When considering potential biases in artificial intelligence (AI) systems, it is important to consider those that may affect individuals who identify as lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+). These biases can have serious implications for their safety, privacy, healthcare access, employment opportunities, and more.
There are several mechanisms in place to detect, mitigate, and prevent these biases from being perpetuated in AI systems. In this article, I will explore how effective these mechanisms are in practice.
One mechanism to detect AI bias against LGBT populations is through auditing. This involves reviewing an algorithm's code and data sources to see if they reflect the diversity of the population they are intended to serve.
A company developing an online dating app might hire an independent consultant to assess whether its algorithms include terms related to gender identity or sexual orientation that could exclude certain users. If so, the consultant would recommend changes to the algorithm to make it more inclusive. Another method of detection is testing with diverse groups. A study published in Nature shows how a team of researchers tested an image classification model using facial features and found it made inaccurate predictions about sexual orientation based on stereotypes like beards or earrings. The results prompted the researchers to retrain the model with a larger, more representative dataset.
Another approach to addressing AI bias is through fairness analysis. This involves measuring the impact of a specific decision-making process on different groups within a population. A study by ProPublica found that Amazon's recruiting system was less likely to select female candidates because it favored resumes that included the word "executive" over those without it. By analyzing the decision-making process, the study identified a potential bias against women. As a result, Amazon updated its system to remove the term "executive." Fairness analysis can also be used to identify biases in other areas such as criminal justice, healthcare, and housing.
To mitigate AI bias, one strategy is to use counterfactual explanations. These explain why a particular decision was made and suggest alternatives that may have yielded a different outcome.
If a mortgage lender uses AI to determine creditworthiness, they should provide explanations for decisions that are not favorable to LGBT applicants.
A counterfactual explanation might state: "If your income had been $500 higher, you would have qualified for this loan." Another strategy is to implement constraints on the data inputs and algorithmic parameters.
A bank could limit its use of gender-specific language when determining eligibility for loans.
There are several steps companies can take to prevent AI bias from occurring in the first place. One approach is to collect demographic information about their users or customers. Companies like Google and Facebook require their developers to consider diversity when designing products, which helps ensure that their systems do not unintentionally exclude certain groups. Another strategy is to use diverse datasets during model training to reduce the risk of stereotyping.
Companies can build ethical guidelines into their AI development processes to help ensure fairness and transparency throughout the process.
While these mechanisms exist to detect, mitigate, and prevent AI bias against LGBT populations, their effectiveness depends on several factors. Some studies suggest that auditing alone cannot fully address all forms of discrimination because biases can be subtle and difficult to identify. Other research suggests that auditing can be helpful but requires expertise in both machine learning and social justice to be effective. Counterfactual explanations may not always be accurate or comprehensive, and constraints can introduce new risks if they are too restrictive. Building inclusivity into the design process may also require resources and personnel who understand how different identities intersect with technology.
It will take sustained effort by industry leaders, policymakers, and advocacy groups to ensure that AI systems serve everyone equally and fairly.
What mechanisms exist to detect, mitigate, and prevent AI bias against LGBT populations, and how effective are they in practice?
Despite increased acceptance of lesbian, gay, bisexual, transgender (LGBT) communities, biases still exist in artificial intelligence (AI). These biases manifest as prejudice based on gender identity, sexual orientation, race, age, ethnicity, religion, appearance, etc. , which can be embedded in the training data used to train algorithms. Different researchers have proposed various mechanisms for addressing AI bias against LGBT populations.