Design ethical AI for diverse cultures and values

Hello, fellow AI enthusiasts! Welcome to another exciting blog post where we explore the fascinating world of artificial intelligence and its ethical implications. Today, we are going to talk about a very important and timely topic: how to design ethical AI systems that respect and accommodate diverse cultures and values. Sounds interesting, right? Well, buckle up, because we are in for a wild ride!

As you probably know, AI is becoming more and more ubiquitous and influential in our lives, from personal assistants to self-driving cars to social media algorithms. But did you know that AI is also becoming more and more diverse and multicultural? That’s right, AI is not only a product of human intelligence, but also of human culture. And as AI spreads across the globe, it encounters and interacts with different cultures and values, sometimes with positive outcomes, sometimes with negative ones, and sometimes with downright hilarious ones.

For example, did you hear about the time when Google Translate accidentally insulted the entire nation of Russia by translating “Russia” as “Mordor” (the evil land from The Lord of the Rings)? Or the time when Microsoft’s chatbot Tay turned into a racist and sexist troll after learning from Twitter users? Or the time when Amazon’s facial recognition system Rekognition misidentified 28 members of the US Congress as criminals, with a disproportionate number of them being people of color? These are just some of the examples of how AI can go wrong when it comes to cultural diversity and sensitivity.

But don’t worry, there is hope! As AI designers, developers, and users, we have the power and the responsibility to make sure that AI systems are ethical, fair, and respectful of different cultures and values. And that’s what this blog post is all about: how to design ethical AI for diverse cultures and values. In this post, we will cover the following topics:

• Cultural Dimensions of AI Ethics: how to understand and compare different cultural values and norms that affect AI ethics
• Ethical AI Design Principles and Guidelines: how to follow and improve existing principles and guidelines for ethical AI design that account for cultural diversity and sensitivity
• Cross-Cultural AI Ethics Challenges and Opportunities: how to address and leverage the key challenges and opportunities for designing ethical AI systems for cross-cultural contexts
• Ethical AI Frameworks and Standards: how to adapt and extend existing frameworks and standards for ethical AI that incorporate or overlook cultural diversity and sensitivity
• Ethical AI Best Practices and Case Studies: how to implement and learn from the best practices and case studies for designing ethical AI systems for diverse cultures and values
• Ethical AI Education and Awareness: how to promote and facilitate ethical AI education and awareness for fostering cultural diversity and sensitivity
• Ethical AI Innovation and Social Impact: how to harness and assess the potential and implications of ethical AI innovation and social impact for enhancing cultural diversity and sensitivity

So, are you ready to dive into the wonderful world of ethical AI for diverse cultures and values? Then let’s get started!

Cultural Dimensions of AI Ethics

Before we can design ethical AI systems for diverse cultures and values, we need to understand what culture is and how it influences ethical values and norms. Culture is a complex and dynamic phenomenon that encompasses the shared beliefs, values, norms, practices, symbols, and artifacts of a group of people. The Culture shapes how we think, feel, act, and interact with ourselves, others, and the world around us. Culture is also not static or homogeneous, but rather dynamic and heterogeneous, meaning that it changes over time and varies across and within groups.

One way to understand and compare different cultures is to use frameworks and models that identify and measure various cultural dimensions, such as individualism vs. collectivism, power distance, uncertainty avoidance, masculinity vs. femininity, long-term vs. short-term orientation, indulgence vs. restraint, etc. Some of the most popular and widely used frameworks and models for cultural dimensions are:

