Organisations speaking openly about equality, diversity and inclusion (EDI) is by now, as it should be, commonplace. From SMES to corporations, if they do not demonstrate a commitment to EDI, they are these days likely to be fiercely criticised and suffer reputational damage. In recent years, EDI has also become a key agenda item or more or less anyone involved in data and AI governance. But what exactly does EDI require? What should your organisation do to take EDI seriously? And, more specifically, what does it take for your organisational AI and data practices to uphold EDI?
The aim of this article is to give partial answers to these questions. I say ‘partial’ because EDI is not a straightforward end-point that will be achieved once your organisation puts some relevant policies in place. Rather, I suggest we should see EDI as a set of moral ideals, to be achieved by actively reflecting on and challenging our existing social, economic and political order. So, with that in mind, in this article, I will discuss:
- Key factors that have inspired contemporary EDI conversations;
- Reasons why we should all care about EDI in the workplace, and in society in general; and
- EDI issues related to AI and data practices, and how we might mitigate them.
Equality, Diversity and Inclusion: History and Meaning
Today, equality, diversity and inclusion have become umbrella terms associated with a wide range of social, political and economics injustices, especially in the Western context. To begin, it is important to understand what historical factors led to the EDI discussions that we are having nowadays.
Social and political movements over the last century have prompted the emergence of EDI-related regulations. In the mid-20th century, for instance, there were significant civil rights movements worldwide, particularly in the US, pushing for equal rights and opportunities for all, regardless of race and ethnicity. The feminist movement, which gained significant momentum in the 20th century, also brought attention to gender disparities and their related injustices. These, in conjunction with the many labour movements for equitable work conditions that have emerged across centuries, as well as issues of cultural diversity raised by increasing global migration, have all contributed to the significance we attach to equality, diversity and inclusion in the present day.
These movements have also accelerated a range of EDI-driven legislative actions, such as the Civil Rights Act of 1964 in the US, the UK Race Relations Act 1976, and so on. Such legal change has had a considerable impact on the workplace. For instance:
Workplace diversity training first emerged in the mid-1960s following the introduction of equal employment laws and affirmative action…These new laws prompted companies to start diversity training programs that would help employees adjust to working in more integrated offices’[1].
Nevertheless, there is disagreement among ethicists – unsurprisingly, perhaps, given the contestability of more or less all ethical judgements – about what equality, diversity and inclusion actually mean, and what they require in different contexts. Despite this, and fortunately, almost all of us converge on the view that some disadvantaged groups, or some forms of inequality, require corrective actions. As such, here are some inequalities or injustices that typically underpin EDI measures:
- Ethnic/Racial Inequality: Ethnic and racial minorities often face barriers to employment and opportunities for promotion; educational resources and attainments; healthcare services; fair representation in the criminal justice system; housing; and representation in mainstream media. This has led to the underrepresentation and stigmatisation of ethnic or racial minorities.
- Educational Inequality: There are disparities among different groups in their access to and quality of educational resources and attainment. Such disparities can result from economic, gender and racial inequalities, or other demographic factors. Educational inequality is particularly relevant to EDI in the workplace, as it is an important factor determining one’s career prospects.
- Wealth Inequality: Wealth has a considerable impact on one’s access to a range of social resources, e.g. educational resources, housing, and so on. In particular, intergenerational transfers of wealth play a significant role in perpetuating wealth inequality. To mitigate the impact of such perpetuating inequality, for instance, many privileged institutions have introduced recruitment quotas for economically disadvantaged individuals, so that, in the long run, there will be more representation of people from less financially privileged backgrounds.
- Gender Inequality: Gender inequality often manifests itself in pay gaps; occupational segregation, in which men are overrepresented in higher-paying fields and positions; underrepresentation of women in leadership positions within politics, business, academia, and other sectors; the disproportionate burden of care work between men and women, such as childcare, and so on. Moreover, our current socio-economic system is also unfavourable to transgender individuals.
- Historical Injustice: Marginalised groups can be disadvantaged partly because of historical injustices. These include, for example, slavery, colonisation and exploitation of indigenous peoples, genocide, segregation laws, forced assimilation policies and systemic discrimination.
This list of inequalities and injustices is certainly not exhaustive. How EDI exists in our collective imagination is heavily influenced by the new challenges facing our society. The far-reaching impact of AI and data is one of those challenges.
