Introduction
Artificial intelligence has evolved significantly since the 1950s, with generative AI emerging as a new era. Businesses are discovering new capabilities with tools like OpenAI’s DALL-E 2 and ChatGPT. AI adoption is accelerating, with Forrester predicting software spending will reach $64 billion in 2025. However, generative AI tools exacerbate AI bias, where models produce predictions based on human biases in data sets.
Even while AI bias is not a recent happenings, the development of generative AI technology has made it more apparent.
AI prejudice compromises business reputations
AI bias may significantly damage a company’s reputation by delivering biased forecasts, resulting in poor decision-making and raising concerns about copyright violations and plagiarism. If trained on incorrect or fraudulent content, generative AI models can provide incorrect outputs. For example, face recognition AI frequently incorrectly detects individuals of colour, and predictive AI models used to accept or reject loans did not offer suitable recommendations for minority loans. There are more examples of AI prejudice and discrimination. Companies must be proactive in managing the quality of training data to ensure that AI models are trained on correct and trustworthy data. This proactive approach is totally up to the people.
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Human engagement is required for high-quality data
According to a DataRobot survey, while more than half of organisations are worried about AI bias, over three-quarters have not taken action to remove bias in data sets. With the emergence of ChatGPT and generative AI, data analysts must be in charge of teaching data custodians to properly curate data and adopt ethical practices. There are three areas to test for AI bias: data bias, algorithm bias, and human bias. Although tools such as LIME and T2IAT can assist in detecting bias, people can still contribute to bias. Data science teams must always be watchful and check for bias. It is critical to have data accessible to a broad group of data scientists to discover biases. AI models will someday replace people processing large amounts of data, but data analysts must lead the charge.
Putting up barriers against AI bias
As AI use increases, it is critical to set rules and practises for developers, data analysts, and anyone involved in AI production to avoid possible harm to businesses and customers. The red team vs. blue team exercise, for example, reveals and corrects prejudice before launching AI-enabled services. To avoid bias in data and algorithms, this process should be continuous. To be more responsible in data and algorithm curation, organizations should evaluate data before and after deployment, and data analysts should become experts in their subject.
NIST encourages data analysts and social scientists to work together to advance AI models and algorithms. Companies may limit the danger of bias and brand reputation damage by concentrating on data quality. Because of the quick speed of AI growth, it is critical to eliminate AI bias before integrating machine learning and AI processes. Businesses may have a beneficial influence on AI adoption and avoid the dangers of bias by concentrating on data quality.
