI capabilities and business needs.

Auditing Ai Systems For Bias And Fairness

At the intermediate level, simply acknowledging the potential for algorithmic bias is insufficient. SMBs need to implement proactive measures for auditing AI systems for bias and fairness. This involves using bias detection tools and techniques to analyze AI models and identify potential sources of unfairness. For example, an SMB using AI in recruitment could audit their hiring algorithm for gender or racial bias, using fairness metrics Meaning ● Fairness Metrics, within the SMB framework of expansion and automation, represent the quantifiable measures utilized to assess and mitigate biases inherent in automated systems, particularly algorithms used in decision-making processes. to assess whether the system disproportionately disadvantages certain demographic groups.

Auditing should not be a one-off exercise; it should be integrated into the AI system lifecycle, with regular audits conducted to monitor for bias drift over time. Remediation strategies should be in place to address identified biases, which might involve retraining AI models with more balanced data or adjusting algorithmic parameters to mitigate unfair outcomes. Auditing for bias and fairness demonstrates a commitment to ethical AI practices Meaning ● Ethical AI Practices, concerning SMB growth, relate to implementing AI systems fairly, transparently, and accountably, fostering trust among stakeholders and users. and builds trust with stakeholders.

Communicating Ai Transparency To Stakeholders

Transparency is not complete until it is effectively communicated to stakeholders, including customers, employees, and partners. At the intermediate level, SMBs should develop communication strategies that clearly articulate their approach to AI transparency. This might involve creating a dedicated transparency page on their website, outlining their AI principles, data governance practices, and efforts to ensure fairness and accountability. Providing customers with clear explanations about how AI is used in specific interactions, such as personalized recommendations or customer service chatbots, builds trust and manages expectations.

Internally, transparent communication with employees about AI systems fosters understanding and buy-in, encouraging responsible AI Meaning ● Responsible AI for SMBs means ethically building and using AI to foster trust, drive growth, and ensure long-term sustainability. usage. Regularly reporting on AI transparency initiatives, both internally and externally, demonstrates ongoing commitment and progress. Effective communication transforms transparency from an internal practice to a visible value proposition.

Building A Culture Of Responsible Ai Innovation

Intermediate AI transparency is not just about implementing specific tools and processes; it’s about fostering a culture of responsible AI innovation Meaning ● Responsible AI Innovation for SMBs means ethically developing and using AI to grow sustainably and benefit society. within the SMB. This involves embedding ethical considerations into the AI development lifecycle, from initial design to deployment and ongoing monitoring. Encouraging open discussions about the ethical implications of AI projects fosters a more thoughtful and responsible approach to innovation. Establishing internal ethical review boards or committees can provide a forum for evaluating AI projects from an ethical perspective, ensuring alignment with business values and societal norms.

Promoting ongoing learning and development in AI ethics Meaning ● AI Ethics for SMBs: Ensuring responsible, fair, and beneficial AI adoption for sustainable growth and trust. and transparency equips employees with the knowledge and skills to navigate the ethical complexities of AI. Building a culture of responsible AI innovation positions the SMB for long-term success in an AI-driven world, fostering trust, mitigating risks, and unlocking the full potential of AI in an ethical and transparent manner.

Moving Towards Proactive Ai Transparency

Reaching an intermediate level of AI transparency requires SMBs to move beyond reactive measures to proactive strategies. Vendor due diligence, XAI implementation, data governance frameworks, algorithmic decision-making transparency, bias auditing, stakeholder communication, and fostering a culture of responsible innovation are all proactive steps that build demonstrable transparency into AI systems. This proactive approach not only mitigates risks and builds trust but also unlocks new opportunities. Transparent AI systems can enhance customer engagement, improve employee morale, and strengthen brand reputation.

As SMBs progress on their AI transparency journey, they lay the foundation for more advanced strategies and a deeper integration of transparency into their core business operations. The intermediate stage is about solidifying transparency as a core operational principle, paving the way for advanced and transformative AI adoption.

Advanced

Eighty-six percent of enterprise-level businesses are actively pursuing AI ethics initiatives, signaling a shift from basic compliance to strategic differentiation through responsible AI. This isn’t merely a matter of ticking boxes; it’s about recognizing that in a hyper-competitive landscape, ethical and transparent AI practices can become a significant competitive advantage. For SMBs aspiring to scale and compete with larger players, adopting advanced transparency strategies becomes crucial.