This background to contemporary discussions about EDI, moreover, ought to be taken seriously, even if you are looking for EDI advice specific to concrete AI and data practices. So, let’s dig into this a little further.
Why Care about EDI?
Why should your organisation, and perhaps everyone, care about EDI? The most influential political philosopher in the 20th century, John Rawls, famously maintains that our place in society is entirely contingent, and that many of us are socially privileged because we are lucky enough to have the natural endowments and social conditions that yield success.[2] Let’s call this phenomenon ‘the fact of contingencies’. When we think about what social and political systems we should seek to create, we should see each other as moral equals, by taking this fact of social contingencies seriously.
Similarly, marginalised groups in society are often disadvantaged by contingent factors entirely outside of their control. Failing to observe how lucky we are, and excluding the less fortunate individuals in arranging our everyday institutions, e.g. schools, the workplace, the government at a given time, is a failure to respect moral equality. As such, EDI initiatives, in a sense, are focused on addressing such unfair contingencies.
But there are economic benefits of EDI initiatives as well. For instance, it has been found that
- ‘companies in the top quartile for ethnic and cultural diversity outperform those in the fourth by 36% in profitability… diverse companies report 19% higher innovation revenues’[3].
- When employees feel that ‘their organisation is committed to and supportive of diversity…their ability to innovate increases by 83%’[4].
- Diversity has positive impact on the quality of deliberation processes.[5]
- ‘76% of job seekers and employees believe that a diverse workforce is an important factor when evaluating job offers, and nearly a third (32%) would not apply to a company that lacks diversity’[6].
These findings suggest at least that EDI, when promoted in the right way, can be expected to enhance the performance, morale and appeal of your organisation.
EDI Issues in AI and Data, and How to Address Them
The far-reaching impact of AI and data has had an unprecedented impact on our society. So, here are some familiar EDI issues which occur in AI and data, and some possible ways to address them.
If you want to deepen your understanding of EDI in AI and data and learn how to apply best practices in your organisation, you can explore IGS’ specialised training module: Equality, Diversity and Inclusion in AI and Data.
Algorithmic Biases
We all know by now that AI systems learn from data. So, if the training data for AI systems is biased or unrepresentative, the systems can perpetuate or even amplify existing biases. IBM has given some relevant examples:[7]
- Because of the underrepresentation of data of minority groups, ‘computer-aided diagnosis (CAD) systems have been found to return lower accuracy results for black patients than white patients’.
- Applicant tracking systems produce biased results. For example, ‘Amazon stopped using a hiring algorithm after finding it favored applicants based on words like “executed” or “captured,” which were more commonly found on men’s resumes’.
- The AI art generation application Midjourney, when ‘asked to create images of people in specialised professions, it showed both younger and older people, but the older people were always men, reinforcing gendered bias of the role of women in the workplace’.
Algorithmic biases have also been a key issue in online advertising and predictive policing. These biases can be addressed by diverse and representative data collection, bias mitigation algorithms, regular monitoring and auditing, and so on.
Lack of Diversity in AI Development
The current infrastructure of AI expertise and finance itself falls short of diversity. For instance, New York University has found that ‘More than 80% of AI professors are men, and only 15% of AI researchers at Facebook and 10% of AI researchers at Google are women’[8].
There are also geographical disparities in AI development. According to the World Economic Forum, an Oxford Insights assessment of 181 countries around the world and their preparedness in using AI in public services highlights that the lowest-scoring regions include much of the Global South, such sub-Saharan Africa, some Central and South Asian countries, and some Latin American countries.[9]
These days, the development of AI systems increasingly often involves input from academics or other professionals similarly formally trained in AI ethics. What is concerning, however, is that even the field of AI ethics research lacks diversity. For example, According to Nature:
Scientists analysed 375 research and review articles on the fairness of artificial intelligence in health care, published in 296 journals between 1991 and 2022. Of 1,984 authors, 64% were white, whereas 27% were Asian, 5% were Black and 4% were Hispanic (see ‘Gaps in representation’). The analysis…also found that 60% of authors were male and 40% female, a gender gap that was heightened among last authors, who often have a senior role in leading the research.[10]
The direct result of a lack of diversity in the AI workforce is that AI development will be impeded if the perspectives providing input remain limited, and this can potentially reinforce existing algorithmic biases, since the relevant system designers and evaluators are biased already. So, if your organisation develops or uses AI-driven systems, then enhancing the diversity of your workforce, will allow those systems to benefit from a wider range of perspectives, and put your organisation in a better position to identify the potential biases that exist in those systems.
