This article explores the importance of responsible and ethical use of artificial intelligence (AI) when deploying emerging technologies in business operations. It also details the best practices and frameworks for AI ethics.
AI ethics frameworks guide responsible, fair and transparent development and use of AI systems to respect human rights. The three major AI frameworks share common principles but have different focus and emphasis. Applying these frameworks is essential to mitigate the risks of bias and harm, create trust, and align AI with societal values - but challenges remain.
What is AI ethics? It is a set of principles that guide the development and use of AI responsibility — producing a safe, secure and ethical framework. By deploying a strong code of AI ethics, developers and organisations can build safeguards to avoid bias, ensure privacy, and mitigate risks of harm.
AI has broad societal impacts. An ethical AI framework can help guide AI development to use data responsibly and ensure privacy, fairness, transparency, accountability and inclusion without hindering innovation.
Various ethical AI frameworks are helping to guide developers, legislators and regulators. We’ll compare and contrast three AI frameworks:
AI poses significant implications for society and has the potential to create immense positive effects and negative impacts. Ethical principles must guide the development, procurement and implementation of AI tools.
These principles apply to a broad cross-section of stakeholders, including:
AI ethics must be universally adopted to be effective, creating trust for all users.
Individuals and companies can deploy AI for good or malicious purposes. AI can streamline workflows, generate content and code and automate tedious manual processes. However, it can also help spread misinformation, introduce bias into decision-making and discriminate.
Ethical AI seeks to respect human rights and protect users from these issues. AI ethics frameworks are guiding principles designed to ensure AI systems adhere to ethical standards such as fairness, transparency and accountability.
An AI ethics framework helps developers and decision-makers navigate the complex challenges and implications of AI. With AI being injected into products and adopted by businesses at an increasing rate, ensuring these tools act ethically is becoming more critical than ever.
The Institute of Electrical and Electronics Engineers (IEEE) introduced the first iteration of its Ethically Aligned Design principles in 2016, incorporating input from more than 100 global AI and ethics experts. It was also updated in 2019 in Version 2.
According to IEEE, the framework is designed to “advance a public discussion about how we can establish ethical and social implementations for intelligent and autonomous systems and technologies, aligning them to defined values and ethical principles that prioritise human well-being in a given cultural context.”
The key principles of the IEEE AI Ethics Framework are:
The framework's primary recommendation is to establish interdisciplinary ethics review boards to assess potential risks and harms at each stage of AI tools, from design to deployment. These boards should include stakeholders from diverse backgrounds like ethicists, domain experts, end-users and impacted community members.
The framework also emphasises techniques like Ethics by Design — proactively embedding ethical principles into system requirements from initial conception. It outlines practical methods such as ethical risk modelling, algorithmic audits and adversarial testing to help validate ethical assumptions and identify unintended consequences.
One unique aspect of the IEEE framework is its emphasis on embedding ethical considerations into the engineering process of AI systems. By integrating AI ethics principles in the earliest stages of system design, this framework creates a foundation for ethical behaviour — rather than trying to embed it after deployment.
The EU has taken a similar approach to AI regulation with a focus on fundamental rights and ethical standards. Published in 2019, the EU's Ethics Guidelines for Trustworthy AI highlights three components companies should incorporate at each stage of AI design:
The guidelines state: “Each component in itself is necessary but not sufficient for the achievement of Trustworthy AI. Ideally, all three components work in harmony and overlap in their operation.”
The document also focuses on the importance of privacy and data governance as foundational elements for trustworthy AI. Three key issues are:
A core premise of these guidelines is the need for human oversight and control measures for AI systems. Guidance on human agency principles like the ability to override decisions, interventions for course corrections and opt-out rights are also available. The same applies to human-AI interaction best practices, such as clear communications about system capabilities and ensuring meaningful human review of outputs.
The guidelines call for extending these ethical requirements beyond just the AI developers to all actors involved in the system’s lifecycle, including integrators, operators, end-users and those impacted. Recommendations span governance structures, due diligence assessments, operational procedures and more.
One unique aspect of this AI framework is an emphasis on regulatory compliance and the need to establish clear legal frameworks. The guidelines should help shape future AI-related legislation within the EU and embed ethical principles in the legal structures that govern AI development and use.
The Organisation for Economic Co-operation and Development (OECD) is an international organisation that promotes prosperity, equity, opportunity and well-being. The OECD adopted the AI Principles in 2019 and recently updated them in May 2024.
