Beyond Bias: How Mindful AI Can Foster Inclusion and Equality
Artificial intelligence (AI) now stands at the heart of 21st-century innovation, yielding solutions from the merely banal to the profoundly sublime. However, its power is often used to replicate the biases of human society, leading to discrimination in hiring, law enforcement, lending, and more. If we can’t eliminate those biases from AI, what if we could program it to reflect our best selves rather than our worst? Mindful AI, a new form of technology that is aware of, resistant to, and corrective of its biases, is a conscious, polished innovation that aids inclusivity and equity and has the potential to create a more just and equitable society.
The Problem of Bias in AI
AI systems are trained on data – on data that reflect human history and culture and contain human prejudice. We’ve seen this with issues such as facial recognition software failing to recognize people of color accurately or hiring algorithms that are biased against women. But the problem with bias in AI can go even further than this – because it can exacerbate inequalities, like those around race, gender, or disability.
The Conscious Approach
This is mindful AI. It involves developers and stakeholders taking the lead in developing systems that are aware of their effects and developed to be fair. It means paying attention to the uniqueness of human diversity and actively working to create AI systems that operate equitably for all. When developers and stakeholders become mindful, they can approach AI development with a contemplative lens, continually reflecting on AI's raison d’être and purpose, its effects on diverse stakeholders, and the ethical implications of its outputs.
Fostering Inclusion in Hiring
Mindful AI can level the playing field in hiring by dropping human prejudices. So-called ‘blind’ hiring systems, designed to drop human prejudices, have run up against several difficulties. For example, names and images of applicants can be revealing, as can the applicant’s description of their gender. Even hiring officers accidentally reveal biases by using common contemporary English words in their questions to job candidates. Mindful AI can take us much further in designing hiring systems that can focus strictly on the skills and qualifications needed and nothing more. For example, hiring systems could be programmed to be mindful of inclusion. Hiring officers could be prompted with questions to screen for possible exclusionary or discriminatory decisions regarding race, gender, and national origin based solely upon irrelevant considerations. Systems could be designed to weed out these results without burdening the humans doing the hiring.
Law Enforcement and Judicial Equity
AI in law enforcement presents a dual prospect of efficiency and inequity. For instance, facial recognition can lead to wrongful arrests if the training data is not carefully selected, and it can also perpetuate biases against minorities. A mindful AI approach would involve responsible training data, continuous methods to identify and eliminate bias, and, most importantly, the active participation of diverse stakeholders in the development process. This inclusive approach ensures that the voices of all communities are heard and valued, leading to a more equitable justice system.
Equality in Lending
AI is also enormously beneficial to the financial sector regarding efficiency and decision-making. However, lending algorithms can replicate a lender’s past biases against minorities or low-income groups. Socially aware AI in lending could resemble transparent algorithms to applicants. Hence, they know all the criteria used to approve or deny a loan and consider a broader array of non-traditional data points to assess creditworthiness, leading to a fairer assessment of those seeking loans.
Building Mindful AI
Those steps to building mindful AI include: 1) diversifying development teams and datasets to include the range of human experiences better; 2) creating algorithms that allow for self-auditing for bias and self-learning to correct over time, and 3) developing ethical frameworks for AI specifically to include inclusivity as a primary objective.
Challenges to Mindfulness in AI
Also, mindful AI faces barriers. Some of those hurdles are technical – algorithms are challenging to de-bias – while others are sociopolitical, such as the lack of common standards on decent treatment and how to achieve it. In addition, economic pressures can reinforce business-as-usual approaches that prioritize speed and efficiency over ethics, further stymieing moves toward mindful AI.
The Role of Regulation
Appropriate government and industry regulation can help ensure that AI is developed thoughtfully. Such regulations include requiring software developers to make their AI systems transparent, mandating audit systems when bias is likely to occur, and promoting inclusive data sets. Because AI systems are often beyond the reach of a single nation, the urgency and significance of enacting regulations on a global level must be addressed.
Success Stories
In some cases, thoughtful AI is having an impact. Some hiring algorithms have increased the racial and gender diversity of workforces, and some lending models have provided loans to those who otherwise would be denied due to biased approaches.
Conclusion
Mindful AI Holds the promise of a fairer, more inclusive society, aligning with our normative aspirations of fairness and justice. We stand at a pivotal moment. Technology can either perpetuate existing inequalities or pave the way for broader inclusion. The choice is ours. As policymakers, industry leaders, and technology professionals, you are not just observers but active participants in this journey. You have the power and responsibility to steer AI development towards the latter, positively impacting society.
ANNEX
VIEWS ON DATA DIVERSITY
Data diversity is vital for building robust, fair, and responsible AI systems that operate in the real world. While data diversity means different things to different people, the concept generally encompasses the deliberate and thoughtful use of data sources, data types, and data sets of varying sizes. Here are some other reasons why data diversity is so crucial in AI.
Reflecting on the Real World
Data shapes AI – and if the data fed into AI is not diverse, the AI’s worldview gets narrowed, which can result in biased outcomes. A diverse data set allows AI systems to function more accurately and inclusively towards more varied use cases. For example, when it comes to facial recognition systems, a diverse data set is needed to recognize black people in the same way they recognize white individuals.
Preventing Bias
The lack of data diversity accounts for bias in AI algorithms to a large degree. If a dataset used to train an AI system is biased, the system might perpetuate and worsen social differences. Ensuring data diversity helps to remove (or mitigate) these biases, leading to AI systems that treat everyone fairly.
Enhancing Innovation
Diverse data can mean more innovative solutions: with more disparate information funneled through the AI system, it could be exposed to patterns and solutions that wouldn’t be obvious from more homogeneous datasets. Take medical research, for example. A diverse genomic data set could help physicians concoct customized patient treatment strategies.
Improving Reliability and Robustness
Generally, it’s better to train AI models on more diverse datasets because they tend to be more robust. They are better at dealing with edge cases and less likely to break under unexpected inputs. That robustness is essential for AI systems that drive cars, choose medical diagnoses, etc.
Ethical and Social Responsibility
On an ethical level, by increasing data diversity, AI developers and companies make it their responsibility to ensure that their technology does not treat individuals or groups differently from the majority. This builds public trust in AI systems by showing how they were built to protect against unfairness.
Market Expansion
In turn, data diversity can create new opportunities and markets for businesses. By learning from and addressing a more diverse population, a company may be able to design new products or services that appeal to a broader audience, thus expanding its reach and relevance.
In sum, data diversity is indeed an essential technical prerequisite for AI. However, we believe it is also a moral commitment to building fair and effective AI systems that benefit all strata of society. This commitment to data diversity will only become more significant as AI systems become more embedded in our everyday lives. Consequently, data diversity must be at the forefront of everyone's mind for all those working on the future of AI.