Consider a fintech SMB leveraging AI for personalized financial advice; advanced transparency means not just explaining recommendations, but also proactively demonstrating adherence to ethical AI frameworks and industry best practices. How can SMBs transcend intermediate measures and implement advanced, strategically driven transparency in their AI systems to achieve sustainable growth Meaning ● Sustainable SMB growth is balanced expansion, mitigating risks, valuing stakeholders, and leveraging automation for long-term resilience and positive impact. and market leadership?

Strategic Alignment Of Ai Transparency With Business Goals

Advanced AI transparency transcends operational compliance; it becomes strategically aligned with core business objectives. This isn’t about viewing transparency as a cost center, but as a value driver that directly contributes to business success. SMBs at this level integrate transparency into their overall business strategy, recognizing its impact on brand reputation, customer loyalty, and investor confidence. For example, an SMB in the healthcare sector might strategically emphasize AI transparency to build patient trust and differentiate itself in a market increasingly sensitive to data privacy and ethical AI practices.

Transparency initiatives are not siloed projects; they are interwoven into marketing campaigns, product development roadmaps, and corporate social responsibility programs. This strategic alignment ensures that AI transparency is not just a buzzword, but a fundamental pillar of the SMB’s value proposition and long-term growth strategy.

Advanced AI transparency for SMBs is not a compliance exercise; it’s a strategic imperative, deeply integrated with business goals to drive competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. and long-term sustainability.

Implementing Formal Ai Ethics Frameworks

Moving beyond ad hoc ethical considerations, advanced AI transparency necessitates the implementation of formal AI ethics frameworks. These frameworks provide a structured approach to ethical AI development and deployment, ensuring consistent application of ethical principles across all AI initiatives. SMBs might adopt established frameworks like the OECD Principles on AI or develop their own customized frameworks tailored to their specific industry and business context. These frameworks typically encompass principles such as fairness, accountability, transparency, explainability, safety, and privacy.

Implementing a framework involves establishing clear guidelines, assigning responsibilities for ethical oversight, and developing processes for ethical impact assessments of AI projects. Formal AI ethics frameworks Meaning ● AI Ethics Frameworks are structured guidelines ensuring responsible AI use in SMBs, fostering trust and sustainable growth. provide a robust foundation for advanced transparency, demonstrating a commitment to ethical AI practices that goes beyond mere rhetoric.

Advanced Xai Techniques For Deep Algorithmic Insights

Explainable AI at the advanced level moves beyond basic feature importance rankings to more sophisticated techniques for gaining deep algorithmic insights. This involves employing methods like SHAP (SHapley Additive exPlanations) values or LIME (Local Interpretable Model-agnostic Explanations) to provide granular explanations of individual AI predictions. For example, an SMB using AI for fraud detection could leverage advanced XAI to understand not just which features are important in general, but specifically why a particular transaction was flagged as potentially fraudulent. Counterfactual explanations, another advanced XAI technique, can provide insights into what changes would be needed to alter an AI’s prediction.

For instance, a loan applicant denied by an AI system could receive a counterfactual explanation indicating what specific financial factors, if improved, would lead to loan approval. Advanced XAI techniques empower SMBs with a deeper understanding of their AI systems, enabling more targeted interventions, bias mitigation, and enhanced transparency for stakeholders.

Proactive Bias Mitigation And Fairness Engineering

Advanced AI transparency demands proactive bias mitigation and fairness engineering Meaning ● Fairness Engineering, in the SMB arena, is the discipline of building and deploying automated systems, specifically those utilizing AI, in a manner that mitigates bias and promotes equitable outcomes. throughout the AI lifecycle. This isn’t just about auditing for bias after model deployment; it’s about incorporating fairness considerations from the initial stages of data collection and model design. Fairness metrics are rigorously defined and monitored, with clear thresholds for acceptable levels of bias. Techniques like adversarial debiasing or fairness-aware machine learning algorithms are employed to actively reduce bias in AI models.

Regular fairness audits are conducted by independent third parties to ensure objectivity and accountability. Advanced bias mitigation extends beyond simply addressing statistical bias; it encompasses considerations of societal and contextual fairness, recognizing that fairness is not a one-size-fits-all concept. Proactive fairness engineering demonstrates a deep commitment to equitable AI outcomes and builds trust with diverse stakeholder groups.

Real-Time Ai System Monitoring And Auditability

Advanced transparency requires real-time monitoring and auditability of AI systems. This involves implementing robust monitoring dashboards that track key performance indicators, including accuracy, fairness metrics, and system stability. Automated alerts are triggered when anomalies or deviations from expected behavior are detected, enabling rapid response and intervention. Comprehensive audit logs are maintained, capturing all AI system activities, data inputs, and decision-making processes.

