Gender, Race & Diversity
The best way to ensure models are fair, equitable, and unbiased is to ensure the models are inclusive of as many people, races, nationalities, ethnicities, genders, and so on. AI models are our way of modeling virtual worlds that are meant to represent “us” in the real world. But what happens when “our type” of data is not included in the model? “Our type” of data refers to information that accurately represents and reflects the population being analyzed, scored, or evaluated. Does the data do a fair job at ensuring certain minorities, genders, or sexual orientations are treated equally with all other groups?
In 2021, there is still a long way to go with data sets that are inclusive in the way we would want them to be. Many data sets that are used in the judicial system do not treat all races equally. Outside of the judicial system, many datasets treat men and women differently from one another, which has negatively impacted women in many cases. Yet, there are other data sets that don’t even include some groups of people, leaving their treatment by the system as a complete toss of the coin.
AI systems do best when they interact with data they are familiar with and have been trained on. So it would make sense that AI systems do poorly when they interact with novel data, often resulting in significant errors that a human would never have made. This leads to the logical inference that there are certain populations who have not been included into the training data or the systems have been trained with inaccurate data. Both of which cause significant issues.
Given these issues, it would make sense for governments, companies, and organizations to actively develop representative datasets that include as many groups of people as possible. It would make sense that new groups would emerge every year, so this would be an ongoing process of updating, refining, and improving the datasets and systems in a continuous fashion. Is this more work? Absolutely. But would it be worth it? We think so.
The benefits would be massive for the economy and for society since more people would be accurately represented and given the opportunities they deserve. Companies would find better applicants, candidates for promotion, and so on. Individuals who find themselves in the judicial system would have a true impartial hearing, with race, gender, and other socioeconomic factors impacting them. More people from diverse backgrounds would be able to move up the economic ladder with greater access to credit, education, and opportunities.
But more importantly, by including everyone possible and actively working to include more people into datasets, we would be placing ourselves on the path to creating a truly equitable, just, and fair world. We understand that creating these types of systems would be challenging, time-consuming, and frustrating, but if we are in the process of creating the new tech-driven world that we and future generations will live in, wouldn’t we want it to be an aspirational one?
What is AI Ethics? Why is it important? Why will it impact all of us?
Implementing Ethical AI isn’t only the right thing to do, it’s the profitable thing to do.
What is the regulatory case for Ethical AI?
Understand the importance of Transparency & Explainability in AI systems.
What happens when intelligent models don’t accurately include or reflect all genders, races, groups? Why is it so important that models are designed with diversity in mind?
Where do we see AI Ethics going forward? How will it apply to companies and individuals?