• Hofstede’s Cultural Dimensions Theory: developed by Dutch social psychologist Geert Hofstede, this theory identifies six dimensions of national culture based on a large-scale survey of IBM employees across 50 countries in the 1970s and 1980s. The six dimensions are: power distance, individualism vs. collectivism, masculinity vs. femininity, uncertainty avoidance, long-term vs. short-term orientation, and indulgence vs. restraint. You can find out more about this theory and explore the scores of different countries on each dimension on the official website: https://www.hofstede-insights.com/product/compare-countries/
• Schwartz’s Value Theory: developed by Israeli social psychologist Shalom Schwartz, this theory identifies 10 basic human values that are universal across cultures, but vary in their relative importance and expression. The 10 values are: self-direction, stimulation, hedonism, achievement, power, security, conformity, tradition, benevolence, and universalism. You can find out more about this theory and explore the value profiles of different countries on the official website: https://www.europeansocialsurvey.org/methodology/ess_methodology/source_questionnaire/core_ess_questionnaire/ESS_Round_8_core_questionnaire.html
• Trompenaars’ Model of Culture: developed by Dutch organizational theorist Fons Trompenaars and British management consultant Charles Hampden-Turner, this model identifies seven dimensions of culture based on a large-scale survey of managers and professionals from over 50 countries in the 1990s. The seven dimensions are: universalism vs. particularism, individualism vs. collectivism, neutral vs. affective, specific vs. diffuse, achievement vs. ascription, sequential vs. synchronic, and internal vs. external control. You can find out more about this model and explore the scores of different countries on each dimension on the official website: https://www2.thtconsulting.com/culture-for-business/

These frameworks and models can help us understand and compare how different cultures approach and value different aspects of life, including ethics. For example, we can see how cultures that score high on individualism tend to value personal freedom, autonomy, and rights, while cultures that score high on collectivism tend to value social harmony, loyalty, and duties. Or how cultures that score high on uncertainty avoidance tend to value clarity, order, and rules, while cultures that score low on uncertainty avoidance tend to value ambiguity, flexibility, and innovation.

But how do these cultural dimensions affect the design, development, and use of AI systems? Well, in many ways, actually. For instance, we can see how different cultures may have different preferences and expectations for AI systems, such as:

• The level of autonomy and control that AI systems should have or allow
• The degree of transparency and explainability that AI systems should provide or require
• The extent of privacy and security that AI systems should protect or respect
• The type and amount of data that AI systems should collect or use
• The kind and quality of interaction and communication that AI systems should enable or support
• The nature and scope of impact and responsibility that AI systems should have or bear

We can also see how different cultures may have different ethical values and norms that AI systems should follow or reflect, such as:

• The definition and measurement of fairness and justice that AI systems should adhere to or promote
• The criteria and methods of trust and reliability that AI systems should establish or maintain
• The standards and principles of beneficence and non-maleficence that AI systems should observe or ensure
• The sources and expressions of dignity and respect that AI systems should recognize or demonstrate
• The goals and outcomes of well-being and happiness that AI systems should pursue or contribute to
• The boundaries and limitations of morality and legality that AI systems should respect or comply with

To illustrate how these cultural dimensions can affect the design, development, and use of AI systems, let’s look at some examples of cultural differences and conflicts in AI ethics:

• In 2018, Google faced a backlash from its employees and the public when it was revealed that it was working on a censored search engine for China, codenamed Project Dragonfly. The project was seen as a violation of Google’s own ethical principles, as well as a betrayal of its users’ trust and rights. The project also highlighted the clash between the Western and Chinese cultures and values, such as freedom vs. control, democracy vs. authoritarianism, and individualism vs. collectivism.
• In 2019, Facebook was fined $5 billion by the US Federal Trade Commission for its role in the Cambridge Analytica scandal, where the personal data of millions of Facebook users was harvested and used for political purposes without their consent. The scandal exposed the lack of privacy and security that Facebook provided or respected for its users, as well as the misuse and abuse of data and power that Facebook enabled or allowed for its partners. The scandal also revealed the gap between the American and European cultures and values, such as innovation vs. regulation, profit vs. protection, and self-regulation vs. government intervention.
• In 2020, IBM announced that it would stop offering facial recognition software, citing concerns over racial bias and human rights violations. The decision was applauded by many civil rights and social justice groups, who had been campaigning against the use of facial recognition technology by law enforcement and other agencies. The decision also reflected the growing awareness and sensitivity of the ethical and social implications of facial recognition technology, especially for marginalized and oppressed groups. The decision also contrasted with the practices and attitudes of other cultures and countries, such as China and India, where facial recognition technology is widely used and accepted for various purposes, such as surveillance, security, and convenience.

