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From generating images of the Pope just for fun to algorithms that help screen job applications and ease the burden on hiring managers, AI programs have taken the public consciousness and the business world by storm. However, it is crucial not to overlook the potentially deep-seated ethical issues associated with it.
These innovative technological tools generate content by drawing on existing data and other material, but if those sources are even partially the result of racial or gender bias, for example, AI will likely replicate this. For those of us who want to live in a world where diversity, equity, and inclusion (DEI) are at the forefront of emerging technology, we should all care about how AI systems create content and the impact their results have on society.
So whether you’re a developer, an AI start-up entrepreneur, or just a concerned citizen like me, consider these principles that can be integrated into AI apps and programs to ensure they create more ethical and equitable outcomes .
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1. Create a user-centric design
User-centered design ensures that the program you are developing includes its users. This may include features such as voice interactions and screen reading capabilities that help people with low vision. Speech recognition models, meanwhile, can be more inclusive of different types of voices (such as female ones or applying accents from around the world).
Simply put, developers should pay close attention to who their AI systems are aimed at: commit to thinking outside the group of engineers who created them. This is especially important if they and/or the company’s entrepreneurs hope to scale the products globally.
2. Build a diverse team of reviewers and decision makers
The development team of an AI app or program is critical, not only in its creation, but also from a review and decision-making perspective. A 2023 report published by New York University’s AI Now Institute described the lack of diversity at multiple levels of AI development. It included notable statistics that at least 80% of AI professors are men, and that fewer than 20% of AI researchers at the world’s top tech companies are women. Without the right checks, balances, and representation in development, we run the serious risk of feeding AI programs with dated and/or biased data that perpetuates unfair myths about certain groups.
3. Audit data sets and create accountability structures
It’s not necessarily anyone’s direct fault that there is older data perpetuating biases, but And It’s someone’s fault if the data isn’t regularly checked. To ensure that AI produces the highest quality output with DEI in mind, developers must carefully evaluate and analyze the information they use. They should ask themselves: How old is he? Where does it come from? What does it contain? Is it ethical or correct in the current moment? Perhaps most importantly, datasets should ensure that AI perpetuates a positive future for DEI and not a negative one from the past.
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4. Collect and curate diverse data
If, after reviewing the information used by an AI program, you notice that there are inconsistencies, biases and/or biases, work to gather better material. This is easier said than done: data collection takes months, even years, but it is absolutely worth it.
To help fuel this process, if you’re an entrepreneur running an AI startup and have the resources to do research and development, create projects where team members create new data that represents different voices, faces, and attributes. This will result in more suitable source material for apps and programs that we can all benefit from, essentially creating a brighter future that shows various individuals as multidimensional rather than one-sided or otherwise simplistic.
Related: AI can be racist, sexist, and creepy. Here are 5 ways to combat this problem in your business
5. Participate in AI ethics training on bias and inclusivity
As a DEI consultant and proud creator of the LinkedIn course, Navigating AI Through an Intersectional DEI Lens, I learned the power of centering DEI in AI development and the positive ripple effects it has.
If you or your team are having trouble putting together a to-do list for developers, reviewers, and others, I recommend organizing corresponding ethics training, including an online course that can help you troubleshoot problems in real time.
Sometimes all you need is a trainer to help you walk through the process and solve each problem one by one to create a lasting result that produces more inclusive, diverse, and ethical data and AI programs.
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Developers, entrepreneurs, and everyone who cares about reducing bias in AI should use our collective energy to train on how to build diverse teams of reviewers who can review and verify data, and focus on projects that make programs more effective. inclusive and accessible. The result will be a landscape that represents a broader range of users, as well as better content.