The framework aims to “guide AI actors in their efforts to develop trustworthy AI and provide policymakers with recommendations for effective AI policies.”
The OECD Principles on Artificial Intelligence focus on fostering innovation and trust in AI systems through principles like inclusiveness, sustainability and accountability.
The guidelines recommend establishing internal governance frameworks, risk management measures, external oversight and audit processes. This includes assigned roles, reporting protocols and grievance/redress mechanisms.
There is also an emphasis on assessing and prioritising AI applications based on their potential benefits versus risks and negative impacts. Companies should weigh factors like the application’s scale, use case and data sensitivity.
The OECD provides tools to help policymakers identify and manage AI systems.
OECD emphasises international cooperation and policy coherence and encourages collaboration from member countries to create a consistent approach to AI governance. The document sets forth a logical construct for a global approach to align policies.
So far, 47 countries have committed to the ethics in AI Principles.
AI applications are growing at a staggering rate. Grand View Research forecasts global AI adoption to expand by a compound annual growth rate of more than 37% between now and 2030. The marketplace is already valued at more than US $196.6 billion.
With such high-stakes competition and rapid development, there are growing concerns about AI and ethics.
The hallucinations of generative AI are well-documented and have spawned lawsuits regarding false statements and damage to individuals. Other lawsuits have focused on copyright violations, alleging unethical or illegal use of copyright materials and organisation’s lack of transparency over training models.
Companies and countries are also taking different approaches to ethics in AI. In the race to develop AI products, for example, Japan suspended the enforcement of copyright laws for material used to train generative AI models, eliminating the requirement to get permission first.
There are also concerns about bias — whether intentional or unintentional. Amazon famously shut down its AI applicant screening process that reviewed resumes when it showed a significant bias against female job seekers. The models were designed to evaluate candidates, in part, by finding patterns in resumes submitted over a decade. Because the overwhelming majority of resumes were from men, the machine learning algorithms taught itself to penalise resumes for female applicants.
There are significant challenges ahead. While the frameworks provide standards and value for the development and use of ethics in AI, they do not fully address all the implications and potential risks. As AI capabilities continue to advance, ethical frameworks must also evolve to account for emerging use cases.
As AI becomes more integrated into products, the risk of autonomous decisions powered by AI increases. Strict guidelines must be in place to prevent AI algorithms from making decisions without human oversight or guidance. There are also some difficult questions to answer.
Will we reach artificial general intelligence (AGI)? If AI can display true human-like intelligence with the ability to teach itself, this could result in autonomous self-control.
A strong ethical foundation must exist to prevent systems from making harmful decisions. There is also the question of existential rights, spurring philosophical questions about whether an AI system itself has rights.
AI technology is still in its infancy. The cited guidelines also do not directly address enforcement or compliance. Adoption of these frameworks or other ethical AI principles is voluntary.
Yet, developers and companies are under increasing competitive pressure to develop AI tools and there are financial incentives for doing so. PwC estimates that AI can increase revenues by $15 trillion by 2030, boosting the GDP of local economies by an additional 26%. These are powerful motivators to cut corners when it comes to ethics.
Large language models and Generative AI have also put powerful tools in the hands of everyone, including those with malicious intent. While these frameworks emphasise ethical use, transparency and accountability, they do not provide guardrails or enforcement powers to avoid misuse. There will need to be deep analysis and legislation that protects human rights, decides questions about fair use of copyright and intellectual property, and prohibits bias.
Ultimately, it will be up to regulators and legislators to define ethics in AI and establish laws to protect us, including enforcement mechanisms for violations. Even so, the patchwork of laws and regulations may still be ineffective in today’s cross-border economy.
While the OECD Framework, in particular, advocates for international cooperation, a global AI ethics policy is unlikely. Gaining universal alignment to consistent AI governance and ethical practices may not be possible considering the sheer number of nations that would have to agree to conflicting agendas.
By understanding and applying the AI ethics frameworks through the AI product lifecycle, developers, businesses, and legislators can contribute to the responsible development and deployment of AI systems. Without guiding principles about ethics in AI, each entity is left to make its own decision about what constitutes ethical behaviour.
Adopting a common framework for development, implementation, and use is crucial to creating trust in systems, addressing societal concerns, and maintaining human rights. AI must align with our shared values and principles to help create a better world. Everyone in the AI community should uphold the highest level of AI ethics.
Zendata integrates privacy by design across the entire data lifecycle, emphasising the context and risks associated with data usage. Our platform provides insights into data usage, third-party risks, and alignment with data protection regulations and policies.