These audit logs provide a detailed trail for forensic analysis in case of errors, biases, or ethical concerns. Real-time monitoring and auditability not only enhance transparency but also improve system reliability and security. This level of vigilance demonstrates a commitment to ongoing oversight and accountability, essential for building trust in AI systems operating in critical business functions.

External Verification And Transparency Certifications

To further enhance credibility and demonstrate advanced transparency, SMBs can pursue external verification and transparency certifications. This involves engaging independent third-party organizations to audit their AI systems and transparency practices against recognized standards and frameworks. Obtaining certifications like ISO/IEC 42001, the international standard for AI management systems, or participating in transparency initiatives Meaning ● Transparency Initiatives, within the context of Small and Medium-sized Businesses, involve openly communicating business processes, financial data, and decision-making rationales to stakeholders. led by industry consortia or regulatory bodies, provides external validation of the SMB’s commitment to transparency.

These certifications and verifications serve as a powerful signal to customers, partners, and investors, demonstrating a level of transparency that goes beyond self-assessment. External verification builds trust and differentiates the SMB in a market where transparency is increasingly valued and expected.

Dynamic Transparency And Contextual Explainability

Advanced transparency recognizes that transparency is not a static concept; it needs to be dynamic and context-aware. This involves tailoring transparency mechanisms to the specific context of AI system usage and the needs of different stakeholders. Dynamic transparency means providing different levels of explanation depending on the user’s expertise and the sensitivity of the AI application. For example, a customer interacting with an AI-powered chatbot might receive a simpler explanation than an internal auditor reviewing the system’s performance.

Contextual explainability involves providing explanations that are relevant and understandable within the specific domain of application. Explanations in a healthcare AI system, for instance, would be tailored to medical professionals, while explanations in a retail AI system would be geared towards consumers. Dynamic transparency and contextual explainability ensure that transparency is not just a generic feature, but a tailored and effective communication tool.

Leading In Ai Ethics And Transparency Advocacy

At the advanced level, SMBs can transcend internal transparency practices and become leaders in AI ethics and transparency advocacy. This involves actively participating in industry discussions, contributing to the development of AI ethics standards, and sharing best practices with other organizations. SMBs can advocate for policies that promote responsible AI innovation and transparency at the industry and regulatory levels. Collaborating with research institutions and non-profit organizations on AI ethics initiatives further demonstrates commitment and thought leadership.

Publishing transparency reports and case studies showcasing the SMB’s AI transparency journey can inspire and guide other organizations. Leading in AI ethics and transparency advocacy not only strengthens the SMB’s brand reputation Meaning ● Brand reputation, for a Small or Medium-sized Business (SMB), represents the aggregate perception stakeholders hold regarding its reliability, quality, and values. but also contributes to a more responsible and ethical AI ecosystem, benefiting society as a whole.

Transformative Ai Transparency For Sustainable Growth

Advanced AI transparency is not just about mitigating risks or enhancing reputation; it’s about driving transformative change and fostering sustainable growth. By strategically aligning transparency with business goals, implementing formal ethics frameworks, employing advanced XAI techniques, proactively mitigating bias, ensuring real-time monitoring and auditability, seeking external verification, embracing dynamic transparency, and leading in advocacy, SMBs can unlock the full potential of AI in a responsible and ethical manner. This advanced approach to transparency builds deep trust with stakeholders, fosters a culture of responsible innovation, and creates a competitive advantage in an increasingly AI-driven world. Transformative AI transparency becomes a cornerstone of sustainable growth, enabling SMBs to thrive in the long term while contributing to a more ethical and equitable AI future.

References

• Oswald, Marion, and Frederik Zuiderveen Borgesius. “Transparency in Algorithmic Decision-Making ● Clarifying the Concepts.” Big Data & Society, vol. 6, no. 1, 2019, pp. 1-15.
• Goodman, Bryce, and Seth Flaxman. “EU General Data Protection Regulation ● Opportunities and Obligations.” Berkeley Technology Law Journal, vol. 32, no. 1, 2017, pp. 68-120.
• Holstein, Klaus, et al. “Improving Fairness in Machine Learning Systems ● What Do Industry Practitioners Need?” Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, pp. 1-16.
• Miller, Tim. “Explanation in Artificial Intelligence ● Insights from the Social Sciences.” Artificial Intelligence, vol. 267, 2019, pp. 1-38.
• The Organisation for Economic Co-operation and Development (OECD). “OECD Principles on AI.” OECD Digital Economy Papers, No. 97, OECD Publishing, 2019.