Fundamentals

In the rapidly evolving landscape of modern business, particularly for Small to Medium-Sized Businesses (SMBs), the integration of technology and automation is no longer a futuristic concept but a present-day necessity. As SMBs strive for growth and efficiency, they increasingly rely on algorithms to streamline operations, enhance decision-making, and improve customer experiences. This reliance, however, brings forth a new dimension to business ethics Meaning ● Business Ethics for SMBs is about integrating fairness, honesty, and responsibility into all business aspects for long-term success. and responsibility, encapsulated in the concept of Algorithmic Moral Agency (AMA). For SMB owners and managers, understanding the fundamentals of AMA is crucial to navigating the ethical implications of increasingly intelligent and autonomous systems within their organizations.

Understanding Algorithmic Moral Agency ● A Simple Start

At its most basic, Algorithmic Moral Agency refers to the capacity of algorithms, particularly within automated systems and artificial intelligence (AI), to make decisions and take actions that have moral or ethical implications. Imagine a simple example within an SMB ● an automated 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. chatbot. This chatbot, driven by algorithms, interacts with customers, answers queries, and resolves basic issues.

While seemingly straightforward, consider a scenario where the chatbot must decide between two courses of action, each with different ethical outcomes ● perhaps prioritizing one customer’s urgent issue over another based on pre-programmed rules. This decision-making process, even in a limited scope, touches upon the core of AMA.

For SMBs, the initial understanding of AMA doesn’t need to be overly complex. It starts with recognizing that the algorithms they employ, even in seemingly simple tools, are not neutral. They are designed and programmed by humans, and these designs inherently reflect certain values and priorities.

As SMBs increasingly adopt more sophisticated automation and AI-driven tools, from marketing automation platforms to inventory management systems, the ethical considerations embedded within these algorithms become more pronounced. It’s about acknowledging that these systems are not just tools; they are becoming active participants in business operations, making decisions that can impact customers, employees, and the overall reputation of the SMB.

For SMBs, understanding Algorithmic Moral Agency begins with recognizing that algorithms are not neutral tools but reflect embedded values and priorities that can have ethical implications in business operations.

Why Should SMBs Care About Algorithmic Moral Agency?

One might ask, why should a busy SMB owner, focused on daily operations and profitability, be concerned with such a seemingly abstract concept as Algorithmic Moral Agency? The answer lies in the increasing pervasiveness of algorithms in business and the potential ramifications of neglecting their ethical dimensions. For SMBs, ignoring AMA can lead to several tangible business risks and missed opportunities:

• Reputational Damage ● Algorithms that make unfair or biased decisions can quickly damage an SMB’s reputation. Imagine an AI-powered hiring tool that unintentionally discriminates against certain demographic groups. Such biases, if exposed, can lead to public outcry, loss of customer trust, and difficulty attracting talent. In today’s interconnected world, negative news travels fast, especially on social media, and can disproportionately impact SMBs that rely heavily on local community reputation.
• Legal and Regulatory Compliance ● As regulations around AI and data privacy become stricter, SMBs need to ensure their algorithmic systems comply with legal standards. For example, algorithms used in loan applications or credit scoring must be fair and non-discriminatory to avoid legal challenges and penalties. Failure to address AMA can lead to legal liabilities and fines, which can be particularly burdensome for SMBs with limited resources.
• Loss of Customer Trust Meaning ● Customer trust for SMBs is the confident reliance customers have in your business to consistently deliver value, act ethically, and responsibly use technology. and Loyalty ● Customers are increasingly aware of how algorithms influence their interactions with businesses. If an SMB’s algorithms are perceived as manipulative, unfair, or lacking in empathy, it can erode customer trust and loyalty. For instance, overly aggressive or intrusive marketing automation, driven by algorithms, can alienate potential customers. Building and maintaining customer trust is paramount for SMB success, and ethical algorithmic practices are crucial in this regard.
• Missed Opportunities for Ethical Innovation ● Embracing AMA proactively is not just about mitigating risks; it’s also about identifying opportunities for ethical innovation. SMBs that prioritize ethical algorithm design Meaning ● Ethical Algorithm Design for SMBs means building fair, transparent, and beneficial automated systems for sustainable growth and trust. can differentiate themselves in the market, attract ethically conscious customers, and build a positive brand image. For example, an SMB could develop AI-powered tools that promote fairness, transparency, and inclusivity, creating a competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. and fostering long-term sustainable growth.