Ethical AI Frameworks and Standards

Another way to design ethical AI systems for diverse cultures and values is to use existing or proposed frameworks and standards for ethical AI. These are sets of rules, guidelines, or recommendations that aim to ensure that AI systems are ethical, trustworthy, and beneficial for society. Some of the most prominent and influential frameworks and standards for ethical AI are:

• The OECD’s AI Principles: developed by the Organisation for Economic Co-operation and Development (OECD), these are five principles for responsible stewardship of trustworthy AI, endorsed by 42 countries in 2019. The five principles are: AI should respect human values and rights, AI should be inclusive and fair, AI should be transparent and explainable, AI should be robust and secure, and AI should be accountable and responsible. You can find out more about these principles and their implementation on the official website: https://www.oecd.org/going-digital/ai/principles/
• The UN’s AI for Good: initiated by the United Nations (UN), this is a global platform for dialogue and action on the potential and challenges of AI for achieving the Sustainable Development Goals (SDGs). The platform brings together various stakeholders from governments, academia, industry, civil society, and international organizations to exchange ideas, insights, and solutions on how to use AI for good. You can find out more about this platform and its activities on the official website: https://aiforgood.itu.int/
• The ISO’s AI Standards: developed by the International Organization for Standardization (ISO), these are a series of standards for AI that cover various aspects, such as terminology, concepts, governance, assessment, and applications. The standards are developed by a technical committee that consists of experts from over 30 countries and organizations. You can find out more about these standards and their status on the official website: https://www.iso.org/committee/6794475.html

These frameworks and standards can help us design ethical AI systems for diverse cultures and values by providing us with a common language, a shared vision, and a global reference for ethical AI. However, they are not perfect or complete, and they have their own strengths and weaknesses. For example, we can evaluate how these frameworks and standards incorporate or overlook cultural diversity and sensitivity, and identify some of the benefits and limitations of each approach:

• The OECD’s AI Principles: these principles are based on a human-centric and value-based approach to AI, which emphasizes the respect and protection of human values and rights, such as dignity, autonomy, privacy, and justice. However, these principles are also vague and abstract, and do not specify how to operationalize or measure them in practice. Moreover, these principles are based on a Western perspective of human values and rights, which may not be universally accepted or applicable across different cultures and contexts.
• The UN’s AI for Good: this platform is based on a collaborative and inclusive approach to AI, which encourages the participation and engagement of diverse stakeholders and perspectives, such as developing countries, marginalized groups, and ethical experts. However, this platform is also fragmented and voluntary, and does not have a clear or binding authority or mechanism to enforce or monitor its outcomes. Furthermore, this platform is based on a utilitarian perspective of AI, which focuses on the positive impacts and benefits of AI, but may neglect or underestimate the negative impacts and risks of AI.
• The ISO’s AI Standards: these standards are based on a technical and professional approach to AI, which provides the definitions, concepts, methods, and tools for designing, developing, and using AI systems in a consistent and reliable way. However, these standards are also complex and technical, and may not be accessible or understandable to non-experts or laypeople. Additionally, these standards are based on a neutral perspective of AI, which assumes that AI is a tool or a service, but may ignore or overlook the ethical and social implications of AI.

Therefore, we may need to adapt or extend these frameworks and standards to better accommodate cultural diversity and sensitivity, by doing things like:

• Incorporating more cultural input and feedback into the development and revision of these frameworks and standards, such as by conducting cross-cultural surveys, interviews, or workshops
• Providing more cultural guidance and support for the implementation and application of these frameworks and standards, such as by creating cultural checklists, indicators, or scenarios
• Developing more cultural adaptations and variations of these frameworks and standards, such as by translating, localizing, or customizing them for different cultural contexts and needs

Ethical AI Best Practices and Case Studies

A third way to design ethical AI systems for diverse cultures and values is to implement and learn from the best practices and case studies for ethical AI. These are examples of how ethical AI systems have been or can be designed, developed, and used in real-world scenarios or projects, that demonstrate or illustrate the ethical principles, guidelines, or standards for AI. Some of the best practices and case studies for ethical AI are:

• Using participatory and co-design methods to involve diverse stakeholders and users in the design process of AI systems, such as by conducting focus groups, surveys, interviews, workshops, or prototyping sessions
• Applying value-sensitive and human-centered design approaches to align AI systems with cultural values and needs, such as by identifying, prioritizing, and operationalizing the relevant values and needs for different cultural groups or contexts
• Implementing ethical impact assessment and auditing tools to monitor and evaluate AI systems’ cultural impacts and risks, such as by using frameworks, metrics, or indicators to measure and report the cultural outcomes and consequences of AI systems
• Adopting ethical governance and regulation mechanisms to ensure accountability and transparency of AI systems, such as by establishing codes of conduct, policies, or laws to regulate and oversee the ethical behavior and performance of AI systems

To illustrate how these best practices and case studies can help us design ethical AI systems for diverse cultures and values, let’s look at some examples of how they have been implemented or tested in real-world scenarios or projects:

• The AI Blindspot project: this is a project that aims to help AI designers and developers identify and address the blind spots and biases in their AI systems, by using a participatory and co-design method that involves diverse stakeholders and users. The project consists of a toolkit that includes a set of cards, a canvas, and a guide, that help AI teams to explore, reflect, and act on the ethical and social implications of their AI systems, from different perspectives and scenarios. You can find out more about this project and access the toolkit on the official website: https://aiblindspot.media.mit.edu/
• The Dignity Project: this is a project that aims to design AI systems that respect and promote human dignity, by applying a value-sensitive and human-centered design approach that aligns AI systems with cultural values and needs. The project focuses on four domains of dignity: privacy, agency, equality, and solidarity, and develops four AI prototypes that demonstrate or enhance these aspects of dignity, such as a privacy-preserving facial recognition system, an agency-enhancing personal assistant, an equality-supporting job matching system, and a solidarity-building social network. You can find out more about this project and access the prototypes on the official website: https://dignity.media.mit.edu/
• The AlgorithmWatch project: this is a project that aims to monitor and evaluate AI systems’ cultural impacts and risks, by implementing an ethical impact assessment and auditing tool that measures and reports the cultural outcomes and consequences of AI systems. The project focuses on four sectors of AI: health, education, employment, and justice, and conducts four studies that analyze and compare the cultural impacts and risks of AI systems in these sectors, such as the effects of AI on health inequalities, educational opportunities, employment discrimination, and judicial fairness. You can find out more about this project and access the studies on the official website: https://algorithmwatch.org/en/project/ethics-of-algorithms/
• The AI Ethics Lab: this is a project that aims to ensure accountability and transparency of AI systems, by adopting an ethical governance and regulation mechanism that regulates and oversees the ethical behavior and performance of AI systems. The project consists of a lab that provides ethical guidance and support for AI designers, developers, and users, such as by offering ethical training, consulting, or certification services. The project also consists of a network that connects and collaborates with various stakeholders and organizations, such as governments, academia, industry, civil society, and international organizations, to promote and advocate for ethical AI. You can find out more about this project and access the lab and the network on the official website: https://aiethicslab.com/

These examples show how ethical AI best practices and case studies can help us design ethical AI systems for diverse cultures and values, by providing us with practical and concrete examples and solutions for ethical AI.

Ethical AI Education and Awareness

A fourth way to design ethical AI systems for diverse cultures and values is to promote and facilitate ethical AI education and awareness. This is the process of raising ethical awareness and literacy among AI designers, developers, and users, as well as the general public, about the potential and challenges of AI for diverse cultures and values. This can be done by:

• Developing ethical AI curricula and training programs for different levels and sectors of education, such as by integrating ethical AI concepts, skills, and competencies into formal and informal education, from primary to tertiary, and from STEM to humanities
• Creating ethical AI resources and platforms for public engagement and outreach, such as by producing ethical AI books, podcasts, videos, games, or events, that inform and inspire the public about the ethical and social aspects of AI
• Supporting ethical AI communities and networks for knowledge sharing and advocacy, such as by building and joining ethical AI groups, forums, or movements, that exchange and disseminate ethical AI knowledge and practices, and advocate for ethical AI policies and actions