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This article explores the importance of responsible and ethical use of artificial intelligence (AI) when deploying emerging technologies in business operations. It also details the best practices and frameworks for AI ethics.
AI ethics frameworks guide responsible, fair and transparent development and use of AI systems to respect human rights. The three major AI frameworks share common principles but have different focus and emphasis. Applying these frameworks is essential to mitigate the risks of bias and harm, create trust, and align AI with societal values - but challenges remain.
What is AI ethics? It is a set of principles that guide the development and use of AI responsibility — producing a safe, secure and ethical framework. By deploying a strong code of AI ethics, developers and organisations can build safeguards to avoid bias, ensure privacy, and mitigate risks of harm.
AI has broad societal impacts. An ethical AI framework can help guide AI development to use data responsibly and ensure privacy, fairness, transparency, accountability and inclusion without hindering innovation.
Various ethical AI frameworks are helping to guide developers, legislators and regulators. We’ll compare and contrast three AI frameworks:
AI poses significant implications for society and has the potential to create immense positive effects and negative impacts. Ethical principles must guide the development, procurement and implementation of AI tools.
These principles apply to a broad cross-section of stakeholders, including:
AI ethics must be universally adopted to be effective, creating trust for all users.
Individuals and companies can deploy AI for good or malicious purposes. AI can streamline workflows, generate content and code and automate tedious manual processes. However, it can also help spread misinformation, introduce bias into decision-making and discriminate.
Ethical AI seeks to respect human rights and protect users from these issues. AI ethics frameworks are guiding principles designed to ensure AI systems adhere to ethical standards such as fairness, transparency and accountability.
An AI ethics framework helps developers and decision-makers navigate the complex challenges and implications of AI. With AI being injected into products and adopted by businesses at an increasing rate, ensuring these tools act ethically is becoming more critical than ever.
The Institute of Electrical and Electronics Engineers (IEEE) introduced the first iteration of its Ethically Aligned Design principles in 2016, incorporating input from more than 100 global AI and ethics experts. It was also updated in 2019 in Version 2.
According to IEEE, the framework is designed to “advance a public discussion about how we can establish ethical and social implementations for intelligent and autonomous systems and technologies, aligning them to defined values and ethical principles that prioritise human well-being in a given cultural context.”
The key principles of the IEEE AI Ethics Framework are:
The framework's primary recommendation is to establish interdisciplinary ethics review boards to assess potential risks and harms at each stage of AI tools, from design to deployment. These boards should include stakeholders from diverse backgrounds like ethicists, domain experts, end-users and impacted community members.
The framework also emphasises techniques like Ethics by Design — proactively embedding ethical principles into system requirements from initial conception. It outlines practical methods such as ethical risk modelling, algorithmic audits and adversarial testing to help validate ethical assumptions and identify unintended consequences.
One unique aspect of the IEEE framework is its emphasis on embedding ethical considerations into the engineering process of AI systems. By integrating AI ethics principles in the earliest stages of system design, this framework creates a foundation for ethical behaviour — rather than trying to embed it after deployment.
The EU has taken a similar approach to AI regulation with a focus on fundamental rights and ethical standards. Published in 2019, the EU's Ethics Guidelines for Trustworthy AI highlights three components companies should incorporate at each stage of AI design:
The guidelines state: “Each component in itself is necessary but not sufficient for the achievement of Trustworthy AI. Ideally, all three components work in harmony and overlap in their operation.”
The document also focuses on the importance of privacy and data governance as foundational elements for trustworthy AI. Three key issues are:
A core premise of these guidelines is the need for human oversight and control measures for AI systems. Guidance on human agency principles like the ability to override decisions, interventions for course corrections and opt-out rights are also available. The same applies to human-AI interaction best practices, such as clear communications about system capabilities and ensuring meaningful human review of outputs.
The guidelines call for extending these ethical requirements beyond just the AI developers to all actors involved in the system’s lifecycle, including integrators, operators, end-users and those impacted. Recommendations span governance structures, due diligence assessments, operational procedures and more.
One unique aspect of this AI framework is an emphasis on regulatory compliance and the need to establish clear legal frameworks. The guidelines should help shape future AI-related legislation within the EU and embed ethical principles in the legal structures that govern AI development and use.
The Organisation for Economic Co-operation and Development (OECD) is an international organisation that promotes prosperity, equity, opportunity and well-being. The OECD adopted the AI Principles in 2019 and recently updated them in May 2024.