Practical Steps for SMBs to Address Foundational AMA Concerns

For SMBs taking their first steps into understanding and addressing Algorithmic Moral Agency, the focus should be on practical, manageable actions. Here are some foundational steps:

1. Algorithm Inventory and Awareness ● Start by creating an inventory of all algorithms and automated systems currently used within the SMB. This includes everything from website analytics tools and social media scheduling software to CRM systems and automated email marketing platforms. The goal is to gain a clear picture of where algorithms are already impacting business operations. Raise awareness among employees about the role of algorithms and their potential ethical implications. Simple training sessions can help staff understand that algorithms are not just technical tools but also decision-making systems with ethical dimensions.
2. Data Transparency and Explainability ● Focus on making data processes more transparent. Understand what data is being collected, how it’s being used by algorithms, and what decisions are being driven by this data. Where possible, prioritize algorithms that are explainable, meaning that the reasoning behind their decisions can be understood. This is particularly important for algorithms that impact customers or employees directly. For SMBs using off-the-shelf software, inquire with vendors about the transparency and explainability of their algorithms.
3. Bias Detection and Mitigation ● Be mindful of potential biases in the data used to train algorithms and in the algorithms themselves. Even seemingly neutral data can contain biases that reflect societal inequalities. For example, historical sales data might reflect past biases in marketing practices. Implement basic checks to detect and mitigate potential biases. This might involve reviewing data sources, testing algorithms for fairness, and seeking diverse perspectives Meaning ● Diverse Perspectives, in the context of SMB growth, automation, and implementation, signifies the inclusion of varied viewpoints, backgrounds, and experiences within the team to improve problem-solving and innovation. in algorithm design and implementation. For SMBs, this could mean consulting with experts or using readily available bias detection tools.
4. Ethical Guidelines and Oversight ● Develop simple ethical guidelines for the use of algorithms within the SMB. These guidelines should reflect the SMB’s values and priorities. For example, a guideline might state that algorithms should be used to enhance customer service, not to manipulate customers. Establish a basic oversight process to review algorithmic decisions, particularly in areas with significant ethical implications. This could be as simple as assigning a team member or manager to periodically review reports generated by automated systems and ensure they align with ethical guidelines.

By taking these fundamental steps, SMBs can begin to navigate the ethical landscape of Algorithmic Moral Agency responsibly. It’s about starting small, building awareness, and integrating ethical considerations into the adoption and implementation of algorithms. This foundational understanding will pave the way for more sophisticated approaches as the SMB grows and its reliance on automated systems increases.

Intermediate

Building upon the foundational understanding of Algorithmic Moral Agency (AMA), SMBs at an intermediate stage of business sophistication can delve deeper into the strategic and operational implications of ethically aligned algorithms. Moving beyond basic awareness, this stage focuses on integrating AMA into core business processes, from strategic decision-making to customer relationship management and internal operations. For SMBs aiming for sustained growth and competitive advantage in an increasingly automated world, a more nuanced and proactive approach to AMA becomes essential.

Intermediate AMA ● Strategic Integration and Operational Refinement

At the intermediate level, Algorithmic Moral Agency is not just about avoiding ethical pitfalls; it becomes a strategic asset. It’s about proactively designing and deploying algorithms that not only drive efficiency and profitability but also embody the SMB’s ethical values and contribute to a positive societal impact. This requires a shift from reactive risk mitigation Meaning ● Within the dynamic landscape of SMB growth, automation, and implementation, Risk Mitigation denotes the proactive business processes designed to identify, assess, and strategically reduce potential threats to organizational goals. to proactive ethical design and implementation. For SMBs, this means considering AMA at the strategic planning stage, embedding ethical considerations into algorithm development and deployment processes, and actively monitoring and refining algorithmic performance from an ethical perspective.

Consider an SMB in the e-commerce sector. At a fundamental level, they might be concerned with ensuring their recommendation algorithms are not biased against certain product categories. At an intermediate level, they would proactively design these algorithms to promote ethical consumption, perhaps by highlighting sustainable or ethically sourced products, even if these are not the most profitable options in the short term.