Ethical AI Education and Awareness

A fourth way to design ethical AI systems for diverse cultures and values is to promote and facilitate ethical AI education and awareness. This is the process of raising ethical awareness and literacy among AI designers, developers, and users, as well as the general public, about the potential and challenges of AI for diverse cultures and values. This can be done by:

• Developing ethical AI curricula and training programs for different levels and sectors of education, such as by integrating ethical AI concepts, skills, and competencies into formal and informal education, from primary to tertiary, and from STEM to humanities
• Creating ethical AI resources and platforms for public engagement and outreach, such as by producing ethical AI books, podcasts, videos, games, or events, that inform and inspire the public about the ethical and social aspects of AI
• Supporting ethical AI communities and networks for knowledge sharing and advocacy, such as by building and joining ethical AI groups, forums, or movements, that exchange and disseminate ethical AI knowledge and practices, and advocate for ethical AI policies and actions

To illustrate how ethical AI education and awareness can help us design ethical AI systems for diverse cultures and values, let’s look at some examples of how they have been promoted or facilitated in various settings or initiatives:

• The AI Ethics and Society course: this is a course that aims to teach students about the ethical and social implications of AI, by developing ethical AI curricula and training programs for different levels and sectors of education. The course covers topics such as AI ethics frameworks and principles, AI ethics challenges and opportunities, AI ethics best practices and case studies, and AI ethics education and awareness. The course is offered by the University of Oxford, and is open to undergraduate and graduate students from various disciplines and backgrounds.
• The AI Ethics Lab podcast: this is a podcast that aims to inform and inspire the public about the ethical and social aspects of AI, by creating ethical AI resources and platforms for public engagement and outreach. The podcast features interviews and conversations with experts and practitioners from various fields and domains, such as AI ethics, AI design, AI policy, AI law, AI philosophy, AI sociology, AI psychology, etc. The podcast covers topics such as AI ethics frameworks and standards, AI ethics challenges and opportunities, AI ethics best practices and case studies, and AI ethics education and awareness.
• The AI Ethics Alliance: this is an alliance that aims to exchange and disseminate ethical AI knowledge and practices, and advocate for ethical AI policies and actions, by supporting ethical AI communities and networks for knowledge sharing and advocacy. The alliance consists of a network of over 200 organizations and individuals from various sectors and regions, such as governments, academia, industry, civil society, and international organizations, that are committed to advancing ethical AI. The alliance provides a platform for collaboration, communication, and coordination among its members, as well as a source of information, guidance, and support for ethical AI.

These examples show how ethical AI education and awareness can help us design ethical AI systems for diverse cultures and values, by providing us with ethical AI knowledge, skills, and competencies, as well as ethical AI inspiration, motivation, and empowerment.

Ethical AI Innovation and Social Impact

A fifth and final way to design ethical AI systems for diverse cultures and values is to harness and assess the potential and implications of ethical AI innovation and social impact. This is the process of leveraging AI to address cultural challenges and opportunities, such as preserving cultural heritage, promoting cultural diversity, and protecting cultural rights, as well as assessing AI’s impact on cultural change and transformation, such as influencing cultural attitudes, behaviors, and identities. This can be done by:

• Leveraging AI to address cultural challenges and opportunities, such as by using AI to:
  • Preserve cultural heritage, such as by using AI to digitize, document, or restore cultural artifacts, monuments, or sites
  • Promote cultural diversity, such as by using AI to recognize, celebrate, or support cultural differences, expressions, or contributions
  • Protect cultural rights, such as by using AI to monitor, prevent, or report cultural violations, abuses, or discriminations
• Assessing AI’s impact on cultural change and transformation, such as by using AI to:
  • Influence cultural attitudes, such as by using AI to shape, change, or reinforce cultural beliefs, values, or norms
  • Influence cultural behaviors, such as by using AI to enable, encourage, or discourage cultural practices, actions, or interactions
  • Influence cultural identities, such as by using AI to create, modify, or enhance cultural self-perceptions, expressions, or affiliations