The framework aims to “guide AI actors in their efforts to develop trustworthy AI and provide policymakers with recommendations for effective AI policies.”
The OECD Principles on Artificial Intelligence focus on fostering innovation and trust in AI systems through principles like inclusiveness, sustainability and accountability.
The guidelines recommend establishing internal governance frameworks, risk management measures, external oversight and audit processes. This includes assigned roles, reporting protocols and grievance/redress mechanisms.
There is also an emphasis on assessing and prioritising AI applications based on their potential benefits versus risks and negative impacts. Companies should weigh factors like the application’s scale, use case and data sensitivity.
The OECD provides tools to help policymakers identify and manage AI systems.
OECD emphasises international cooperation and policy coherence and encourages collaboration from member countries to create a consistent approach to AI governance. The document sets forth a logical construct for a global approach to align policies.
So far, 47 countries have committed to the ethics in AI Principles.
AI applications are growing at a staggering rate. Grand View Research forecasts global AI adoption to expand by a compound annual growth rate of more than 37% between now and 2030. The marketplace is already valued at more than US $196.6 billion.
With such high-stakes competition and rapid development, there are growing concerns about AI and ethics.
The hallucinations of generative AI are well-documented and have spawned lawsuits regarding false statements and damage to individuals. Other lawsuits have focused on copyright violations, alleging unethical or illegal use of copyright materials and organisation’s lack of transparency over training models.
Companies and countries are also taking different approaches to ethics in AI. In the race to develop AI products, for example, Japan suspended the enforcement of copyright laws for material used to train generative AI models, eliminating the requirement to get permission first.
There are also concerns about bias — whether intentional or unintentional. Amazon famously shut down its AI applicant screening process that reviewed resumes when it showed a significant bias against female job seekers. The models were designed to evaluate candidates, in part, by finding patterns in resumes submitted over a decade. Because the overwhelming majority of resumes were from men, the machine learning algorithms taught itself to penalise resumes for female applicants.
There are significant challenges ahead. While the frameworks provide standards and value for the development and use of ethics in AI, they do not fully address all the implications and potential risks. As AI capabilities continue to advance, ethical frameworks must also evolve to account for emerging use cases.
As AI becomes more integrated into products, the risk of autonomous decisions powered by AI increases. Strict guidelines must be in place to prevent AI algorithms from making decisions without human oversight or guidance. There are also some difficult questions to answer.
Will we reach artificial general intelligence (AGI)? If AI can display true human-like intelligence with the ability to teach itself, this could result in autonomous self-control.
A strong ethical foundation must exist to prevent systems from making harmful decisions. There is also the question of existential rights, spurring philosophical questions about whether an AI system itself has rights.
AI technology is still in its infancy. The cited guidelines also do not directly address enforcement or compliance. Adoption of these frameworks or other ethical AI principles is voluntary.
Yet, developers and companies are under increasing competitive pressure to develop AI tools and there are financial incentives for doing so. PwC estimates that AI can increase revenues by $15 trillion by 2030, boosting the GDP of local economies by an additional 26%. These are powerful motivators to cut corners when it comes to ethics.
Large language models and Generative AI have also put powerful tools in the hands of everyone, including those with malicious intent. While these frameworks emphasise ethical use, transparency and accountability, they do not provide guardrails or enforcement powers to avoid misuse. There will need to be deep analysis and legislation that protects human rights, decides questions about fair use of copyright and intellectual property, and prohibits bias.
Ultimately, it will be up to regulators and legislators to define ethics in AI and establish laws to protect us, including enforcement mechanisms for violations. Even so, the patchwork of laws and regulations may still be ineffective in today’s cross-border economy.
While the OECD Framework, in particular, advocates for international cooperation, a global AI ethics policy is unlikely. Gaining universal alignment to consistent AI governance and ethical practices may not be possible considering the sheer number of nations that would have to agree to conflicting agendas.
By understanding and applying the AI ethics frameworks through the AI product lifecycle, developers, businesses, and legislators can contribute to the responsible development and deployment of AI systems. Without guiding principles about ethics in AI, each entity is left to make its own decision about what constitutes ethical behaviour.
Adopting a common framework for development, implementation, and use is crucial to creating trust in systems, addressing societal concerns, and maintaining human rights. AI must align with our shared values and principles to help create a better world. Everyone in the AI community should uphold the highest level of AI ethics.
Zendata integrates privacy by design across the entire data lifecycle, emphasising the context and risks associated with data usage. Our platform provides insights into data usage, third-party risks, and alignment with data protection regulations and policies.
