Artificial Intelligence (AI) has been one of the most promising technologies of the 21st century. Across the financial services industry, AI has paved the way to improved progress by providing decision-makers with access to advanced information. AI has also become the cornerstone of automation by allowing managers to focus on critical decisions. However, with the development and incorporation of technology in a broader capacity, instances of inherent bias have started to emerge as a part of the equation. From factors like race to religion, AI has also felt the impacts of the developmental process. It is essential to explore a shift to unbiased intelligence to ensure that the emerging decisions have an equally positive impact on people around the world.
Existing bias in AI has been a key area of concern for investment managers because of the impact AI decisions have on financial organizations. Exploring the nature of these biases is essential to devise the right solutions.
To have clarity on the problematic areas in artificial intelligence, it is essential to explore the key challenges that are currently hindering a shift towards progress and broader equality.
Here are the biggest challenges.
Institutional Bias in Artificial Development
Even though the inherent conception is that machines are free from human partiality, the reality in the AI landscape is different. Artificial Intelligence is designed to learn from datasets and observe human behavior to develop its understanding of the world around us.
Personalized Prices Bias
Multiple instances of personalized prices bias have emerged on social applications where minorities have been charged significantly higher prices with the same financial backgrounds. These biases have impacted customer experiences and created a negative landscape for the companies on social media. In today’s world of proactive ESG deployment, it is essential for organizations to put an effort towards fairness to ensure that customers receive fair treatment.
Voice Assistant Improvement
Voice assistance is rapidly becoming an integral part of customer experiences. These assistants have showcased multiple instances of social bias stemming from the development dataset. Companies need to develop products that integrate equitable voice support for customers from different segments. A conscious internal effort is required by companies to ensure effective deployment across key verticals.
Based on the current understanding of AI, two different types of biases are currently impacting decision-making.
Data bias is also referred to as algorithmic bias, where algorithms develop their primary basis of decision-making from the originating biased data. This bias stems from the AI developing its framework based on a systematically impacted dataset. Social influences formulate the majority of existing data biases. The end result of data biases is now increasingly becoming public as consumers get to interact with open-ended AI solutions.
Here’s a key example of how the phenomenon is practically emerging in public.
- The PortraitAI platform allows users to take selfies and see interpretations in artistic end-results. A key bias in the system emerged in the form of painting results being impacted by skin color. The inherent result of the bias stemmed from the majority of well-known paintings featuring primarily Caucasian individuals. This, as a result, populated the application with a database of Caucasian individuals, and the bias significantly impacted the end results.
- Chatbots developed by leading companies like Twitter and Microsoft had repeatedly showcased racist behavior when they were trained on public interaction data.
- Twitter’s Image Selection Tool had exhibited racially biased behavior by cropping out human faces in the center when images of African American individuals were uploaded. The algorithm showcased similar biases even on the images of the previous American president Barack Obama.
Addressing such instances of existing data bias requires a comprehensive overhaul of the initial datasets used to train AI models. The datasets must be evaluated for their existing bias and reviewed under a comprehensive mechanism to prevent data bias from impacting AI performance.
Social AI bias is when AI behavior reflects social intolerance and institutional discrimination. Even though the initial outlook of the data presents an unbiased outlook, the result of the algorithm supplements existing social biases.
Examples of these biases include the following examples.
- The Google Maps Pronunciation results reveal a significant disparity between the pronunciation of ethnic words. This bias results from a lack of diverse datasets in the application’s dataset.
- Law Enforcement Applications have showcased an inherent bias against women and individuals with foreign names stemming from the inherent bias within AI systems.
The correction of social bias is significantly harder than data bias because the identification is significantly challenging to trace and correct. Comprehensive mechanisms need to be devised by researchers to ensure effective solution deployment.
Considering the nature of the dataset required for AI training and development, companies need to consider privacy and data rights as critical elements of unbiased AI development. Maintaining data privacy can allow users to feel confident about contributing their data for further refinement of AI products.
Aside from existing privacy concerns, DIF recommends a shift to consumer-centric data storage and handling policies. Companies need to actively communicate with consumers regarding data storage and handling to prevent instances of data misuse. Increased communication would also be a positive factor in enhancing customer trust in company directives.
Due to the critical nature of AI solutions and the emergence of an increasingly digital future, DIF is focused on the development of cutting-edge solutions to support unbiased AI development. Our technology labs are integrating global talent through grants and support initiatives to mutually explore the optimal way forward to remove existing biases in the AI landscape. We’re also actively investing in companies that are complying with privacy and data management requirements across all layers of development and operations.