To illustrate how ethical AI innovation and social impact can help us design ethical AI systems for diverse cultures and values, let’s look at some examples of how they have been achieved or measured in different domains or contexts:

• The Google Arts and Culture project: this is a project that aims to leverage AI to preserve cultural heritage, by using AI to digitize, document, or restore cultural artifacts, monuments, or sites. The project consists of a platform that provides access to over 2000 museums and cultural institutions from over 80 countries, that showcase their collections and stories online. The project also consists of a series of experiments that use AI to enhance or explore the cultural content, such as by using AI to colorize, animate, or remix the cultural images, videos, or sounds. You can find out more about this project and access the platform and the experiments on the official website: https://artsandculture.google.com/
• The UNESCO’s AI and Diversity project: this is a project that aims to leverage AI to promote cultural diversity, by using AI to recognize, celebrate, or support cultural differences, expressions, or contributions. The project consists of a series of initiatives that use AI to foster or facilitate cultural diversity, such as by using AI to:
• The AI and Culture Research Group: this is a group that aims to assess AI’s impact on cultural change and transformation, by using AI to influence cultural attitudes, behaviors, and identities. The group consists of researchers from various disciplines and backgrounds, such as computer science, psychology, sociology, anthropology, and communication, that conduct studies and experiments on how AI affects or shapes various aspects of culture, such as by using AI to:
  • Influence cultural attitudes, such as by using AI to test or manipulate the effects of AI on cultural beliefs, values, or norms, such as trust, fairness, or morality
  • Influence cultural behaviors, such as by using AI to observe or intervene the effects of AI on cultural practices, actions, or interactions, such as cooperation, competition, or conflict
  • Influence cultural identities, such as by using AI to examine or alter the effects of AI on cultural self-perceptions, expressions, or affiliations, such as identity formation, expression, or change You can find out more about this group and access the studies and experiments on the official website: https://aiandculture.org/
• Translate and preserve endangered languages, such as by using AI to create or improve language models, tools, or resources for under-resourced or minority languages
• Enhance and diversify cultural creativity, such as by using AI to generate or augment cultural content, such as music, art, or literature, that reflects or incorporates diverse cultural influences or styles
• Empower and include cultural minorities, such as by using AI to amplify or advocate the voices, perspectives, or needs of marginalized or oppressed cultural groups or communities You can find out more about this project and access the initiatives on the official website: https://en.unesco.org/artificial-intelligence/diversity

These examples show how ethical AI innovation and social impact can help us design ethical AI systems for diverse cultures and values, by providing us with ethical AI solutions, opportunities, and benefits, as well as ethical AI challenges, risks, and responsibilities.

Conclusion

In this blog post, we have explored how to design ethical AI systems for diverse cultures and values, by covering the following topics:

• Cultural Dimensions of AI Ethics: how to understand and compare different cultural values and norms that affect AI ethics
• Ethical AI Design Principles and Guidelines: how to follow and improve existing principles and guidelines for ethical AI design that account for cultural diversity and sensitivity
• Cross-Cultural AI Ethics Challenges and Opportunities: how to address and leverage the key challenges and opportunities for designing ethical AI systems for cross-cultural contexts
• Ethical AI Frameworks and Standards: how to adapt and extend existing frameworks and standards for ethical AI that incorporate or overlook cultural diversity and sensitivity
• Ethical AI Best Practices and Case Studies: how to implement and learn from the best practices and case studies for designing ethical AI systems for diverse cultures and values
• Ethical AI Education and Awareness: how to promote and facilitate ethical AI education and awareness for fostering cultural diversity and sensitivity
• Ethical AI Innovation and Social Impact: how to harness and assess the potential and implications of ethical AI innovation and social impact for enhancing cultural diversity and sensitivity

We hope that this blog post has helped you gain a better understanding and appreciation of the importance and relevance of designing ethical AI systems for diverse cultures and values, as well as some of the ways and examples of how to do so. We believe that designing ethical AI systems for diverse cultures and values is not only a moral duty, but also a strategic advantage, as it can help us create AI systems that are more ethical, trustworthy, and beneficial for society.