Generation AI

Our world is currently experiencing a digital renaissance powered by artificial intelligence (AI).

AI has existed for decades in one form or another, but over the past few months, generative AI has emerged as a game-changer. Generative AI models and products are redefining norms and boundaries across almost all sectors in society.

But beyond the dazzle of new technologies, we must think critically about how we can ensure generative AI benefits society as a whole. In particular, we must reflect on the interplay between AI and human culture, and ensure we take a human-centred approach. This is essential if we are to truly realise the potential of generative AI.

Digital anthropology and its role in tech and education

Digital anthropology explores the impact of digital technologies on society, culture, and human behaviour, and equips us with intellectual tools that can help examine the numerous ways in which emerging technologies such as generative AI can impact society. The field incorporates a human-centred perspective into discussions around tech and innovation that considers human needs, respects cultural diversity, and fosters responsible and accountable practices.

One area in which an anthropological perspective is particularly relevant is the education sector. Education forms a fundamental pillar of society, shaping the minds and values of future generations. Generative AI is set to transform the education sector, but its implications go far beyond the underlying technology and raise fundamental questions about who we are as a society and how we want to shape our collective future.

This is relevant not only for the institutions that we associate with education – schools, colleges, universities - but also for organisations across all industries who are conscious of and conscientious about their societal impact.

To help illustrate the value of this approach, let’s briefly explore three topics at the intersection of generative AI, education and society from anthropological perspective: 1) bias and inaccuracy, 2) ethical engagement, and 3) social inequality. In exploring these areas, we’ll demonstrate the value of anthropology within discussions around generative AI, and also the importance of an anthropological perspective within a wider commercial context.

Bias and inaccuracy

Data used to train generative AI models is not neutral, but reflects the cultural and historical context in which it is collected. As a result, biases that are present in the data can be perpetuated or amplified by AI systems, leading to potentially skewed outputs. These biases can reinforce existing power structures, perpetuate stereotypes, and marginalise certain perspectives and experiences within the educational sphere.

The complexity of generative AI models makes it challenging to identify embedded biases and trace their origins. The algorithmic processes that underpin model outputs remain opaque, including to both educators and students. We must all remain critical when using generative AI and question the validity and reliability of any outputs.

Educators play a crucial role in addressing biases and inaccuracies, including raising awareness among students about potential biases and stereotypes within AI-generated content. Educators need to equip students with the skills to critically engage with model outputs and assess their validity and accuracy. For example, universities are already issuing guidance to support students in using generative AI tools effectively, ethically and transparently.

Ethical engagement

Education goes beyond simply transferring information, and plays a vital role in encouraging critical thought and discussion. This requires ethical engagement with AI-produced knowledge, wherein students apply nuanced human perspectives by considering the social, cultural and historical context around the use and impact of AI-generated content.

Education plays an important role in nurturing ethical engagement with AI-produced knowledge. By cultivating a culture of responsible AI use, educators can guide students to consider the ethical implications of AI-generated outputs. This involves addressing issues such as privacy, consent, transparency, and the responsible use of AI technologies. By integrating these considerations into educational curricula, students can develop the necessary skills and ethical frameworks to engage with AI in a critical and responsible manner. For example, students exploring generative AI for visual art can critically analyse the outputs by considering the impact of generated images on different communities, and examine the implications of AI-generated artwork for copyright and intellectual property.

Social inequality

While some advanced large language models (LLMs) are currently accessible to the general public and have huge potential to democratise education, we must also acknowledge the presence of an existing digital divide in society. This divide is both in terms of access to technology, and to the knowledge required to use technology effectively and ethically.

Uneven implementation can widen existing social gaps. For example, if commercial tools that use generative AI cater primarily to well-funded private schools or countries with greater existing educational resources, this may contribute further to perpetuating social inequality. In contrast, by fostering a more inclusive and equitable approach to generative AI within education, we can strive towards a society where generative AI serves as a tool for empowerment and equal opportunity for all students.

Concluding thoughts

Generative AI is already a reality, and educators must quickly adapt to this new technology to ensure students can use these tools effectively, ethically, and transparently. Educators need to ensure they help resolve – as opposed to accentuate – some of the existing biases and inequalities in society.

In industry too, generative AI has the opportunity to both reduce and accentuate existing biases and inequalities, and a key role for Deloitte in the on-going generative AI revolution will undoubtedly be to help steer organisations towards valuable applications of generative AI, not just in the narrow sense of increased revenue and profit, but in the broader sense of value within organisations and society.

Anthropology helps us to untangle some of the complexities in achieving value across society as a whole. It forces us to ask difficult questions where there are no easy answers, but in doing so, we can help ensure we harness the full potential of generative AI and direct it towards building a better future for all.