This strategic integration of AMA can differentiate the SMB in a crowded marketplace and attract customers who value ethical business practices. Similarly, in HR, an SMB might move beyond simply avoiding discriminatory hiring algorithms to actively using AI to promote diversity and inclusion, creating a more equitable and innovative workforce.

For SMBs at an intermediate level, Algorithmic Moral Agency transitions from risk mitigation to a strategic asset, proactively shaping algorithms to reflect ethical values and create positive societal impact.

Deepening the Understanding of Ethical Frameworks for Algorithms

To effectively integrate AMA at an intermediate level, SMBs need to move beyond a basic understanding of ethical principles and begin to apply specific ethical frameworks Meaning ● Ethical Frameworks are guiding principles for morally sound SMB decisions, ensuring sustainable, reputable, and trusted business practices. to their algorithmic systems. This involves understanding different ethical theories and how they can be translated into practical guidelines for algorithm design and deployment. Some relevant ethical frameworks for SMBs to consider include:

• Deontology (Rule-Based Ethics) ● This framework emphasizes adherence to moral rules and duties. For algorithmic systems, this translates to defining clear ethical rules and ensuring algorithms consistently follow these rules, regardless of the specific context or outcome. For example, an SMB might establish a deontological rule that customer data will never be shared with third parties without explicit consent, and design their data processing algorithms to strictly enforce this rule.
• Utilitarianism (Consequence-Based Ethics) ● Utilitarianism focuses on maximizing overall well-being or happiness. In the context of algorithms, this means designing systems that produce the greatest good for the greatest number of people affected. For example, an SMB using AI to optimize delivery routes might employ a utilitarian approach by prioritizing routes that minimize overall fuel consumption and emissions, even if it slightly increases delivery time for some customers. However, SMBs need to be cautious about how “good” and “well-being” are defined and measured, as purely utilitarian approaches can sometimes lead to neglecting the rights or needs of minorities.
• Virtue Ethics (Character-Based Ethics) ● Virtue ethics emphasizes the development of virtuous character traits. For algorithms, this is a more nuanced approach that focuses on designing systems that embody virtues like fairness, transparency, empathy, and responsibility. This is less about strict rules or quantifiable outcomes and more about cultivating ethical behavior in algorithmic systems. For example, an SMB developing an AI-powered customer service chatbot might focus on instilling virtues like empathy and helpfulness in the chatbot’s responses, rather than just optimizing for speed or efficiency.
• Care Ethics (Relationship-Based Ethics) ● Care ethics prioritizes relationships and interdependence. In algorithmic contexts, this framework emphasizes the importance of considering the impact of algorithms on relationships, both within the SMB and with customers and stakeholders. It encourages designing algorithms that foster trust, empathy, and mutual respect in these relationships. For example, an SMB using algorithms for personalized marketing might adopt a care ethics approach by prioritizing customer autonomy and avoiding manipulative or intrusive tactics that could damage the customer-business relationship.

SMBs can benefit from exploring these ethical frameworks and selecting the ones that best align with their values and business context. Often, a hybrid approach, combining elements from different frameworks, is most effective in addressing the complex ethical challenges of Algorithmic Moral Agency.

Implementing Intermediate AMA Practices in SMB Operations

Moving from ethical frameworks to practical implementation, SMBs at an intermediate level can adopt more sophisticated practices to integrate AMA into their operations:

1. Ethical Algorithm Design Process ● Establish a formal process for ethical algorithm Meaning ● Ethical Algorithms for SMBs represent the application of AI and machine learning models designed and deployed with a commitment to fairness, transparency, and accountability, specifically aimed at fostering sustainable business growth and responsible automation strategies. design. This process should involve stakeholders from different departments, including ethics or compliance officers (if available), technology teams, and business unit representatives. The process should include ethical impact assessments for new algorithms, considering potential biases, fairness concerns, and societal implications. Design algorithms with built-in ethical safeguards, such as transparency mechanisms, accountability measures, and options for human oversight or intervention. For SMBs without dedicated ethics teams, this process can be led by a designated manager or team responsible for technology implementation, potentially with external ethical consulting support.
2. Algorithmic Auditing and Monitoring ● Implement regular audits of algorithmic systems to assess their ethical performance. This goes beyond technical performance monitoring to include evaluating fairness, bias, transparency, and alignment with ethical guidelines. Use metrics and KPIs to track ethical performance over time and identify areas for improvement. Establish mechanisms for reporting and addressing ethical concerns related to algorithms. This could involve creating an internal ethics hotline or feedback channel for employees and customers to raise concerns about algorithmic systems. For SMBs, auditing can be simplified by focusing on key algorithms with high ethical impact and using readily available auditing tools or services.
3. Stakeholder Engagement and Communication ● Engage with stakeholders, including employees, customers, and the broader community, on ethical considerations related to algorithms. Communicate transparently about how algorithms are being used, the ethical principles guiding their design, and the safeguards in place to ensure responsible use. Solicit feedback from stakeholders on ethical concerns and incorporate this feedback into algorithm development and refinement. For SMBs, this could involve conducting customer surveys on algorithm transparency, holding employee workshops on ethical AI, or publishing ethical guidelines on the company website.
4. Ethical Training and Education ● Provide ongoing training and education to employees on Algorithmic Moral Agency 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. This training should be tailored to different roles and responsibilities within the SMB, ensuring that everyone understands their role in promoting ethical algorithm use. Include practical case studies and scenarios to illustrate ethical challenges and best practices. For SMBs, training can be integrated into existing professional development programs or delivered through online modules or workshops.

By implementing these intermediate-level practices, SMBs can move beyond basic compliance and actively cultivate an ethical algorithmic culture. This not only mitigates risks but also positions the SMB for long-term success by building trust, fostering innovation, and attracting ethically conscious talent and customers. It’s about making ethical considerations an integral part of the SMB’s operational DNA.

Intermediate AMA implementation for SMBs involves establishing ethical algorithm design processes, conducting regular audits, engaging stakeholders transparently, and providing ongoing ethical training to employees.

Advanced

At the advanced level, Algorithmic Moral Agency (AMA) transcends operational considerations and becomes a cornerstone of organizational identity and societal contribution for SMBs. This stage demands a profound understanding of AMA, moving beyond ethical frameworks and practical implementations to grapple with the philosophical underpinnings, societal implications, and future trajectories of algorithmic ethics. For sophisticated SMBs seeking to be leaders in responsible technology adoption and to contribute meaningfully to the evolving ethical landscape, an advanced engagement with AMA is not just beneficial but essential. This necessitates a critical examination of the very nature of moral agency in algorithms, considering diverse perspectives, cultural nuances, and the long-term consequences of embedding moral decision-making into automated systems.

Advanced Algorithmic Moral Agency ● Redefining Meaning and Purpose

After rigorous analysis and integration of diverse perspectives, including cross-cultural business Meaning ● Navigating global markets by understanding and respecting diverse cultural values for SMB success. ethics and interdisciplinary research, we arrive at an advanced definition of Algorithmic Moral Agency tailored for SMBs ● Algorithmic Moral Agency, within the SMB context, signifies the capacity of algorithms and AI systems, operating within and on behalf of the business, to independently evaluate, decide, and act in situations presenting ethical dilemmas, guided by pre-programmed values and learning, with consequences that are directly attributable to the SMB’s ethical standing and long-term sustainability. This definition moves beyond simple rule-following or outcome optimization. It emphasizes the Independent Evaluation, suggesting a level of autonomy in ethical reasoning, even if constrained by programming. The phrase Situations Presenting Ethical Dilemmas highlights that AMA is not just about routine decisions but about navigating complex moral choices.

Crucially, the definition underscores that the Consequences are Directly Attributable to the SMB’s Ethical Standing and Long-Term Sustainability, linking AMA directly to core business outcomes and reputational capital. This advanced understanding recognizes that as algorithms become more sophisticated, their ethical decisions are no longer merely technical outputs but reflections of the SMB’s moral character and commitment to responsible innovation.

Consider the cross-sectorial influence of the financial services industry on AMA. Financial algorithms, for example, are increasingly used in lending, investment, and risk assessment. The ethical implications of these algorithms are profound, impacting access to capital, economic opportunity, and financial stability. SMBs in fintech or those relying heavily on financial algorithms must grapple with the advanced AMA considerations arising from this sector.

