AI By All: Embracing Diversity For Ethical AI And Sustainable Digital Transformation

The present generation is witnessing several euphoric moments in artificial intelligence (AI) leading to a very rapid influence of intelligent technologies in industrial automation, decision making, medical analytics, drug discovery, precision medicine, fintech solutions, retail, manufacturing, and whatnot. With AI becoming more ubiquitous, there is also a groundswell of concerns related to ethics and biases that seeps into AI models. Hence, it's important to understand that AI models build their intelligence based on data pools. This means that a large amount of historical or real-time data is the key to driving the AI and Deep Learning (DL) based model training and intelligence building. The biases stem from data repositories, or more specifically from data that is used to train AI software and teams that influence how technology is built. Several factors play a role here – the key being the need to have a rational representation of the population in AI and data science domain.


The recently released 2023 AI Index report by Stanford University also bears out this fact. The report spotlights a sharp “disparity between the individuals who develop AI and those who use AI.” Only 22% of AI professionals globally are female, compared to 78% who are male according to the World Economic Forum. This lack of diversity in building AI systems can perpetuate existing societal inequalities and biases in the long run, due to the decision-making capabilities that AI influences us to have directly or indirectly. Hence, it's imperative to say that building AI systems that are not only technologically advanced but also ethical, fair, and inclusive has never been more critical. By fostering diversity, we empower teams to identify and rectify biases, ensuring AI systems are fair and just, irrespective of gender, race, or background.


What is the source of bias?


Training data bias: AI systems make decisions based on training data and hence it is key to build diverse datasets and widen the scope of data sources to provide fair and equitable results. For example, training data for a facial recognition algorithm that over-represents a section of people and lacks data of other ethnicities will result in biased output and perpetuate racial bias. Another case could be a breast cancer detection solution trained on a specific group of women with locality reference that might deliver inaccurate results for women from another region due to changes in genetic constitution and various other physiological factors.


Algorithmic bias: Using flawed data leads to algorithmic bias and can discriminate against people of specific gender or race. A case in point is a popular text-to-image AI tool that uses diffusion models to create output, provided skewed results when prompted to produce images of “reporter” or “correspondent”. The system generated images of light-skinned people, and fails to easily interpret women dressed in Indian attires like saree; underscoring the lack of diverse dataset, signaling the lack of diversity in the AI industry.


Cognitive bias: Cognitive bias extends beyond technology and manifests when people building AI systems may reinforce existing inequalities during AI development through their perception or notions which can affect the trustworthiness of AI systems.



What is the impact of data bias?


While a lot has been written about AI’s diversity crisis, the lack of equal representation of population (women in general) in building AI based systems results in flawed systems that are often due to inadequate datasets that fail to reflect cultural and socio-economic knowledge. By excluding diverse groups – resulting AI systems often lead to biased algorithms that show a higher error rate for other diverse groups of people. This does not only have negative impacts on women, but also business and economies and usefulness of AI based solutions in general.

Hence, to reduce the risk of biases, AI development teams should have individuals from diverse backgrounds, perspectives, and experiences. This will not only ensure that data being used to train AI systems is fair and equitable but also weed out cases of biases in the development cycle. Additionally, diverse teams with women are more likely to consider the ethical implications of the system and prioritise fairness and transparency in AI systems. Women bring their unique perspective to the table and have shown commendable contributions to research and innovation.

According to the World Economic Forum, the release of Gen AI applications for mass consumption further exacerbates automation bias, fueled by insufficient transparency about model capabilities and limitations. This lack of transparency extends inequities across multiple domains or sectors. Diverse teams are better equipped to understand and address the complexities of a globalised market.


What are the advantages of fostering diversity in AI development?




Building a pool of diverse datasets: As AI becomes more ubiquitous and AI systems continues to shape the economy, it is crucial for AI development teams to have a diverse data pool that embodies transparency and inclusivity. This captures a spectrum of perspectives, enabling a more nuanced understanding of varied user needs and cultural contexts. This ensures that AI systems are not one-size-fits-all but are AI-By-ALL.



Fostering problem-solving and innovation: Diversity in building AI systems can help bring a wider range of people with diverse perspectives together. The amalgamation of different backgrounds, experiences, and viewpoints fosters a culture of innovation that is crucial for developing AI solutions capable of addressing a wide range of challenges and opportunities. This not only helps in overcoming biases but also supercharges creativity and innovation which is crucial in the field of AI where technology is evolving at a rapid clip.

Building Trust and Acceptance: Users are more likely to trust AI systems developed by diverse teams. Trust is a cornerstone for the widespread adoption of AI applications, and a lack of diversity can contribute to scepticism and resistance among users. AI is a global phenomenon, and trust and success of AI hinges on its ability to navigate diverse markets and cultural nuances.

Legal and Regulatory Compliance: Legal and regulatory frameworks increasingly demand fairness and non-discrimination in AI applications. Embracing diversity in development teams ensures organisations comply with these regulations, mitigating legal risks associated with biased or discriminatory AI systems.

Unlock new opportunities for socio-economic growth: A gender balanced AI workforce can build a more equitable future and drive economic development. By investing in diversity and inclusion initiatives, organisations will be better positioned to tap into new markets and drive growth.

Social Responsibility: Companies and organisations have a social responsibility to ensure their technologies benefit society as a whole. Diverse and inclusive development practices contribute to building ethical AI systems that align with broader societal values.




Why AI diversity matters



People are an intrinsic part of our technological innovation, and the future of AI is less artificial and more humanlike.

So, as the AI landscape continues to evolve, the importance of diversity and inclusion cannot be overstated. It is not just a matter of representation; it is strategically imperative for building AI systems that are ethical, fair, and inclusive. Companies that champion diversity in their AI development teams are not only meeting legal and ethical standards but are also driving innovation and building a technological future that serves all of humanity. It’s important that a diverse continuum of people work closely with AI models, towards enhancing learning, productivity, and satisfaction to bring out human touch in technology and build inclusive AI-solutions.