Access to AI
Using AI systems requires a certain level of digital literacy, and not all AI systems are equally user-friendly. For example, if your organisation implements a new AI system, some of your employees might find it difficult to master those systems, and thus might find themselves at a professional disadvantage. They might have backgrounds that limit their digital literacy, internet access and technological capabilities. Or, another example would be voice-based AI assistants which fail to recognise non-standard speech patterns or accents. This might exclude those with speech impairments or non-native speakers from using such assistive technologies.
The key lesson here, then, is that your organisation ought to put effective measures in place whenever you introduce new AI or data systems to your employees, to ensure equal mastery of those systems across the organisation, taking into account differences in levels of digital literacy.
Given the technical nature of the AI and data systems of different organisations, as well as the complexity of how those systems interact with AI and data regulations, we suggest that a sensible course of action for your organisation would be to seek external advisors, such as ourselves at IGS, to consider what EDI measures are suitable for your particular circumstances.
Conclusion
As with many other EDI issues, there are no perfect solutions to EDI problems in the domain of AI and data. For example, in the past we might have mistakenly thought that once we digitalise our hiring processes, they will become fairer and less biased. However, as it turns out, algorithms themselves can be biased, and AI and data developers and ethicists are still working hard to address these.
This also suggests why, as I said at the beginning, EDI is not a straightforward endpoint that can be achieved once we put certain measures in place. Organisations need standing practices and individuals which can evaluate whether and how their procedures and systems marginalise certain groups. Most importantly, your organisation should seek to develop a culture in which everyone actively familiarises themselves with EDI-related issues, and the implications of these for AI and data processes.
To help you to begin navigating EDI challenges in AI and data, IGS has developed a dedicated online training module that your organisation can find via our website. And beyond this, if your organisation needs consultancy or training in the development and implementation of EDI practices in data and AI governance, at IGS we are here to help with whatever you need.
[1] Dong, S. (2021). The History and Growth of the Diversity, Equity, and Inclusion Profession. [online] Global Research and Consulting Group Insights. Available at: https://insights.grcglobalgroup.com/the-history-and-growth-of-the-diversity-equity-and-inclusion-profession/.
[2] Rawls, J. (1971). A Theory of Justice. Harvard University Press.
[3] Rizvi, J. (2023). How AI Can Be Leveraged For Diversity And Inclusion. [online] Forbes. Available at: https://www.forbes.com/sites/jiawertz/2023/11/19/how-ai-can-be-leveraged-for-diversity-and-inclusion/?sh=6fe168194ee9 [Accessed 20 Feb. 2024].
[4] Guerra, S. (2020). Invest in Inclusion: The Business Case for EDI – Diversity Digest. [online] Diversity Digest. Available at: https://blogs.kcl.ac.uk/diversity/2020/11/02/invest-in-inclusion-the-business-case-for-edi/.
[5] Bergold, A.N. and Bull Kovera, M. (2021). Diversity’s Impact on the Quality of Deliberations. Personality and Social Psychology Bulletin, p.014616722110409. doi:https://doi.org/10.1177/01461672211040960.
[6] Chen, J. (2022). Here’s how to tailor employee benefits to a diverse workforce. [online] World Economic Forum. Available at: https://www.weforum.org/agenda/2022/09/employee-benefits-diversity/.
[7] IBM Data and AI Team (2023). Shedding light on AI bias with real world examples. [online] IBM Blog. Available at: https://www.ibm.com/blog/shedding-light-on-ai-bias-with-real-world-examples/.
[8] Paul, K. (2019). ‘Disastrous’ Lack of Diversity in AI Industry Perpetuates bias, Study Finds. [online] The Guardian. Available at: https://www.theguardian.com/technology/2019/apr/16/artificial-intelligence-lack-diversity-new-york-university-study.
[9] Yu, D., Rosenfeld, H. and Gupta, A. (2023). The ‘AI divide’ between the Global North and Global South. [online] World Economic Forum. Available at: https://www.weforum.org/agenda/2023/01/davos23-ai-divide-global-north-global-south/.
[10] Wong, C. (2023). AI ‘fairness’ research held back by lack of diversity. Nature. doi:https://doi.org/10.1038/d41586-023-00935-z.