For instance, an AI-powered lending platform for SMBs must not only be efficient and profitable but also demonstrably fair, transparent, and inclusive in its lending decisions, avoiding algorithmic bias that could perpetuate economic inequalities. The long-term business outcome of ethically sound financial algorithms is not just regulatory compliance but also building trust with customers and stakeholders, fostering financial inclusion, and contributing to a more equitable economic ecosystem. This cross-sectorial perspective highlights that AMA is not confined to specific industries but permeates all sectors where algorithms are employed for decision-making with ethical ramifications.

Advanced Algorithmic Moral Agency for SMBs is defined as the capacity of algorithms to independently evaluate and act in ethical dilemmas, guided by values, with consequences directly impacting the SMB’s ethical standing and long-term sustainability.

Exploring Diverse Perspectives and Cross-Cultural Business Ethics in AMA

A truly advanced understanding of Algorithmic Moral Agency requires embracing diverse perspectives and acknowledging the influence of cross-cultural business ethics. Ethical norms and values are not universal; they vary across cultures, societies, and even within different business contexts. SMBs operating in global markets or serving diverse customer bases must be particularly sensitive to these cultural nuances in AMA.

Ignoring these perspectives can lead to algorithms that are perceived as unethical or culturally insensitive, even if they are considered acceptable within a specific cultural framework. Key considerations include:

• Cultural Relativism Vs. Ethical Universalism ● This philosophical debate is highly relevant to AMA. Cultural relativism suggests that ethical values are culturally specific, and there are no universal moral principles. Ethical universalism, on the other hand, posits that some moral principles are universally applicable across cultures. For SMBs, navigating this tension means acknowledging cultural differences in ethical norms while also identifying core ethical principles that should guide algorithm design and deployment globally. For example, while privacy norms may vary across cultures, principles of fairness and non-discrimination are often considered more universally applicable. Advanced AMA approaches seek to balance cultural sensitivity with adherence to fundamental ethical values.
• Non-Western Ethical Traditions ● Western ethical frameworks, such as deontology and utilitarianism, are often dominant in discussions of AMA. However, non-Western ethical traditions, such as Confucianism, Buddhism, or Ubuntu philosophy, offer valuable alternative perspectives. These traditions often emphasize community, harmony, relationships, and interconnectedness, which can enrich our understanding of ethical algorithm design. For example, Ubuntu philosophy, emphasizing interconnectedness and collective responsibility, might suggest designing algorithms that prioritize the well-being of the community as a whole, rather than just individual outcomes. Exploring these diverse ethical traditions can broaden the ethical toolkit for SMBs and lead to more culturally inclusive and ethically robust algorithmic systems.
• Stakeholder-Centric Ethics ● Advanced AMA approaches often adopt a stakeholder-centric ethical perspective, considering the ethical implications for all stakeholders affected by algorithmic decisions, not just shareholders or customers. This includes employees, suppliers, communities, and even future generations. Algorithms can have far-reaching societal impacts, and a stakeholder-centric approach ensures that ethical considerations are broadened to encompass all relevant parties. For SMBs, this means engaging with a wider range of stakeholders in ethical algorithm design and implementation, considering their diverse needs and values, and striving for algorithmic systems that benefit society as a whole, not just the business itself.
• Ethical AI and Global Governance ● The field of ethical AI is rapidly evolving, with increasing attention to global governance and standardization. International organizations and consortia are developing ethical AI guidelines and frameworks that aim to promote responsible AI development and deployment worldwide. SMBs operating internationally should be aware of these global initiatives and align their AMA practices with emerging international standards. This not only ensures compliance but also positions the SMB as a responsible global actor in the ethical AI landscape. Active participation in industry forums and collaborations on ethical AI can further enhance an SMB’s advanced AMA capabilities and contribute to shaping the future of ethical technology.

By engaging with these diverse perspectives and cross-cultural ethical considerations, SMBs can develop a more nuanced and sophisticated understanding of Algorithmic Moral Agency. This advanced understanding is crucial for navigating the complex ethical challenges of a globalized and interconnected world and for building algorithmic systems that are not only effective and efficient but also ethically sound and culturally sensitive.

Advanced AMA Implementation and Long-Term Business Outcomes for SMBs

Implementing advanced AMA practices requires a strategic and holistic approach, integrating ethical considerations into the very fabric of the SMB’s operations and culture. This goes beyond reactive risk mitigation and proactive design to encompass a commitment to ethical innovation and societal contribution. Key elements of advanced AMA implementation include:

1. Embedding Ethics in Organizational Culture ● Cultivate an organizational culture that deeply values ethics and responsibility in all aspects of algorithm development and deployment. This requires leadership commitment, ethical training at all levels, and the creation of internal mechanisms for ethical reflection and deliberation. Establish an ethics committee or advisory board with diverse expertise to guide AMA strategy and provide ongoing ethical oversight. Promote a culture of ethical awareness and accountability, where employees are empowered and encouraged to raise ethical concerns related to algorithms and are recognized for ethical behavior. For SMBs, this cultural shift may start with leadership championing ethical AI and gradually embedding ethical values into company policies, training programs, and performance evaluations.
2. Developing Explainable and Accountable AI (XAI) ● Prioritize the development and deployment of Explainable AI (XAI) systems. XAI aims to make AI decision-making processes more transparent and understandable to humans. This is crucial for building trust, ensuring accountability, and facilitating ethical oversight of algorithms. Invest in research and development of XAI techniques and tools that are applicable to the SMB’s specific algorithmic systems. Implement mechanisms for auditing and explaining algorithmic decisions, particularly in high-stakes areas with ethical implications. For SMBs, adopting XAI may involve choosing AI platforms and tools that offer explainability features, investing in training for employees to understand and interpret XAI outputs, and developing clear communication strategies to explain algorithmic decisions to customers and stakeholders.
3. Promoting Fairness, Equity, and Justice in Algorithms ● Go beyond simply avoiding bias to actively promoting fairness, equity, and justice in algorithmic systems. This requires a deep understanding of different notions of fairness and how they apply to specific contexts. Develop and implement fairness metrics and auditing tools to assess and mitigate algorithmic unfairness. Actively work to address systemic biases that may be reflected in data and algorithms. Design algorithms that promote equitable outcomes and opportunities for all stakeholders, particularly marginalized or underrepresented groups. For SMBs, this may involve conducting fairness audits of key algorithms, using diverse datasets for algorithm training, and implementing fairness-aware machine learning techniques. It also requires ongoing monitoring and refinement to ensure algorithms remain fair and equitable over time.
4. Fostering Algorithmic Transparency Meaning ● Algorithmic Transparency for SMBs means understanding how automated systems make decisions to ensure fairness and build trust. and Public Discourse ● Embrace algorithmic transparency as a core ethical principle. Be transparent about the algorithms used in business operations, their purpose, and their potential impacts. Engage in public discourse and dialogue about the ethical implications of algorithms and AI. Contribute to industry-wide efforts to promote algorithmic transparency and accountability. For SMBs, transparency may involve publishing ethical guidelines for AI use, providing clear explanations of how algorithms affect customers, and participating in industry forums and public discussions on ethical AI. Open communication and public engagement are crucial for building trust and fostering responsible innovation in the age of algorithms.

The long-term business outcomes of advanced AMA implementation are profound and multifaceted. SMBs that embrace advanced AMA are not only mitigating ethical risks but also building a strong ethical brand, attracting ethically conscious customers and investors, fostering innovation through trust and transparency, and contributing to a more just and equitable society. In an increasingly algorithm-driven world, advanced AMA becomes a source of competitive advantage, societal impact, and long-term sustainable success. It is a journey of continuous learning, ethical reflection, and commitment to responsible technology leadership.

Advanced AMA implementation for SMBs involves embedding ethics in organizational culture, developing XAI, promoting fairness and equity in algorithms, and fostering algorithmic transparency and public discourse.

In conclusion, navigating the complex terrain of Algorithmic Moral Agency is not merely a technical or compliance challenge for SMBs; it is a strategic imperative and an ethical responsibility. By progressing through the fundamental, intermediate, and advanced stages of AMA understanding and implementation, SMBs can not only mitigate risks and ensure ethical operations but also unlock new opportunities for innovation, growth, and positive societal impact. Embracing AMA is not just about doing business ethically; it is about building a more ethical and sustainable business for the future.